@Preamble{
"\input bibnames.sty"
# "\hyphenation{Christ-o-dou-lou Dan-iel-la Dough-er-ty Giu-sep-pe
Hab-tom Le-o-nar-do Ma-ran-go-ni Mee-nak-shi
Pav-lo-vic Pro-ko-pen-ko Rez-ar-ta Ri-bei-ro
Sid-da-ha-na-val-li Tei-xei-ra Ven-kat-es-wa-ran}"
# "\ifx \undefined \bioname \def \bioname#1{{{\em #1\/}}} \fi"
# "\ifx \undefined \poly \def \poly {{\rm poly}}\fi"
# "\ifx \undefined \TM \def \TM {${}^{\sc TM}$} \fi"
}
@String{ack-nhfb = "Nelson H. F. Beebe,
University of Utah,
Department of Mathematics, 110 LCB,
155 S 1400 E RM 233,
Salt Lake City, UT 84112-0090, USA,
Tel: +1 801 581 5254,
FAX: +1 801 581 4148,
e-mail: \path|beebe@math.utah.edu|,
\path|beebe@acm.org|,
\path|beebe@computer.org| (Internet),
URL: \path|https://www.math.utah.edu/~beebe/|"}
@String{j-TCBB = "IEEE\slash ACM Transactions on Computational
Biology and Bioinformatics"}
@Article{Williams:2004:WM,
author = "Michael R. Williams",
title = "Welcome Message",
journal = j-TCBB,
volume = "1",
number = "1",
pages = "1--1",
month = jan,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Nov 22 06:42:56 MST 2004",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gusfield:2004:IIA,
author = "Dan Gusfield",
title = "Introduction to the {IEEE\slash ACM Transactions on
Computational Biology and Bioinformatics}",
journal = j-TCBB,
volume = "1",
number = "1",
pages = "2--3",
month = jan,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Nov 22 06:42:56 MST 2004",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Williams:2004:INA,
author = "Michael R. Williams",
title = "Introduction of New {Associate Editors}",
journal = j-TCBB,
volume = "1",
number = "1",
pages = "4--12",
month = jan,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Nov 22 06:42:56 MST 2004",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Moret:2004:PNM,
author = "Bernard M. E. Moret and Luay Nakhleh and Tandy Warnow
and C. Randal Linder and Anna Tholse and Anneke
Padolina and Jerry Sun and Ruth Timme",
title = "Phylogenetic Networks: Modeling, Reconstructibility,
and Accuracy",
journal = j-TCBB,
volume = "1",
number = "1",
pages = "13--23",
month = jan,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Nov 22 06:42:56 MST 2004",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Madeira:2004:BAB,
author = "Sara C. Madeira and Arlindo L. Oliveira",
title = "Biclustering Algorithms for Biological Data Analysis:
a Survey",
journal = j-TCBB,
volume = "1",
number = "1",
pages = "24--45",
month = jan,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Nov 22 06:42:56 MST 2004",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Preparata:2004:SHR,
author = "Franco P. Preparata",
title = "Sequencing-by-Hybridization Revisited: The
Analog-Spectrum Proposal",
journal = j-TCBB,
volume = "1",
number = "1",
pages = "46--52",
month = jan,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Nov 22 06:42:56 MST 2004",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hochsmann:2004:PMR,
author = "Matthias H{\"o}chsmann and Bj{\"o}rn Voss and Robert
Giegerich",
title = "Pure Multiple {RNA} Secondary Structure Alignments:
a Progressive Profile Approach",
journal = j-TCBB,
volume = "1",
number = "1",
pages = "53--62",
month = jan,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Nov 22 06:42:56 MST 2004",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2004:INA,
author = "Anonymous",
title = "Introduction of New {Associate Editor}",
journal = j-TCBB,
volume = "1",
number = "2",
pages = "65--65",
month = apr,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Nov 22 06:42:56 MST 2004",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Witwer:2004:PCR,
author = "Christina Witwer and Ivo L. Hofacker and Peter F.
Stadler",
title = "Prediction of Consensus {RNA} Secondary Structures
Including Pseudoknots",
journal = j-TCBB,
volume = "1",
number = "2",
pages = "66--77",
month = apr,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Nov 22 06:42:56 MST 2004",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bafna:2004:NRE,
author = "Vineet Bafna and Vikas Bansal",
title = "The Number of Recombination Events in a Sample
History: Conflict Graph and Lower Bounds",
journal = j-TCBB,
volume = "1",
number = "2",
pages = "78--90",
month = apr,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Nov 22 06:42:56 MST 2004",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Raphael:2004:UPM,
author = "Benjamin Raphael and Lung-Tien Liu and George
Varghese",
title = "A Uniform Projection Method for Motif Discovery in
{DNA} Sequences",
journal = j-TCBB,
volume = "1",
number = "2",
pages = "91--94",
month = apr,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Nov 22 06:42:56 MST 2004",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gusfield:2004:INA,
author = "Dan Gusfield",
title = "Introduction of New {Associate Editors}",
journal = j-TCBB,
volume = "1",
number = "3",
pages = "97--97",
month = jul,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jan 24 14:15:55 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Scheid:2004:SDS,
author = "Stefanie Scheid and Rainer Spang",
title = "A Stochastic Downhill Search Algorithm for Estimating
the Local False Discovery Rate",
journal = j-TCBB,
volume = "1",
number = "3",
pages = "98--108",
month = jul,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jan 24 14:15:55 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dress:2004:CSG,
author = "Andreas W. M. Dress and Daniel H. Huson",
title = "Constructing Splits Graphs",
journal = j-TCBB,
volume = "1",
number = "3",
pages = "109--115",
month = jul,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jan 24 14:15:55 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cameron:2004:IGA,
author = "Michael Cameron and Hugh E. Williams and Adam
Cannane",
title = "Improved Gapped Alignment in {BLAST}",
journal = j-TCBB,
volume = "1",
number = "3",
pages = "116--129",
month = jul,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jan 24 14:15:55 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Evans:2004:UDT,
author = "Steven N. Evans and Tandy Warnow",
title = "Unidentifiable Divergence Times in Rates-across-Sites
Models",
journal = j-TCBB,
volume = "1",
number = "3",
pages = "130--134",
month = jul,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jan 24 14:15:55 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kim:2004:GEW,
author = "Junhyong Kim and Inge Jonassen",
title = "Guest Editorial: {WABI} Special Section Part 1",
journal = j-TCBB,
volume = "1",
number = "4",
pages = "137--138",
month = oct,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jan 24 14:15:55 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Csuros:2004:MSS,
author = "Miklos Csuros",
title = "Maximum-Scoring Segment Sets",
journal = j-TCBB,
volume = "1",
number = "4",
pages = "139--150",
month = oct,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jan 24 14:15:55 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huson:2004:PSN,
author = "Daniel H. Huson and Tobias Dezulian and Tobias Klopper
and Mike A. Steel",
title = "Phylogenetic Super-Networks from Partial Trees",
journal = j-TCBB,
volume = "1",
number = "4",
pages = "151--158",
month = oct,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jan 24 14:15:55 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bannai:2004:ADO,
author = "Hideo Bannai and Heikki Hyyro and Ayumi Shinohara and
Masayuki Takeda and Kenta Nakai and Satoru Miyano",
title = "An {$ O(N^2) $} Algorithm for Discovering Optimal
{Boolean} Pattern Pairs",
journal = j-TCBB,
volume = "1",
number = "4",
pages = "159--170",
month = oct,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jan 24 14:15:55 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gramm:2004:PTA,
author = "Jens Gramm",
title = "A Polynomial-Time Algorithm for the Matching of
Crossing Contact-Map Patterns",
journal = j-TCBB,
volume = "1",
number = "4",
pages = "171--180",
month = oct,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jan 24 14:15:55 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ye:2004:UUD,
author = "Jieping Ye and Tao Li and Tao Xiong and Ravi
Janardan",
title = "Using Uncorrelated Discriminant Analysis for Tissue
Classification with Gene Expression Data",
journal = j-TCBB,
volume = "1",
number = "4",
pages = "181--190",
month = oct,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jan 24 14:15:55 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2004:AI,
author = "Anonymous",
title = "Annual Index",
journal = j-TCBB,
volume = "1",
number = "4",
pages = "191--192",
month = oct,
year = "2004",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jan 24 14:15:55 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kim:2005:GEW,
author = "Junhyong Kim and Inge Jonassen",
title = "Guest Editorial: {WABI} Special Section. {Part II}",
journal = j-TCBB,
volume = "2",
number = "1",
pages = "1--2",
month = jan,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Apr 12 07:11:54 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Allali:2005:NDH,
author = "Julien Allali and Marie-France Sagot",
title = "A New Distance for High Level {RNA} Secondary
Structure Comparison",
journal = j-TCBB,
volume = "2",
number = "1",
pages = "3--14",
month = jan,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Apr 12 07:11:54 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bertrand:2005:TRL,
author = "Denis Bertrand and Olivier Gascuel",
title = "Topological Rearrangements and Local Search Method for
Tandem Duplication Trees",
journal = j-TCBB,
volume = "2",
number = "1",
pages = "15--28",
month = jan,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Apr 12 07:11:54 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Brown:2005:OMS,
author = "Daniel G. Brown",
title = "Optimizing Multiple Seeds for Protein Homology
Search",
journal = j-TCBB,
volume = "2",
number = "1",
pages = "29--38",
month = jan,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Apr 12 07:11:54 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gusfield:2005:EST,
author = "Dan Gusfield",
title = "Editorial-State of the Transaction",
journal = j-TCBB,
volume = "2",
number = "1",
pages = "39--39",
month = jan,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Apr 12 07:11:54 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pisanti:2005:BMG,
author = "Nadia Pisanti and Maxime Crochemore and Roberto Grossi
and Marie-France Sagot",
title = "Bases of Motifs for Generating Repeated Patterns with
Wild Cards",
journal = j-TCBB,
volume = "2",
number = "1",
pages = "40--50",
month = jan,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Apr 12 07:11:54 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kucherov:2005:MLF,
author = "Gregory Kucherov and Laurent Noe and Mikhail
Roytberg",
title = "Multiseed Lossless Filtration",
journal = j-TCBB,
volume = "2",
number = "1",
pages = "51--61",
month = jan,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Apr 12 07:11:54 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2005:TMB,
author = "Ying Liu and Shamkant B. Navathe and Jorge Civera and
Venu Dasigi and Ashwin Ram and Brian J. Ciliax and Ray
Dingledine",
title = "Text Mining Biomedical Literature for Discovering
Gene-to-Gene Relationships: a Comparative Study of
Algorithms",
journal = j-TCBB,
volume = "2",
number = "1",
pages = "62--76",
month = jan,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Apr 12 07:11:54 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Staff:2005:RL,
author = "{IEEE and ACM Transactions on Computational Biology
and Bioinformatics staff}",
title = "2004 Reviewers List",
journal = j-TCBB,
volume = "2",
number = "1",
pages = "77--77",
month = jan,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Apr 12 07:11:54 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ling:2005:GEIa,
author = "Charles X. Ling and William Stafford Noble and Qiang
Yang",
title = "{Guest Editors}' Introduction to the {Special Issue:
Machine Learning for Bioinformatics---Part 1}",
journal = j-TCBB,
volume = "2",
number = "2",
pages = "81--82",
month = apr,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 22 17:33:35 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Au:2005:ACG,
author = "Wai-Ho Au and Keith C. C. Chan and Andrew K. C. Wong
and Yang Wang",
title = "Attribute Clustering for Grouping, Selection, and
Classification of Gene Expression Data",
journal = j-TCBB,
volume = "2",
number = "2",
pages = "83--101",
month = apr,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 22 17:33:35 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Biyani:2005:JCP,
author = "Pravesh Biyani and Xiaolin Wu and Abhijit Sinha",
title = "Joint Classification and Pairing of Human
Chromosomes",
journal = j-TCBB,
volume = "2",
number = "2",
pages = "102--109",
month = apr,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 22 17:33:35 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Furlanello:2005:SLM,
author = "Cesare Furlanello and Maria Serafini and Stefano
Merler and Giuseppe Jurman",
title = "Semisupervised Learning for Molecular Profiling",
journal = j-TCBB,
volume = "2",
number = "2",
pages = "110--118",
month = apr,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 22 17:33:35 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mamitsuka:2005:ELK,
author = "Hiroshi Mamitsuka",
title = "Essential Latent Knowledge for Protein-Protein
Interactions: Analysis by an Unsupervised Learning
Approach",
journal = j-TCBB,
volume = "2",
number = "2",
pages = "119--130",
month = apr,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 22 17:33:35 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rajapakse:2005:MED,
author = "Jagath C. Rajapakse and Loi Sy Ho",
title = "{Markov} Encoding for Detecting Signals in Genomic
Sequences",
journal = j-TCBB,
volume = "2",
number = "2",
pages = "131--142",
month = apr,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 22 17:33:35 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rogers:2005:LPD,
author = "Simon Rogers and Mark Girolami and Colin Campbell and
Rainer Breitling",
title = "The Latent Process Decomposition of {cDNA} Microarray
Data Sets",
journal = j-TCBB,
volume = "2",
number = "2",
pages = "143--156",
month = apr,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 22 17:33:35 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2005:FRP,
author = "Jinbo Xu",
title = "Fold Recognition by Predicted Alignment Accuracy",
journal = j-TCBB,
volume = "2",
number = "2",
pages = "157--165",
month = apr,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 22 17:33:35 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Shen:2005:DRB,
author = "Li Shen and Eng Chong Tan",
title = "Dimension Reduction-Based Penalized Logistic
Regression for Cancer Classification Using Microarray
Data",
journal = j-TCBB,
volume = "2",
number = "2",
pages = "166--175",
month = apr,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 22 17:33:35 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ling:2005:GEIb,
author = "C. X. Ling and W. S. Noble and Q. Yang",
title = "{Guest Editor}'s Introduction to the {Special Issue:
Machine Learning for Bioinformatics---Part 2}",
journal = j-TCBB,
volume = "2",
number = "3",
pages = "177--178",
month = jul,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Sep 20 06:11:25 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Schliep:2005:AGE,
author = "Alexander Schliep and Ivan G. Costa and Christine
Steinhoff and Alexander Schonhuth",
title = "Analyzing Gene Expression Time-Courses",
journal = j-TCBB,
volume = "2",
number = "3",
pages = "179--193",
month = jul,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Sep 20 06:11:25 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kundaje:2005:CST,
author = "Anshul Kundaje and Manuel Middendorf and Feng Gao and
Chris Wiggins and Christina Leslie",
title = "Combining Sequence and Time Series Expression Data to
Learn Transcriptional Modules",
journal = j-TCBB,
volume = "2",
number = "3",
pages = "194--202",
month = jul,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Sep 20 06:11:25 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kaski:2005:ACE,
author = "Samuel Kaski and Janne Nikkila and Janne Sinkkonen and
Leo Lahti and Juha E. A. Knuuttila and Christophe
Roos",
title = "Associative Clustering for Exploring Dependencies
between Functional Genomics Data Sets",
journal = j-TCBB,
volume = "2",
number = "3",
pages = "203--216",
month = jul,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Sep 20 06:11:25 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2005:PMF,
author = "Jingfen Zhang and Wen Gao and Jinjin Cai and Simin He
and Rong Zeng and Runsheng Chen",
title = "Predicting Molecular Formulas of Fragment Ions with
Isotope Patterns in Tandem Mass Spectra",
journal = j-TCBB,
volume = "2",
number = "3",
pages = "217--230",
month = jul,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Sep 20 06:11:25 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Keedwell:2005:DGN,
author = "Edward Keedwell and Ajit Narayanan",
title = "Discovering Gene Networks with a Neural-Genetic
Hybrid",
journal = j-TCBB,
volume = "2",
number = "3",
pages = "231--242",
month = jul,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Sep 20 06:11:25 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hawkins:2005:ARN,
author = "John Hawkins and Mikael Boden",
title = "The Applicability of Recurrent Neural Networks for
Biological Sequence Analysis",
journal = j-TCBB,
volume = "2",
number = "3",
pages = "243--253",
month = jul,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Sep 20 06:11:25 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gustafsson:2005:CAL,
author = "Mika Gustafsson and Michael Hornquist and Anna
Lombardi",
title = "Constructing and Analyzing a Large-Scale Gene-to-Gene
Regulatory Network-Lasso-Constrained Inference and
Biological Validation",
journal = j-TCBB,
volume = "2",
number = "3",
pages = "254--261",
month = jul,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Sep 20 06:11:25 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Demir:2005:LTP,
author = "Cigdem Demir and S. Humayun Gultekin and Bulent
Yener",
title = "Learning the Topological Properties of Brain Tumors",
journal = j-TCBB,
volume = "2",
number = "3",
pages = "262--270",
month = jul,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Sep 20 06:11:25 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2005:CPS,
author = "Anonymous",
title = "Call for Papers for {Special Issue on Computational
Intelligence Approaches in Computational Biology and
Bioinformatics}",
journal = j-TCBB,
volume = "2",
number = "3",
pages = "271--271",
month = jul,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Sep 20 06:11:25 MDT 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cickovski:2005:FTD,
author = "Trevor M. Cickovski and Chengbang Huang and Rajiv
Chaturvedi and Tilmann Glimm and H. George E. Hentschel
and Mark S. Alber and James A. Glazier and Stuart A.
Newman and Jesus A. Izaguirre",
title = "A Framework for Three-Dimensional Simulation of
Morphogenesis",
journal = j-TCBB,
volume = "2",
number = "4",
pages = "273--288",
month = oct,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 18 05:22:15 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Boscolo:2005:GFN,
author = "Riccardo Boscolo and Chiara Sabatti and James C. Liao
and Vwani P. Roychowdhury",
title = "A Generalized Framework for Network Component
Analysis",
journal = j-TCBB,
volume = "2",
number = "4",
pages = "289--301",
month = oct,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 18 05:22:15 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2005:AOG,
author = "Xin Chen and Jie Zheng and Zheng Fu and Peng Nan and
Yang Zhong and Stefano Lonardi and Tao Jiang",
title = "Assignment of Orthologous Genes via Genome
Rearrangement",
journal = j-TCBB,
volume = "2",
number = "4",
pages = "302--315",
month = oct,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 18 05:22:15 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Turner:2005:BMS,
author = "Heather L. Turner and Trevor C. Bailey and Wojtek J.
Krzanowski and Cheryl A. Hemingway",
title = "Biclustering Models for Structured Microarray Data",
journal = j-TCBB,
volume = "2",
number = "4",
pages = "316--329",
month = oct,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 18 05:22:15 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sevilla:2005:CBG,
author = "Jose L. Sevilla and Victor Segura and Adam Podhorski
and Elizabeth Guruceaga and Jose M. Mato and Luis A.
Martinez-Cruz and Fernando J. Corrales and Angel
Rubio",
title = "Correlation between Gene Expression and {GO} Semantic
Similarity",
journal = j-TCBB,
volume = "2",
number = "4",
pages = "330--338",
month = oct,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 18 05:22:15 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yoon:2005:DCB,
author = "Sungroh Yoon and Christine Nardini and Luca Benini and
Giovanni De Micheli",
title = "Discovering Coherent Biclusters from Gene Expression
Data Using Zero-Suppressed Binary Decision Diagrams",
journal = j-TCBB,
volume = "2",
number = "4",
pages = "339--354",
month = oct,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 18 05:22:15 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tseng:2005:EMG,
author = "Vincent S. Tseng and Ching-Pin Kao",
title = "Efficiently Mining Gene Expression Data via a Novel
Parameterless Clustering Method",
journal = j-TCBB,
volume = "2",
number = "4",
pages = "355--365",
month = oct,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 18 05:22:15 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2005:SGN,
author = "Shaojie Zhang and Brian Haas and Eleazar Eskin and
Vineet Bafna",
title = "Searching Genomes for Noncoding {RNA} Using {FastR}",
journal = j-TCBB,
volume = "2",
number = "4",
pages = "366--379",
month = oct,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 18 05:22:15 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2005:AI,
author = "Anonymous",
title = "2005 Annual Index",
journal = j-TCBB,
volume = "2",
number = "4",
pages = "380--384",
month = oct,
year = "2005",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 18 05:22:15 MST 2005",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gusfield:2006:SJ,
author = "Dan Gusfield",
title = "State of the Journal",
journal = j-TCBB,
volume = "3",
number = "1",
pages = "1--1",
month = jan,
year = "2006",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2006.12",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Feb 16 11:06:15 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Berger:2006:JAG,
author = "John A. Berger and Sampsa Hautaniemi and Sanjit K.
Mitra and Jaakko Astola",
title = "Jointly Analyzing Gene Expression and Copy Number Data
in Breast Cancer Using Data Reduction Models",
journal = j-TCBB,
volume = "3",
number = "1",
pages = "2--16",
month = jan,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Feb 16 11:06:15 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sebastian:2006:STA,
author = "Rafael Sebastian and Maria-Elena Diaz and Guillermo
Ayala and Kresimir Letinic and Jose Moncho-Bogani and
Derek Toomre",
title = "Spatio-Temporal Analysis of Constitutive Exocytosis in
Epithelial Cells",
journal = j-TCBB,
volume = "3",
number = "1",
pages = "17--32",
month = jan,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Feb 16 11:06:15 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hershkovitz:2006:SAR,
author = "Eli Hershkovitz and Guillermo Sapiro and Allen
Tannenbaum and Loren Dean Williams",
title = "Statistical Analysis of {RNA} Backbone",
journal = j-TCBB,
volume = "3",
number = "1",
pages = "33--46",
month = jan,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Feb 16 11:06:15 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dawy:2006:GMM,
author = "Zaher Dawy and Bernhard Goebel and Joachim Hagenauer
and Christophe Andreoli and Thomas Meitinger and Jakob
C. Mueller",
title = "Gene Mapping and Marker Clustering Using {Shannon}'s
Mutual Information",
journal = j-TCBB,
volume = "3",
number = "1",
pages = "47--56",
month = jan,
year = "2006",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2006.9",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jun 7 15:19:59 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/bibnet/authors/s/shannon-claude-elwood.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Goutsias:2006:HMM,
author = "John Goutsias",
title = "A Hidden {Markov} Model for Transcriptional Regulation
in Single Cells",
journal = j-TCBB,
volume = "3",
number = "1",
pages = "57--71",
month = jan,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Feb 16 11:06:15 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rueda:2006:HCA,
author = "Luis Rueda and Vidya Vidyadharan",
title = "A Hill-Climbing Approach for Automatic Gridding of
{cDNA} Microarray Images",
journal = j-TCBB,
volume = "3",
number = "1",
pages = "72--83",
month = jan,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Feb 16 11:06:15 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Semple:2006:UNC,
author = "Charles Semple and Mike Steel",
title = "Unicyclic Networks: Compatibility and Enumeration",
journal = j-TCBB,
volume = "3",
number = "1",
pages = "84--91",
month = jan,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Feb 16 11:06:15 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Roch:2006:SPP,
author = "Sebastien Roch",
title = "A Short Proof that Phylogenetic Tree Reconstruction by
Maximum Likelihood Is Hard",
journal = j-TCBB,
volume = "3",
number = "1",
pages = "92--94",
month = jan,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Feb 16 11:06:15 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2006:RL,
author = "Anonymous",
title = "2005 Reviewers List",
journal = j-TCBB,
volume = "3",
number = "1",
pages = "95--96",
month = jan,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Feb 16 11:06:15 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gusfield:2006:INA,
author = "Dan Gusfield",
title = "Introduction of New {Associate Editors}",
journal = j-TCBB,
volume = "3",
number = "2",
pages = "97--97",
month = apr,
year = "2006",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2006.25",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 7 06:38:18 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chu:2006:BSM,
author = "Wei Chu and Zoubin Ghahramani and Alexei
Podtelezhnikov and David L. Wild",
title = "{Bayesian} Segmental Models with Multiple Sequence
Alignment Profiles for Protein Secondary Structure and
Contact Map Prediction",
journal = j-TCBB,
volume = "3",
number = "2",
pages = "98--113",
month = apr,
year = "2006",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2006.17",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 7 06:38:18 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Danziger:2006:FCM,
author = "Samuel A. Danziger and S. Joshua Swamidass and Jue
Zeng and Lawrence R. Dearth and Qiang Lu and Jonathan
H. Chen and Jianlin Cheng and Vinh P. Hoang and Hiroto
Saigo and Ray Luo and Pierre Baldi and Rainer K.
Brachmann and Richard H. Lathrop",
title = "Functional Census of Mutation Sequence Spaces: The
Example of p53 Cancer Rescue Mutants",
journal = j-TCBB,
volume = "3",
number = "2",
pages = "114--125",
month = apr,
year = "2006",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2006.22",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 7 06:38:18 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Carvalho:2006:EAI,
author = "Alexandra M. Carvalho and Ana T. Freitas and Arlindo
L. Oliveira and Marie-France Sagot",
title = "An Efficient Algorithm for the Identification of
Structured Motifs in {DNA} Promoter Sequences",
journal = j-TCBB,
volume = "3",
number = "2",
pages = "126--140",
month = apr,
year = "2006",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2006.16",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 7 06:38:18 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Brown:2006:IPA,
author = "Daniel G. Brown and Ian M. Harrower",
title = "Integer Programming Approaches to Haplotype Inference
by Pure Parsimony",
journal = j-TCBB,
volume = "3",
number = "2",
pages = "141--154",
month = apr,
year = "2006",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2006.24",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 7 06:38:18 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Vass:2006:JMB,
author = "Marc T. Vass and Clifford A. Shaffer and Naren
Ramakrishnan and Layne T. Watson and John J. Tyson",
title = "The {JigCell} Model Builder: a Spreadsheet Interface
for Creating Biochemical Reaction Network Models",
journal = j-TCBB,
volume = "3",
number = "2",
pages = "155--164",
month = apr,
year = "2006",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2006.27",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 7 06:38:18 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2006:MFS,
author = "Duhong Chen and Oliver Eulenstein and David
Fernandez-Baca and Michael Sanderson",
title = "Minimum-Flip Supertrees: Complexity and Algorithms",
journal = j-TCBB,
volume = "3",
number = "2",
pages = "165--173",
month = apr,
year = "2006",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2006.26",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 7 06:38:18 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sevon:2006:TTP,
author = "Petteri Sevon and Hannu Toivonen and Vesa Ollikainen",
title = "{TreeDT}: Tree Pattern Mining for Gene Mapping",
journal = j-TCBB,
volume = "3",
number = "2",
pages = "174--185",
month = apr,
year = "2006",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2006.28",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 7 06:38:18 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Song:2006:CNS,
author = "Yun S. Song",
title = "A Concise Necessary and Sufficient Condition for the
Existence of a Galled-Tree",
journal = j-TCBB,
volume = "3",
number = "2",
pages = "186--191",
month = apr,
year = "2006",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2006.15",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 7 06:38:18 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Galled-trees are a special class of graphical
representation of evolutionary history that has proven
amenable to efficient, polynomial-time algorithms. The
goal of this paper is to construct a concise necessary
and sufficient condition for the existence of a
galled-tree for $M$, a set of binary sequences that
purportedly have evolved in the presence of
recombination. Both root-known and root-unknown cases
are considered here.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Daras:2006:TDS,
author = "Petros Daras and Dimitrios Zarpalas and Apostolos
Axenopoulos and Dimitrios Tzovaras and Michael
Gerassimos Strintzis",
title = "Three-Dimensional Shape-Structure Comparison Method
for Protein Classification",
journal = j-TCBB,
volume = "3",
number = "3",
pages = "193--207",
month = jul,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Sep 11 07:36:29 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2006:MPA,
author = "Weichuan Yu and Xiaoye Li and Junfeng Liu and Baolin
Wu and Kenneth R. Williams and Hongyu Zhao",
title = "Multiple Peak Alignment in Sequential Data Analysis:
a Scale-Space-Based Approach",
journal = j-TCBB,
volume = "3",
number = "3",
pages = "208--219",
month = jul,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Sep 11 07:36:29 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Abul:2006:PAE,
author = "Osman Abul and Reda Alhajj and Faruk Polat",
title = "A Powerful Approach for Effective Finding of
Significantly Differentially Expressed Genes",
journal = j-TCBB,
volume = "3",
number = "3",
pages = "220--231",
month = jul,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Sep 11 07:36:29 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nagarajan:2006:CSC,
author = "Radhakrishnan Nagarajan and Meenakshi Upreti",
title = "Correlation Statistics for {cDNA} Microarray Image
Analysis",
journal = j-TCBB,
volume = "3",
number = "3",
pages = "232--238",
month = jul,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Sep 11 07:36:29 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Song:2006:CAP,
author = "Yun S. Song and Rune Lyngso and Jotun Hein",
title = "Counting All Possible Ancestral Configurations of
Sample Sequences in Population Genetics",
journal = j-TCBB,
volume = "3",
number = "3",
pages = "239--251",
month = jul,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Sep 11 07:36:29 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pirinen:2006:FCG,
author = "Matti Pirinen and Dario Gasbarra",
title = "Finding Consistent Gene Transmission Patterns on Large
and Complex Pedigrees",
journal = j-TCBB,
volume = "3",
number = "3",
pages = "252--262",
month = jul,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Sep 11 07:36:29 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Popescu:2006:FMG,
author = "Mihail Popescu and James M. Keller and Joyce A.
Mitchell",
title = "Fuzzy Measures on the Gene Ontology for Gene Product
Similarity",
journal = j-TCBB,
volume = "3",
number = "3",
pages = "263--274",
month = jul,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Sep 11 07:36:29 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bernt:2006:GRB,
author = "Matthias Bernt and Daniel Merkle and Martin
Middendorf",
title = "Genome Rearrangement Based on Reversals that Preserve
Conserved Intervals",
journal = j-TCBB,
volume = "3",
number = "3",
pages = "275--288",
month = jul,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Sep 11 07:36:29 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Berry:2006:IPC,
author = "Vincent Berry and Fran{\c{c}}ois Nicolas",
title = "Improved Parameterized Complexity of the Maximum
Agreement Subtree and Maximum Compatible Tree
Problems",
journal = j-TCBB,
volume = "3",
number = "3",
pages = "289--302",
month = jul,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Sep 11 07:36:29 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sharan:2006:ITP,
author = "Roded Sharan and Bjarni V. Halldorsson and Sorin
Istrail",
title = "Islands of Tractability for Parsimony Haplotyping",
journal = j-TCBB,
volume = "3",
number = "3",
pages = "303--311",
month = jul,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Sep 11 07:36:29 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2006:SGR,
author = "Chaolin Zhang and Xuesong Lu and Xuegong Zhang",
title = "Significance of Gene Ranking for Classification of
Microarray Samples",
journal = j-TCBB,
volume = "3",
number = "3",
pages = "312--320",
month = jul,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Sep 11 07:36:29 MDT 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Casadio:2006:GEI,
author = "Rita Casadio",
title = "{Guest Editor}'s Introduction to the Special Issue on
Computational Biology and Bioinformatics -- Part 1",
journal = j-TCBB,
volume = "3",
number = "4",
pages = "321--322",
month = oct,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 30 19:05:58 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Snir:2006:UMC,
author = "Sagi Snir and Satish Rao",
title = "Using Max Cut to Enhance Rooted Trees Consistency",
journal = j-TCBB,
volume = "3",
number = "4",
pages = "323--333",
month = oct,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 30 19:05:58 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ganapathy:2006:PIB,
author = "Ganeshkumar Ganapathy and Barbara Goodson and Robert
Jansen and Hai-son Le and Vijaya Ramachandran and Tandy
Warnow",
title = "Pattern Identification in Biogeography",
journal = j-TCBB,
volume = "3",
number = "4",
pages = "334--346",
month = oct,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 30 19:05:58 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wernicke:2006:EDN,
author = "Sebastian Wernicke",
title = "Efficient Detection of Network Motifs",
journal = j-TCBB,
volume = "3",
number = "4",
pages = "347--359",
month = oct,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 30 19:05:58 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lacroix:2006:MSG,
author = "Vincent Lacroix and Cristina G. Fernandes and
Marie-France Sagot",
title = "Motif Search in Graphs: Application to Metabolic
Networks",
journal = j-TCBB,
volume = "3",
number = "4",
pages = "360--368",
month = oct,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 30 19:05:58 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Elias:2006:AAS,
author = "Isaac Elias and Tzvika Hartman",
title = "A $ 1.375 $-Approximation Algorithm for Sorting by
Transpositions",
journal = j-TCBB,
volume = "3",
number = "4",
pages = "369--379",
month = oct,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 30 19:05:58 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Labarre:2006:NBT,
author = "Anthony Labarre",
title = "New Bounds and Tractable Instances for the
Transposition Distance",
journal = j-TCBB,
volume = "3",
number = "4",
pages = "380--394",
month = oct,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 30 19:05:58 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sammeth:2006:CTR,
author = "Michael Sammeth and Jens Stoye",
title = "Comparing Tandem Repeats with Duplications and
Excisions of Variable Degree",
journal = j-TCBB,
volume = "3",
number = "4",
pages = "395--407",
month = oct,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 30 19:05:58 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bilu:2006:FAO,
author = "Yonatan Bilu and Pankaj K. Agarwal and Rachel
Kolodny",
title = "Faster Algorithms for Optimal Multiple Sequence
Alignment Based on Pairwise Comparisons",
journal = j-TCBB,
volume = "3",
number = "4",
pages = "408--422",
month = oct,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 30 19:05:58 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Song:2006:EPA,
author = "Yinglei Song and Chunmei Liu and Xiuzhen Huang and
Russell L. Malmberg and Ying Xu and Liming Cai",
title = "Efficient Parameterized Algorithms for Biopolymer
Structure-Sequence Alignment",
journal = j-TCBB,
volume = "3",
number = "4",
pages = "423--432",
month = oct,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 30 19:05:58 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2006:AI,
author = "Anonymous",
title = "Annual Index",
journal = j-TCBB,
volume = "3",
number = "4",
pages = "??--??",
month = oct,
year = "2006",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 30 19:05:58 MST 2006",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gusfield:2007:SJ,
author = "Dan Gusfield",
title = "State of the {Journal}",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "1--1",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gusfield:2007:AEAa,
author = "Dan Gusfield",
title = "{Associate Editor} Appreciation and Welcome",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "2--2",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Casadio:2007:GEI,
author = "Rita Casadio",
title = "{Guest Editor}'s Introduction to the {Special Section
on Computational Biology and Bioinformatics (WABI)} --
Part 2",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "3--3",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Berard:2007:PSR,
author = "Severine B{\'e}rard and Anne Bergeron and Cedric
Chauve and Christophe Paul",
title = "Perfect Sorting by Reversals Is Not Always Difficult",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "4--16",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We propose new algorithms for computing pairwise
rearrangement scenarios that conserve the combinatorial
structure of genomes. More precisely, we investigate
the problem of sorting signed permutations by reversals
without breaking common intervals. We describe a
combinatorial framework for this problem that allows us
to characterize classes of signed permutations for
which one can compute, in polynomial time, a shortest
reversal scenario that conserves all common intervals.
In particular, we define a class of permutations for
which this computation can be done in linear time with
a very simple algorithm that does not rely on the
classical Hannenhalli-Pevzner theory for sorting by
reversals. We apply these methods to the computation of
rearrangement scenarios between permutations obtained
from 16 synteny blocks of the X chromosomes of the
human, mouse, and rat.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "common intervals; evolution scenarios; reversals",
}
@Article{Vashist:2007:OCM,
author = "Akshay Vashist and Casimir A. Kulikowski and Ilya
Muchnik",
title = "Ortholog Clustering on a Multipartite Graph",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "17--27",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present a method for automatically extracting
groups of orthologous genes from a large set of genomes
by a new clustering algorithm on a weighted
multipartite graph. The method assigns a score to an
arbitrary subset of genes from multiple genomes to
assess the orthologous relationships between genes in
the subset. This score is computed using sequence
similarities between the member genes and the
phylogenetic relationship between the corresponding
genomes. An ortholog cluster is found as the subset
with the highest score, so ortholog clustering is
formulated as a combinatorial optimization problem. The
algorithm for finding an ortholog cluster runs in time
$ O(|E| + |V| l o g|V|) $, where $V$ and $E$ are the
sets of vertices and edges, respectively, in the graph.
However, if we discretize the similarity scores into a
constant number of bins, the runtime improves to $
O(|E| + |V|) $. The proposed method was applied to
seven complete eukaryote genomes on which the manually
curated database of eukaryotic ortholog clusters, KOG,
is constructed. A comparison of our results with the
manually curated ortholog clusters shows that our
clusters are well correlated with the existing
clusters.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biology; clustering algorithms; genetics;
Graph-theoretic methods",
}
@Article{Lasker:2007:EDH,
author = "Keren Lasker and Oranit Dror and Maxim Shatsky and
Ruth Nussinov and Haim J. Wolfson",
title = "{EMatch}: Discovery of High Resolution Structural
Homologues of Protein Domains in Intermediate
Resolution Cryo-{EM} Maps",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "28--39",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Cryo-EM has become an increasingly powerful technique
for elucidating the structure, dynamics, and function
of large flexible macromolecule assemblies that cannot
be determined at atomic resolution. However, due to the
relatively low resolution of cryo-EM data, a major
challenge is to identify components of complexes
appearing in cryo-EM maps. Here, we describe EMatch, a
novel integrated approach for recognizing structural
homologues of protein domains present in a 6-10{\AA}
resolution cryo-EM map and constructing a quasi-atomic
structural model of their assembly. The method is
highly efficient and has been successfully validated on
various simulated data. The strength of the method is
demonstrated by a domain assembly of an experimental
cryo-EM map of native GroEL at 6{\AA} resolution.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "3D alignment of secondary structures; cyclic symmetry;
intermediate resolution cryo-EM maps; macromolecular
assemblies; structural bioinformatics",
}
@Article{Wang:2007:ACC,
author = "Lipo Wang and Feng Chu and Wei Xie",
title = "Accurate Cancer Classification Using Expressions of
Very Few Genes",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "40--53",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We aim at finding the smallest set of genes that can
ensure highly accurate classification of cancers from
microarray data by using supervised machine learning
algorithms. The significance of finding the minimum
gene subsets is three-fold: (1) It greatly reduces the
computational burden and `noise' arising from
irrelevant genes. In the examples studied in this
paper, finding the minimum gene subsets even allows for
extraction of simple diagnostic rules which lead to
accurate diagnosis without the need for any
classifiers. (2) It simplifies gene expression tests to
include only a very small number of genes rather than
thousands of genes, which can bring down the cost for
cancer testing significantly. (3) It calls for further
investigation into the possible biological relationship
between these small numbers of genes and cancer
development and treatment. Our simple yet very
effective method involves two steps. In the first step,
we choose some important genes using a feature
importance ranking scheme. In the second step, we test
the classification capability of all simple
combinations of those important genes by using a good
classifier. For three `small' and `simple' data sets
with two, three, and four cancer (sub)types, our
approach obtained very high accuracy with only two or
three genes. For a `large' and `complex' data set with
14 cancer types, we divided the whole problem into a
group of binary classification problems and applied the
2--step approach to each of these binary classification
problems. Through this `divide-and-conquer' approach,
we obtained accuracy comparable to previously reported
results but with only 28 genes rather than 16,063
genes. In general, our method can significantly reduce
the number of genes required for highly reliable
diagnosis.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "cancer classification; fuzzy; gene expression; neural
networks; support vector machines.",
}
@Article{Zhi:2007:CBA,
author = "Degui Zhi and Uri Keich and Pavel Pevzner and Steffen
Heber and Haixu Tang",
title = "Correcting Base-Assignment Errors in Repeat Regions of
Shotgun Assembly",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "54--64",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Accurate base-assignment in repeat regions of a whole
genome shotgun assembly is an unsolved problem. Since
reads in repeat regions cannot be easily attributed to
a unique location in the genome, current assemblers may
place these reads arbitrarily. As a result, the
base-assignment error rate in repeats is likely to be
much higher than that in the rest of the genome. We
developed an iterative algorithm, EULER-AIR, that is
able to correct base-assignment errors in finished
genome sequences in public databases. The Wolbachia
genome is among the best finished genomes. Using this
genome project as an example, we demonstrated that
EULER-AIR can (1) discover and correct base-assignment
errors, (2) provide accurate read assignments, (3)
utilize finishing reads for accurate base-assignment,
and (4) provide guidance for designing finishing
experiments. In the genome of Wolbachia, EULER-AIR
found 16 positions with ambiguous base-assignment and
two positions with erroneous bases. Besides Wolbachia,
many other genome sequencing projects have
significantly fewer finishing reads and, hence, are
likely to contain more base-assignment errors in
repeats. We demonstrate that EULER-AIR is a software
tool that can be used to find and correct
base-assignment errors in a genome assembly project.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "expectation maximization; finishing; fragment
assembly",
}
@Article{Xu:2007:MCC,
author = "Rui Xu and Georgios C. Anagnostopoulos and Donald C.
Wunsch",
title = "Multiclass Cancer Classification Using Semisupervised
Ellipsoid {ARTMAP} and Particle Swarm Optimization with
Gene Expression Data",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "65--77",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "It is crucial for cancer diagnosis and treatment to
accurately identify the site of origin of a tumor. With
the emergence and rapid advancement of DNA microarray
technologies, constructing gene expression profiles for
different cancer types has already become a promising
means for cancer classification. In addition to
research on binary classification such as normal versus
tumor samples, which attracts numerous efforts from a
variety of disciplines, the discrimination of multiple
tumor types is also important. Meanwhile, the selection
of genes which are relevant to a certain cancer type
not only improves the performance of the classifiers,
but also provides molecular insights for treatment and
drug development. Here, we use Semisupervised Ellipsoid
ARTMAP (ssEAM) for multiclass cancer discrimination and
particle swarm optimization for informative gene
selection. ssEAM is a neural network architecture
rooted in Adaptive Resonance Theory and suitable for
classification tasks. ssEAM features fast, stable, and
finite learning and creates hyperellipsoidal clusters,
inducing complex nonlinear decision boundaries. PSO is
an evolutionary algorithm-based technique for global
optimization. A discrete binary version of PSO is
employed to indicate whether genes are chosen or not.
The effectiveness of ssEAM\slash PSO for multiclass
cancer diagnosis is demonstrated by testing it on three
publicly available multiple-class cancer data sets.
ssEAM\slash PSO achieves competitive performance on all
these data sets, with results comparable to or better
than those obtained by other classifiers.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "cancer classification; gene expression profile;
particle swarm optimization; semisupervised ellipsoid
ARTMAP",
}
@Article{Huang:2007:PPP,
author = "Chengbang Huang and Faruck Morcos and Simon P. Kanaan
and Stefan Wuchty and Danny Z. Chen and Jesus A.
Izaguirre",
title = "Predicting Protein-Protein Interactions from Protein
Domains Using a Set Cover Approach",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "78--87",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "One goal of contemporary proteome research is the
elucidation of cellular protein interactions. Based on
currently available protein-protein interaction and
domain data, we introduce a novel method, Maximum
Specificity Set Cover (MSSC), for the prediction of
protein-protein interactions. In our approach, we map
the relationship between interactions of proteins and
their corresponding domain architectures to a
generalized weighted set cover problem. The application
of a greedy algorithm provides sets of domain
interactions which explain the presence of protein
interactions to the largest degree of specificity.
Utilizing domain and protein interaction data of {\em
S. cerevisiae}, MSSC enables prediction of previously
unknown protein interactions, links that are well
supported by a high tendency of coexpression and
functional homogeneity of the corresponding proteins.
Focusing on concrete examples, we show that MSSC
reliably predicts protein interactions in well-studied
molecular systems, such as the 26S proteasome and RNA
polymerase II of \bioname{S. cerevisiae}. We also show that
the quality of the predictions is comparable to the
Maximum Likelihood Estimation while MSSC is faster.
This new algorithm and all data sets used are
accessible through a Web portal at
\path=http://ppi.cse.nd.edu=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "bioinformatics (genome or protein) databases; biology;
Computations on discrete structures; genetics; graph
algorithms",
}
@Article{Kim:2007:AAD,
author = "Jong Hyun Kim and Michael S. Waterman and Lei M. Li",
title = "Accuracy Assessment of Diploid Consensus Sequences",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "88--97",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "If the origins of fragments are known in genome
sequencing projects, it is straightforward to
reconstruct diploid consensus sequences. In reality,
however, this is not true. Although there are proposed
methods to reconstruct haplotypes from genome
sequencing projects, an accuracy assessment is required
to evaluate the confidence of the estimated diploid
consensus sequences. In this paper, we define the
confidence score of diploid consensus sequences. It
requires the calculation of the likelihood of an
assembly. To calculate the likelihood, we propose a
linear time algorithm with respect to the number of
polymorphic sites. The likelihood calculation and
confidence score are used for further improvements of
haplotype estimation in two directions. One direction
is that low-scored phases are disconnected. The other
direction is that, instead of using nominal frequency
1/2, the haplotype frequency is estimated to reflect
the actual contribution of each haplotype. Our method
was evaluated on the simulated data whose polymorphism
rate (1.2 percent) was based on Ciona intestinalis. As
a result, the high accuracy of our algorithm was
indicated: The true positive rate of the haplotype
estimation was greater than 97 percent.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "diploid; haplotype; polymorphism; shotgun sequencing",
}
@Article{Alekseyev:2007:CBG,
author = "Max A. Alekseyev and Pavel A. Pevzner",
title = "Colored {de Bruijn} Graphs and the Genome Halving
Problem",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "98--107",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Breakpoint graph analysis is a key algorithmic
technique in studies of genome rearrangements. However,
breakpoint graphs are defined only for genomes without
duplicated genes, thus limiting their applications in
rearrangement analysis. We discuss a connection between
the breakpoint graphs and de Bruijn graphs that leads
to a generalization of the notion of breakpoint graph
for genomes with duplicated genes. We further use the
generalized breakpoint graphs to study the Genome
Halving Problem (first introduced and solved by Nadia
El-Mabrouk and David Sankoff). The El-Mabrouk-Sankoff
algorithm is rather complex, and, in this paper, we
present an alternative approach that is based on
generalized breakpoint graphs. The generalized
breakpoint graphs make the El-Mabrouk-Sankoff result
more transparent and promise to be useful in future
studies of genome rearrangements.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "breakpoint graph; de Bruijn graph; genome duplication;
genome halving; genome rearrangement; reversal",
}
@Article{Mossel:2007:DMT,
author = "Elchanan Mossel",
title = "Distorted Metrics on Trees and Phylogenetic Forests",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "108--116",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We study distorted metrics on binary trees in the
context of phylogenetic reconstruction. Given a binary
tree $T$ on $n$ leaves with a path metric $d$, consider
the pairwise distances $ d(u, v) $ between leaves. It
is well known that these determine the tree and the
$d$-length of all edges. Here, we consider distortions
$ \hat {d} $ of $d$ such that, for all leaves $u$ and
$v$, it holds that $ |d(u, v) - \hat {d}(u, v)| < f / 2
$ if either $ d(u, v) < M + f / 2 $ or $ \hat {d}(u, v)
< M + f / 2 $, where $d$ satisfies $ f \leq d(e) \leq g
$ for all edges $e$. Given such distortions, we show
how to reconstruct in polynomial time a forest $ T_1,
\ldots {}, T_\alpha $ such that the true tree $T$ may
be obtained from that forest by adding $ \alpha - 1 $
edges and $ \alpha - 1 \leq 2 - \Omega (M / g) n $. Our
distorted metric result implies a reconstruction
algorithm of phylogenetic forests with a small number
of trees from sequences of length logarithmic in the
number of species. The reconstruction algorithm is
applicable for the general Markov model. Both the
distorted metric result and its applications to
phylogeny are almost tight.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "CFN; distortion; forest; Jukes--Cantor; metric;
phylogenetics; tree",
}
@Article{Aeling:2007:DDE,
author = "Kimberly A. Aeling and Nicholas R. Steffen and Matthew
Johnson and G. Wesley Hatfield and Richard H. Lathrop
and Donald F. Senear",
title = "{DNA} Deformation Energy as an Indirect Recognition
Mechanism in Protein-{DNA} Interactions",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "117--125",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Proteins that bind to specific locations in genomic
DNA control many basic cellular functions. Proteins
detect their binding sites using both direct and
indirect recognition mechanisms. Deformation energy,
which models the energy required to bend DNA from its
native shape to its shape when bound to a protein, has
been shown to be an indirect recognition mechanism for
one particular protein, Integration Host Factor (IHF).
This work extends the analysis of deformation to two
other DNA-binding proteins, CRP and SRF, and two
endonucleases, I-CreI and I-PpoI. Known binding sites
for all five proteins showed statistically significant
differences in mean deformation energy as compared to
random sequences. Binding sites for the three
DNA-binding proteins and one of the endonucleases had
mean deformation energies lower than random sequences.
Binding sites for I-PpoI had mean deformation energy
higher than random sequences. Classifiers that were
trained using the deformation energy at each base pair
step showed good cross-validated accuracy when
classifying unseen sequences as binders or nonbinders.
These results support DNA deformation energy as an
indirect recognition mechanism across a wider range of
DNA-binding proteins. Deformation energy may also have
a predictive capacity for the underlying catalytic
mechanism of DNA-binding enzymes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "deformation energy; DNA bending; DNA-protein binding;
indirect readout; indirect recognition; perceptron
learning",
}
@Article{Yang:2007:MFE,
author = "Jing Yang and Sarawan Wongsa and Visakan
Kadirkamanathan and Stephen A. Billings and Phillip C.
Wright",
title = "Metabolic Flux Estimation --- a Self-Adaptive
Evolutionary Algorithm with Singular Value
Decomposition",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "126--138",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Metabolic flux analysis is important for metabolic
system regulation and intracellular pathway
identification. A popular approach for intracellular
flux estimation involves using $^{13}{\rm C}$ tracer
experiments to label states that can be measured by
nuclear magnetic resonance spectrometry or gas
chromatography mass spectrometry. However, the bilinear
balance equations derived from $^{13}{\rm C}$ tracer
experiments and the noisy measurements require a
nonlinear optimization approach to obtain the optimal
solution. In this paper, the flux quantification
problem is formulated as an error-minimization problem
with equality and inequality constraints through the
$^{13}{\rm C}$ balance and stoichiometric equations.
The stoichiometric constraints are transformed to a
null space by singular value decomposition.
Self-adaptive evolutionary algorithms are then
introduced for flux quantification. The performance of
the evolutionary algorithm is compared with ordinary
least squares estimation by the simulation of the
central pentose phosphate pathway. The proposed
algorithm is also applied to the central metabolism of
Corynebacterium glutamicum under lysine-producing
conditions. A comparison between the results from the
proposed algorithm and data from the literature is
given. The complexity of a metabolic system with
bidirectional reactions is also investigated by
analyzing the fluctuations in the flux estimates when
available measurements are varied.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "evolutionary computing; least squares method;
metabolic flux analysis; singular value
decomposition.",
}
@Article{Wu:2007:QBP,
author = "Gang Wu and Jia-Huai You and Guohui Lin",
title = "Quartet-Based Phylogeny Reconstruction with Answer Set
Programming",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "139--152",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, a new representation is presented for
the Maximum Quartet Consistency (MQC) problem, where
solving the MQC problem becomes searching for an
ultrametric matrix that satisfies a maximum number of
given quartet topologies. A number of structural
properties of the MQC problem in this new
representation are characterized through formulating
into answer set programming, a recent powerful logic
programming tool for modeling and solving search
problems. Using these properties, a number of
optimization techniques are proposed to speed up the
search process. The experimental results on a number of
simulated data sets suggest that the new
representation, combined with answer set programming,
presents a unique perspective to the MQC problem.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Answer Set Programming (ASP); Maximum Quartet
Consistency (MQC); phylogeny; quartet; ultrametric
matrix.",
}
@Article{Reinert:2007:LLE,
author = "Gesine Reinert and Michael S. Waterman",
title = "On the Length of the Longest Exact Position Match in a
Random Sequence",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "153--156",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A mixed Poisson approximation and a Poisson
approximation for the length of the longest exact match
of a random sequence across another sequence are
provided, where the match is required to start at
position 1 in the first sequence. This problem arises
when looking for suitable anchors in whole genome
alignments.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Chen-Stein method; length of longest match; mixed
Poisson approximation; Poisson approximation",
}
@Article{Au:2007:CAC,
author = "Wai-Ho Au and Keith C. C. Chan and Andrew K. C. Wong
and Yang Wang",
title = "Correction to {``Attribute Clustering for Grouping,
Selection, and Classification of Gene Expression
Data''}",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "157--157",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This is a correction to a typographical error in (11)
in [1] which present the calculation of the sum of the
multiple significant interdependence redundancy
measure. Equation (11) in [1] should be: $$ k = \arg
\max \nolimits_{k \in \{ 2, \ldots, p \} } \sum_{r =
1}^k \sum_{A_i \in \{ C_r - \eta_r \} }R(A_i \colon \eta_r).
$$ (11)We remark that the experimental results reported
in [1] are based on (11) above not (11) in [1].",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Staff:2007:RL,
author = "{IEEE and ACM Transactions on Computational Biology
and Bioinformatics staff}",
title = "2006 Reviewers List",
journal = j-TCBB,
volume = "4",
number = "1",
pages = "158--160",
month = jan,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:20 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rajapakse:2007:GEI,
author = "Jagath C. Rajapakse and Yan-Qing Zhang and Gary B.
Fogel",
title = "{Guest Editors}' Introduction to the {Special Section:
Computational Intelligence Approaches in Computational
Biology and Bioinformatics}",
journal = j-TCBB,
volume = "4",
number = "2",
pages = "161--162",
month = apr,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:55 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2007:PBS,
author = "Haiying Wang and Huiru Zheng and Francisco Azuaje",
title = "{Poisson}-Based Self-Organizing Feature Maps and
Hierarchical Clustering for Serial Analysis of Gene
Expression Data",
journal = j-TCBB,
volume = "4",
number = "2",
pages = "163--175",
month = apr,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:55 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Serial analysis of gene expression (SAGE) is a
powerful technique for global gene expression
profiling, allowing simultaneous analysis of thousands
of transcripts without prior structural and functional
knowledge. Pattern discovery and visualization have
become fundamental approaches to analyzing such
large-scale gene expression data. From the pattern
discovery perspective, clustering techniques have
received great attention. However, due to the
statistical nature of SAGE data (i.e., underlying
distribution), traditional clustering techniques may
not be suitable for SAGE data analysis. Based on the
adaptation and improvement of Self-Organizing Maps and
hierarchical clustering techniques, this paper presents
two new clustering algorithms, namely, PoissonS and
PoissonHC, for SAGE data analysis. Tested on synthetic
and experimental SAGE data, these algorithms
demonstrate several advantages over traditional pattern
discovery techniques. The results indicate that, by
incorporating statistical properties of SAGE data,
PoissonS and PoissonHC, as well as a hybrid approach
(neuro-hierarchical approach) based on the combination
of PoissonS and PoissonHC, offer significant
improvements in pattern discovery and visualization for
SAGE data. Moreover, a user-friendly platform, which
may improve and accelerate SAGE data mining, was
implemented. The system is freely available on request
from the authors for nonprofit use.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "hybrid machine learning; Pattern discovery and
visualization; Poisson distribution; self-organizing
maps; serial analysis of gene expression.",
}
@Article{Sjahputera:2007:RAC,
author = "Ozy Sjahputera and James M. Keller and J. Wade Davis
and Kristen H. Taylor and Farahnaz Rahmatpanah and
Huidong Shi and Derek T. Anderson and Samuel N. Blisard
and Robert H. Luke and Mihail Popescu and Gerald C.
Arthur and Charles W. Caldwell",
title = "Relational Analysis of {CpG} Islands Methylation and
Gene Expression in Human Lymphomas Using Possibilistic
{C}-Means Clustering and Modified Cluster Fuzzy
Density",
journal = j-TCBB,
volume = "4",
number = "2",
pages = "176--189",
month = apr,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:55 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Heterogeneous genetic and epigenetic alterations are
commonly found in human non-Hodgkin's lymphomas (NHL).
One such epigenetic alteration is aberrant methylation
of gene promoter-related CpG islands, where
hypermethylation frequently results in transcriptional
inactivation of target genes, while a decrease or loss
of promoter methylation (hypomethylation) is frequently
associated with transcriptional activation. Discovering
genes with these relationships in NHL or other types of
cancers could lead to a better understanding of the
pathobiology of these diseases. The simultaneous
analysis of promoter methylation using Differential
Methylation Hybridization (DMH) and its associated gene
expression using Expressed CpG Island Sequence Tag
(ECIST) microarrays generates a large volume of
methylation-expression relational data. To analyze this
data, we propose a set of algorithms based on fuzzy
sets theory, in particular Possibilistic c-Means (PCM)
and cluster fuzzy density. For each gene, these
algorithms calculate measures of confidence of various
methylation-expression relationships in each NHL
subclass. Thus, these tools can be used as a means of
high volume data exploration to better guide biological
confirmation using independent molecular biology
methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "cluster density; clustering; expression; fuzzy sets;
Methylation; microarray",
}
@Article{Lu:2007:ISL,
author = "Yijuan Lu and Qi Tian and Feng Liu and Maribel Sanchez
and Yufeng Wang",
title = "Interactive Semisupervised Learning for Microarray
Analysis",
journal = j-TCBB,
volume = "4",
number = "2",
pages = "190--203",
month = apr,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:55 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Microarray technology has generated vast amounts of
gene expression data with distinct patterns. Based on
the premise that genes of correlated functions tend to
exhibit similar expression patterns, various machine
learning methods have been applied to capture these
specific patterns in microarray data. However, the
discrepancy between the rich expression profiles and
the limited knowledge of gene functions has been a
major hurdle to the understanding of cellular networks.
To bridge this gap so as to properly comprehend and
interpret expression data, we introduce Relevance
Feedback to microarray analysis and propose an
interactive learning framework to incorporate the
expert knowledge into the decision module. In order to
find a good learning method and solve two intrinsic
problems in microarray data, high dimensionality and
small sample size, we also propose a semisupervised
learning algorithm: Kernel Discriminant-EM (KDEM). This
algorithm efficiently utilizes a large set of unlabeled
data to compensate for the insufficiency of a small set
of labeled data and it extends the linear algorithm in
Discriminant-EM (DEM) to a kernel algorithm to handle
nonlinearly separable data in a lower dimensional
space. The Relevance Feedback technique and KDEM
together construct an efficient and effective
interactive semisupervised learning framework for
microarray analysis. Extensive experiments on the yeast
cell cycle regulation data set and Plasmodium
falciparum red blood cell cycle data set show the
promise of this approach.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "kernel DEM; microarray analysis; relevance feedback;
semisupervised learning",
}
@Article{Lerner:2007:CSI,
author = "Boaz Lerner and Josepha Yeshaya and Lev Koushnir",
title = "On the Classification of a Small Imbalanced
Cytogenetic Image Database",
journal = j-TCBB,
volume = "4",
number = "2",
pages = "204--215",
month = apr,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:55 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Solving a multiclass classification task using a small
imbalanced database of patterns of high dimension is
difficult due to the curse-of-dimensionality and the
bias of the training toward the majority classes. Such
a problem has arisen while diagnosing genetic
abnormalities by classifying a small database of
fluorescence in situ hybridization signals of types
having different frequencies of occurrence. We propose
and experimentally study using the cytogenetic domain
two solutions to the problem. The first is hierarchical
decomposition of the classification task, where each
hierarchy level is designed to tackle a simpler problem
which is represented by classes that are approximately
balanced. The second solution is balancing the data by
up-sampling the minority classes accompanied by
dimensionality reduction. Implemented by the naive
Bayesian classifier or the multilayer perceptron neural
network, both solutions have diminished the problem and
contributed to accuracy improvement. In addition, the
experiments suggest that coping with the smallness of
the data is more beneficial than dealing with its
imbalance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "classification; dimensionality reduction; genetic
diagnosis; imbalanced data; multilayer perceptron
(MLP); naive Bayesian classifier (NBC); small sample
size.",
}
@Article{Igel:2007:GBO,
author = "Christian Igel and Tobias Glasmachers and Britta
Mersch and Nico Pfeifer and Peter Meinicke",
title = "Gradient-Based Optimization of Kernel-Target Alignment
for Sequence Kernels Applied to Bacterial Gene Start
Detection",
journal = j-TCBB,
volume = "4",
number = "2",
pages = "216--226",
month = apr,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:55 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Biological data mining using kernel methods can be
improved by a task-specific choice of the kernel
function. Oligo kernels for genomic sequence analysis
have proven to have a high discriminative power and to
provide interpretable results. Oligo kernels that
consider subsequences of different lengths can be
combined and parameterized to increase their
flexibility. For adapting these parameters efficiently,
gradient-based optimization of the kernel-target
alignment is proposed. The power of this new, general
model selection procedure and the benefits of fitting
kernels to problem classes are demonstrated by adapting
oligo kernels for bacterial gene start detection.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "kernel target alignment; model selection; oligo
kernel; sequence analysis; support vector machines;
translation initiation sites",
}
@Article{Ogul:2007:SLP,
author = "Hasan Ogul and Erkan U. Mumcuo{\u{g}}lu",
title = "Subcellular Localization Prediction with New Protein
Encoding Schemes",
journal = j-TCBB,
volume = "4",
number = "2",
pages = "227--232",
month = apr,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:55 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Subcellular localization is one of the key properties
in functional annotation of proteins. Support vector
machines (SVMs) have been widely used for automated
prediction of subcellular localizations. Existing
methods differ in the protein encoding schemes used. In
this study, we present two methods for protein encoding
to be used for SVM-based subcellular localization
prediction: n{\hbox{-}}\rm peptide compositions with
reduced amino acid alphabets for larger values of $n$
and pairwise sequence similarity scores based on whole
sequence and N-terminal sequence. We tested the methods
on a common benchmarking data set that consists of
2,427 eukaryotic proteins with four localization sites.
As a result of 5-fold cross-validation tests, the
encoding with n{\hbox{-}}\rm peptide compositions
provided the accuracies of 84.5, 88.9, 66.3, and 94.3
percent for cytoplasmic, extracellular, mitochondrial,
and nuclear proteins, where the overall accuracy was
87.1 percent. The second method provided 83.6, 87.7,
87.9, and 90.5 percent accuracies for individual
locations and 87.8 percent overall accuracy. A hybrid
system, which we called PredLOC, makes a final decision
based on the results of the two presented methods which
achieved an overall accuracy of 91.3 percent, which is
better than the achievements of many of the existing
methods. The new system also outperformed the recent
methods in the experiments conducted on a new-unique
SWISSPROT test set.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "n{\hbox{-}}\rm peptide composition; probabilistic
suffix tree; subcellular localization; support vector
machines.",
}
@Article{Li:2007:DSD,
author = "Wenyuan Li and Ying Liu and Hung-Chung Huang and
Yanxiong Peng and Yongjing Lin and Wee-Keong Ng and
Kok-Leong Ong",
title = "Dynamical Systems for Discovering Protein Complexes
and Functional Modules from Biological Networks",
journal = j-TCBB,
volume = "4",
number = "2",
pages = "233--250",
month = apr,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:55 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recent advances in high throughput experiments and
annotations via published literature have provided a
wealth of interaction maps of several biomolecular
networks, including metabolic, protein-protein, and
protein-DNA interaction networks. The architecture of
these molecular networks reveals important principles
of cellular organization and molecular functions.
Analyzing such networks, i.e., discovering dense
regions in the network, is an important way to identify
protein complexes and functional modules. This task has
been formulated as the problem of finding heavy
subgraphs, the Heaviest k{\hbox{-}}\rm Subgraph Problem
(k{\hbox{-}}\rm HSP), which itself is NP-hard. However,
any method based on the k{\hbox{-}}\rm HSP requires the
parameter $k$ and an exact solution of k{\hbox{-}}\rm
HSP may still end up as a `spurious' heavy subgraph,
thus reducing its practicability in analyzing large
scale biological networks. We proposed a new
formulation, called the rank-HSP, and two dynamical
systems to approximate its results. In addition, a
novel metric, called the Standard deviation and Mean
Ratio (SMR), is proposed for use in `spurious' heavy
subgraphs to automate the discovery by setting a fixed
threshold. Empirical results on both the simulated
graphs and biological networks have demonstrated the
efficiency and effectiveness of our proposal.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "bioinformatics databases; evolutionary computing;
Graph algorithms; neural nets",
}
@Article{Hu:2007:DMP,
author = "Xiaohua Hu and Daniel D. Wu",
title = "Data Mining and Predictive Modeling of Biomolecular
Network from Biomedical Literature Databases",
journal = j-TCBB,
volume = "4",
number = "2",
pages = "251--263",
month = apr,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:55 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, we present a novel approach Bio-IEDM
(Biomedical Information Extraction and Data Mining) to
integrate text mining and predictive modeling to
analyze biomolecular network from biomedical literature
databases. Our method consists of two phases. In phase
1, we discuss a semisupervised efficient learning
approach to automatically extract biological
relationships such as protein-protein interaction,
protein-gene interaction from the biomedical literature
databases to construct the biomolecular network. Our
method automatically learns the patterns based on a few
user seed tuples and then extracts new tuples from the
biomedical literature based on the discovered patterns.
The derived biomolecular network forms a large
scale-free network graph. In phase 2, we present a
novel clustering algorithm to analyze the biomolecular
network graph to identify biologically meaningful
subnetworks (communities). The clustering algorithm
considers the characteristics of the scale-free network
graphs and is based on the local density of the vertex
and its neighborhood functions that can be used to find
more meaningful clusters with different density level.
The experimental results indicate our approach is very
effective in extracting biological knowledge from a
huge collection of biomedical literature. The
integration of data mining and information extraction
provides a promising direction for analyzing the
biomolecular network.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biological complexes (communities); biomolecular
network; information extraction; scale-free network;
semisupervised learning",
}
@Article{Neri:2007:AMA,
author = "Ferrante Neri and Jari Toivanen and Giuseppe Leonardo
Cascella and Yew-Soon Ong",
title = "An Adaptive Multimeme Algorithm for Designing {HIV}
Multidrug Therapies",
journal = j-TCBB,
volume = "4",
number = "2",
pages = "264--278",
month = apr,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:55 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper proposes a period representation for
modeling the multidrug HIV therapies and an Adaptive
Multimeme Algorithm (AMmA) for designing the optimal
therapy. The period representation offers benefits in
terms of flexibility and reduction in dimensionality
compared to the binary representation. The AMmA is a
memetic algorithm which employs a list of three local
searchers adaptively activated by an evolutionary
framework. These local searchers, having different
features according to the exploration logic and the
pivot rule, have the role of exploring the decision
space from different and complementary perspectives
and, thus, assisting the standard evolutionary
operators in the optimization process. Furthermore, the
AMmA makes use of an adaptation which dynamically sets
the algorithmic parameters in order to prevent
stagnation and premature convergence. The numerical
results demonstrate that the application of the
proposed algorithm leads to very efficient medication
schedules which quickly stimulate a strong immune
response to HIV. The earlier termination of the
medication schedule leads to lesser unpleasant side
effects for the patient due to strong antiretroviral
therapy. A numerical comparison shows that the AMmA is
more efficient than three popular metaheuristics.
Finally, a statistical test based on the calculation of
the tolerance interval confirms the superiority of the
AMmA compared to the other methods for the problem
under study.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "adaptive algorithms; HIV therapy design; memetic
algorithms; nonlinear integer programming.",
}
@Article{Handl:2007:MOB,
author = "Julia Handl and Douglas B. Kell and Joshua Knowles",
title = "Multiobjective Optimization in Bioinformatics and
Computational Biology",
journal = j-TCBB,
volume = "4",
number = "2",
pages = "279--292",
month = apr,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:55 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper reviews the application of multiobjective
optimization in the fields of bioinformatics and
computational biology. A survey of existing work,
organized by application area, forms the main body of
the review, following an introduction to the key
concepts in multiobjective optimization. An original
contribution of the review is the identification of
five distinct `contexts,' giving rise to multiple
objectives: These are used to explain the reasons
behind the use of multiobjective optimization in each
application area and also to point the way to potential
future uses of the technique.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "bioinformatics (genome or protein) databases;
classification and association rules; clustering;
experimental design; global optimization; interactive
data exploration and discovery; machine learning",
}
@Article{Bontempi:2007:BSI,
author = "Gianluca Bontempi",
title = "A Blocking Strategy to Improve Gene Selection for
Classification of Gene Expression Data",
journal = j-TCBB,
volume = "4",
number = "2",
pages = "293--300",
month = apr,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:55 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Because of high dimensionality, machine learning
algorithms typically rely on feature selection
techniques in order to perform effective classification
in microarray gene expression data sets. However, the
large number of features compared to the number of
samples makes the task of feature selection
computationally hard and prone to errors. This paper
interprets feature selection as a task of stochastic
optimization, where the goal is to select among an
exponential number of alternative gene subsets the one
expected to return the highest generalization in
classification. Blocking is an experimental design
strategy which produces similar experimental conditions
to compare alternative stochastic configurations in
order to be confident that observed differences in
accuracy are due to actual differences rather than to
fluctuations and noise effects. We propose an original
blocking strategy for improving feature selection which
aggregates in a paired way the validation outcomes of
several learning algorithms to assess a gene subset and
compare it to others. This is a novelty with respect to
conventional wrappers, which commonly adopt a sole
learning algorithm to evaluate the relevance of a given
set of variables. The rationale of the approach is
that, by increasing the amount of experimental
conditions under which we validate a feature subset, we
can lessen the problems related to the scarcity of
samples and consequently come up with a better
selection. The paper shows that the blocking strategy
significantly improves the performance of a
conventional forward selection for a set of 16 publicly
available cancer expression data sets. The experiments
involve six different classifiers and show that
improvements take place independent of the
classification algorithm used after the selection step.
Two further validations based on available biological
annotation support the claim that blocking strategies
in feature selection may improve the accuracy and the
quality of the solution. The first validation is based
on retrieving PubMEd abstracts associated to the
selected genes and matching them to regular expressions
describing the biological phenomenon underlying the
expression data sets. The biological validation that
follows is based on the use of the Bioconductor package
GoStats in order to perform Gene Ontology statistical
analysis.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "bioinformatics (genome or protein) databases; data
mining; feature evaluation and selection; machine
learning",
}
@Article{Diekmann:2007:EUR,
author = "Yoan Diekmann and Marie-France Sagot and Eric
Tannier",
title = "Evolution under Reversals: Parsimony and Conservation
of Common Intervals",
journal = j-TCBB,
volume = "4",
number = "2",
pages = "301--309",
month = apr,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:55 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In comparative genomics, gene order data is often
modeled as signed permutations. A classical problem for
genome comparison is to detect common intervals in
permutations, that is, genes that are colocalized in
several species, indicating that they remained grouped
during evolution. A second largely studied problem
related to gene order is to compute a minimum scenario
of reversals that transforms a signed permutation into
another. Several studies began to mix the two problems
and it was observed that their results are not always
compatible: Often, parsimonious scenarios of reversals
break common intervals. If a scenario does not break
any common interval, it is called perfect. In two
recent studies, B{\'e}rard et al. defined a class of
permutations for which building a perfect scenario of
reversals sorting a permutation was achieved in
polynomial time and stated as an open question whether
it is possible to decide, given a permutation, if there
exists a minimum scenario of reversals that is perfect.
In this paper, we give a solution to this problem and
prove that this widens the class of permutations
addressed by the aforementioned studies. We implemented
and tested this algorithm on gene order data of
chromosomes from several mammal species and we compared
it to other methods. The algorithm helps to choose
among several possible scenarios of reversals and
indicates that the minimum scenario of reversals is not
always the most plausible.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "common intervals; computational biology; genome
rearrangements; perfect sorting; signed permutations;
sorting by reversals",
}
@Article{Weskamp:2007:MGA,
author = "Nils Weskamp and Eyke Hullermeier and Daniel Kuhn and
Gerhard Klebe",
title = "Multiple Graph Alignment for the Structural Analysis
of Protein Active Sites",
journal = j-TCBB,
volume = "4",
number = "2",
pages = "310--320",
month = apr,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:57:55 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Graphs are frequently used to describe the geometry
and also the physicochemical composition of protein
active sites. Here, the concept of graph alignment as a
novel method for the structural analysis of protein
binding pockets is presented. Using inexact
graph-matching techniques, one is able to identify both
conserved areas and regions of difference among
different binding pockets. Thus, using multiple graph
alignments, it is possible to characterize functional
protein families and to examine differences among
related protein families independent of sequence or
fold homology. Optimized algorithms are described for
the efficient calculation of multiple graph alignments
for the analysis of physicochemical descriptors
representing protein binding pockets. Additionally, it
is shown how the calculated graph alignments can be
analyzed to identify structural features that are
characteristic for a given protein family and also
features that are discriminative among related
families. The methods are applied to a substantial
high-quality subset of the PDB database and their
ability to successfully characterize and classify 10
highly populated functional protein families is shown.
Additionally, two related protein families from the
group of serine proteases are examined and important
structural differences are detected automatically and
efficiently.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "drug design; fuzzy patterns; graph mining; knowledge
discovery in databases; structural pattern discovery",
}
@Article{Gusfield:2007:AEAb,
author = "Dan Gusfield",
title = "{Associate Editor} Appreciation and Welcome",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "321--321",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fujarewicz:2007:ASM,
author = "Krzysztof Fujarewicz and Marek Kimmel and Tomasz
Lipniacki and Andrzej Swierniak",
title = "Adjoint Systems for Models of Cell Signaling Pathways
and their Application to Parameter Fitting",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "322--335",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The paper concerns the problem of fitting mathematical
models of cell signaling pathways. Such models
frequently take the form of sets of nonlinear ordinary
differential equations. While the model is continuous
in time, the performance index used in the fitting
procedure, involves measurements taken at discrete time
moments. Adjoint sensitivity analysis is a tool, which
can be used for finding the gradient of a performance
index in the space of parameters of the model. In the
paper a structural formulation of adjoint sensitivity
analysis called the Generalized Backpropagation Through
Time (GBPTT) is used. The method is especially suited
for hybrid, continuous-discrete time systems. As an
example we use the mathematical model of the NF-kB
regulatory module, which plays a major role in the
innate immune response in animals.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biology and genetics; modeling; ordinary differential
equations; parameter learning",
}
@Article{Wan:2007:CCN,
author = "Xiang Wan and Guohui Lin",
title = "{CISA}: Combined {NMR} Resonance Connectivity
Information Determination and Sequential Assignment",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "336--348",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A nearly complete sequential resonance assignment is a
key factor leading to successful protein structure
determination via NMR spectroscopy. Assuming the
availability of a set of NMR spectral peak lists, most
of the existing assignment algorithms first use the
differences between chemical shift values for common
nuclei across multiple spectra to provide the evidence
that some pairs of peaks should be assigned to
sequentially adjacent amino acid residues in the target
protein. They then use these connectivities as
constraints to produce a sequential assignment. At
various levels of success, these algorithms typically
generate a large number of potential connectivity
constraints, and it grows exponentially as the quality
of spectral data decreases. A key observation used in
our sequential assignment program, CISA, is that
chemical shift residual signature information can be
used to improve the connectivity determination, and
thus to dramatically decrease the number of predicted
connectivity constraints. Fewer connectivity
constraints lead to less ambiguities in the sequential
assignment. Extensive simulation studies on several
large test datasets demonstrated that CISA is efficient
and effective, compared to three most recently proposed
sequential resonance assignment programs RANDOM, PACES,
and MARS.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "NMR sequential resonance assignment; spin system; spin
system assignment; spin system residual signature; spin
system sequential connectivity",
}
@Article{Cameron:2007:CCS,
author = "Michael Cameron and Hugh Williams",
title = "Comparing Compressed Sequences for Faster Nucleotide
{BLAST} Searches",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "349--364",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Molecular biologists, geneticists, and other life
scientists use the BLAST homology search package as
their first step for discovery of information about
unknown or poorly annotated genomic sequences. There
are two main variants of BLAST: BLASTP for searching
protein collections and BLASTN for nucleotide
collections. Surprisingly, BLASTN has had very little
attention; for example, the algorithms it uses do not
follow those described in the 1997 BLAST paper [1] and
no exact description has been published. It is
important that BLASTN is state-of-the-art: Nucleotide
collections such as GenBank dwarf the protein
collections in size, they double in size almost yearly,
and they take many minutes to search on modern general
purpose workstations. This paper proposes significant
improvements to the BLASTN algorithms. Each of our
schemes is based on compressed bytepacked formats that
allow queries and collection sequences to be compared
four bases at a time, permitting very fast query
evaluation using lookup tables and numeric comparisons.
Our most significant innovations are two new, fast
gapped alignment schemes that allow accurate sequence
alignment without decompression of the collection
sequences. Overall, our innovations more than double
the speed of BLASTN with no effect on accuracy and have
been integrated into our new version of BLAST that is
freely available for download from
\path=http://www.fsa-blast.org/=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "BLAST; compression; Four Russians algorithm; homology
search; sequence alignment",
}
@Article{Tang:2007:DTS,
author = "Yuchun Tang and Yan-Qing Zhang and Zhen Huang",
title = "Development of Two-Stage {SVM}-{RFE} Gene Selection
Strategy for Microarray Expression Data Analysis",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "365--381",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Extracting a subset of informative genes from
microarray expression data is a critical data
preparation step in cancer classification and other
biological function analyses. Though many algorithms
have been developed, the Support Vector Machine -
Recursive Feature Elimination (SVM-RFE) algorithm is
one of the best gene feature selection algorithms. It
assumes that a smaller `filter-out' factor in the
SVM-RFE, which results in a smaller number of gene
features eliminated in each recursion, should lead to
extraction of a better gene subset. Because the SVM-RFE
is highly sensitive to the `filter-out' factor, our
simulations have shown that this assumption is not
always correct and that the SVM-RFE is an unstable
algorithm. To select a set of key gene features for
reliable prediction of cancer types or subtypes and
other applications, a new two-stage SVM-RFE algorithm
has been developed. It is designed to effectively
eliminate most of the irrelevant, redundant and noisy
genes while keeping information loss small at the first
stage. A fine selection for the final gene subset is
then performed at the second stage. The two-stage
SVM-RFE overcomes the instability problem of the
SVM-RFE to achieve better algorithm utility. We have
demonstrated that the two-stage SVM-RFE is
significantly more accurate and more reliable than the
SVM-RFE and three correlation-based methods based on
our analysis of three publicly available microarray
expression datasets. Furthermore, the two-stage SVM-RFE
is computationally efficient because its time
complexity is $ O(d * \log {_2d}) $, where $d$ is the
size of the original gene set.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "bioinformatics; cancer classification; feature
selection; gene selection; microarray gene expression
data analysis; recursive feature elimination; support
vector machines",
}
@Article{Ng:2007:NGW,
author = "Lydia Ng and Sayan Pathak and Chihchau Kuan and Chris
Lau and Hong-wei Dong and Andrew Sodt and Chinh Dang
and Brian Avants and Paul Yushkevich and James Gee and
David Haynor and Ed Lein and Allan Jones and Mike
Hawrylycz",
title = "Neuroinformatics for Genome-Wide {$3$-D} Gene
Expression Mapping in the Mouse Brain",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "382--393",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Large scale gene expression studies in the mammalian
brain offer the promise of understanding the topology,
networks and ultimately the function of its complex
anatomy, opening previously unexplored avenues in
neuroscience. High-throughput methods permit
genome-wide searches to discover genes that are
uniquely expressed in brain circuits and regions that
control behavior. Previous gene expression mapping
studies in model organisms have employed situ
hybridization (ISH), a technique that uses labeled
nucleic acid probes to bind to specific mRNA
transcripts in tissue sections. A key requirement for
this effort is the development of fast and robust
algorithms for anatomically mapping and quantifying
gene expression for ISH. We describe a neuroinformatics
pipeline for automatically mapping expression profiles
of ISH data and its use to produce the first genomic
scale 3-D mapping of gene expression in a mammalian
brain. The pipeline is fully automated and adaptable to
other organisms and tissues. Our automated study of
over 20,000 genes indicates that at least 78.8\% are
expressed at some level in the adult C56BL/6J mouse
brain. In addition to providing a platform for genomic
scale search, high-resolution images and visualization
tools for expression analysis are available at the
Allen Brain Atlas web site
(http://www.brain-map.org).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "bioinformatics (genome or protein) databases; data
mining; information visualization; registration;
segmentation",
}
@Article{Nguyen:2007:RRN,
author = "C. Thach Nguyen and Nguyen Bao Nguyen and Wing-Kin
Sung and Louxin Zhang",
title = "Reconstructing Recombination Network from Sequence
Data: The Small Parsimony Problem",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "394--402",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The small parsimony problem is studied for
reconstructing recombination networks from sequence
data. The small parsimony problem is polynomial-time
solvable for phylogenetic trees. However, the problem
is proved NP-hard even for galled recombination
networks. A dynamic programming algorithm is also
developed to solve the small parsimony problem. It
takes $ O(d n2^{3h}) $ time on an input recombination
network over length-$d$ sequences in which there are
$h$ recombination and $ n - h $ tree nodes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "approximability; combination network; dynamic
programming; NP-hardness; parsimony method;
phylogenetic network",
}
@Article{Lones:2007:RMD,
author = "Michael Lones and Andy Tyrrell",
title = "Regulatory Motif Discovery Using a Population
Clustering Evolutionary Algorithm",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "403--414",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper describes a novel evolutionary algorithm
for regulatory motif discovery in DNA promoter
sequences. The algorithm uses data clustering to
logically distribute the evolving population across the
search space. Mating then takes place within local
regions of the population, promoting overall solution
diversity and encouraging discovery of multiple
solutions. Experiments using synthetic data sets have
demonstrated the algorithm's capacity to find position
frequency matrix models of known regulatory motifs in
relatively long promoter sequences. These experiments
have also shown the algorithm's ability to maintain
diversity during search and discover multiple motifs
within a single population. The utility of the
algorithm for discovering motifs in real biological
data is demonstrated by its ability to find meaningful
motifs within muscle-specific regulatory sequences.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "evolutionary computation; motif discovery;
muscle-specific gene expression; population-based data
clustering; transcription factor binding sites",
}
@Article{Yip:2007:SIS,
author = "Andy M. Yip and Michael K. Ng and Edmond H. Wu and
Tony F. Chan",
title = "Strategies for Identifying Statistically Significant
Dense Regions in Microarray Data",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "415--429",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We propose and study the notion of dense regions for
the analysis of categorized gene expression data and
present some searching algorithms for discovering them.
The algorithms can be applied to any categorical data
matrices derived from gene expression level matrices.
We demonstrate that dense regions are simple but useful
and statistically significant patterns that can be used
to (1) identify genes and/or samples of interest and
(2) eliminate genes and/or samples corresponding to
outliers, noise, or abnormalities. Some theoretical
studies on the properties of the dense regions are
presented which allow us to characterize dense regions
into several classes and to derive tailor-made
algorithms for different classes of regions. Moreover,
an empirical simulation study on the distribution of
the size of dense regions is carried out which is then
used to assess the significance of dense regions and to
derive effective pruning methods to speed up the
searching algorithms. Real microarray data sets are
employed to test our methods. Comparisons with six
other well-known clustering algorithms using synthetic
and real data are also conducted which confirm the
superiority of our methods in discovering dense
regions. The DRIFT code and a tutorial are available as
supplemental material, which can be found on the
Computer Society Digital Library at
\path=http://computer.org/tcbb/archives.htm=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "bicluster; categorical data; clustering; coexpressed
genes; dense region; gene expression; microarray",
}
@Article{Liang:2007:BBD,
author = "Kuo-ching Liang and Xiaodong Wang and Dimitris
Anastassiou",
title = "{Bayesian} Basecalling for {DNA} Sequence Analysis
Using Hidden {Markov} Models",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "430--440",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "It has been shown that electropherograms of DNA
sequences can be modeled with hidden Markov models.
Basecalling, the procedure that determines the sequence
of bases from the given eletropherogram, can then be
performed using the Viterbi algorithm. A training step
is required prior to basecalling in order to estimate
the HMM parameters. In this paper, we propose a
Bayesian approach which employs the Markov chain Monte
Carlo (MCMC) method to perform basecalling. Such an
approach not only allows one to naturally encode the
prior biological knowledge into the basecalling
algorithm, it also exploits both the training data and
the basecalling data in estimating the HMM parameters,
leading to more accurate estimates. Using the recently
sequenced genome of the organism Legionella pneumophila
we show that the MCMC basecaller outperforms the
state-of-the-art basecalling algorithm in terms of
total errors while requiring much less training than
other proposed statistical basecallers.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "basecalling; DNA sequencing; electropherogram; hidden
Markov model (HMM); Markov chain Monte Carlo (MCMC)",
}
@Article{Thireou:2007:BLS,
author = "Trias Thireou and Martin Reczko",
title = "Bidirectional Long Short-Term Memory Networks for
Predicting the Subcellular Localization of Eukaryotic
Proteins",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "441--446",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "An algorithm called Bidirectional Long Short-Term
Memory Networks (BLSTM) for processing sequential data
is introduced. This supervised learning method trains a
special recurrent neural network to use very long
ranged symmetric sequence context using a combination
of nonlinear processing elements and linear feedback
loops for storing long-range context. The algorithm is
applied to the sequence-based prediction of protein
localization and predicts 93.3\% novel non-plant
proteins and 88.4\% novel plant proteins correctly,
which is an improvement over feedforward and standard
recurrent networks solving the same problem. The BLSTM
system is available as a web-service
(http://www.stepc.gr/~synaptic/blstm.html).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biological sequence analysis; long short-term memory;
protein subcellular localization prediction; recurrent
neural networks",
}
@Article{Korodi:2007:CAN,
author = "Gergely Korodi and Ioan Tabus",
title = "Compression of Annotated Nucleotide Sequences",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "447--457",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This article introduces an algorithm for the lossless
compression of DNA files, which contain annotation text
besides the nucleotide sequence. First a grammar is
specifically designed to capture the regularities of
the annotation text. A revertible transformation uses
the grammar rules in order to equivalently represent
the original file as a collection of parsed segments
and a sequence of decisions made by the grammar parser.
This decomposition enables the efficient use of
state-of-the-art encoders for processing the parsed
segments. The output size of the decision-making
process of the grammar is optimized by extending the
states to account for high-order Markovian
dependencies. The practical implementation of the
algorithm achieves a significant improvement when
compared to the general-purpose methods currently used
for DNA files.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "4 [Data]: Coding and Information Theory | Data
compaction and compression; Annotation; Compression;
F.4 [Theory of Computation]: Mathematical Logic and
Formal Languages | Formal languages; Formal Grammars;
G.3 [Mathematics of Computing]: Probability and
Statistics | Markov processes; J.3 [Computer
Applications]: Life and Medical Sciences | Biology and
genetics; nucleotide sequences",
}
@Article{Bordewich:2007:CHN,
author = "Magnus Bordewich and Charles Semple",
title = "Computing the Hybridization Number of Two Phylogenetic
Trees Is Fixed-Parameter Tractable",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "458--466",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Reticulation processes in evolution mean that the
ancestral history of certain groups of present-day
species is non-tree-like. These processes include
hybridization, lateral gene transfer, and
recombination. Despite the existence of reticulation,
such events are relatively rare and so a fundamental
problem for biologists is the following: given a
collection of rooted binary phylogenetic trees on sets
of species that correctly represent the tree-like
evolution of different parts of their genomes, what is
the smallest number of `reticulation' vertices in any
network that explains the evolution of the species
under consideration. It has been previously shown that
this problem is NP-hard even when the collection
consists of only two rooted binary phylogenetic trees.
However, in this paper, we show that the problem is
fixed-parameter tractable in the two-tree instance,
when parameterized by this smallest number of
reticulation vertices.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "agreement forest; hybridization network; reticulate
evolution; rooted phylogenetic tree; subtree prune and
regraft",
}
@Article{Huang:2007:EGS,
author = "D. Huang and Tommy Chow",
title = "Effective Gene Selection Method With Small Sample Sets
Using Gradient-Based and Point Injection Techniques",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "467--475",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Microarray gene expression data usually consist of a
large amount of genes. Among these genes, only a small
fraction is informative for performing cancer
diagnostic test. This paper focuses on effective
identification of informative genes. We analyze gene
selection models from the perspective of optimization
theory. As a result, a new strategy is designed to
modify conventional search engines. Also, as
overfitting is likely to occur in microarray data
because of their small sample set, a point injection
technique is developed to address the problem of
overfitting. The proposed strategies have been
evaluated on three kinds of cancer diagnosis. Our
results show that the proposed strategies can improve
the performance of gene selection substantially. The
experimental results also indicate that the proposed
methods are very robust under all the investigated
cases.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "gene selection; gradient based learning; optimization
theory; point injection",
}
@Article{Hecht:2007:HTL,
author = "David Hecht and Gary Fogel",
title = "High-Throughput Ligand Screening via Preclustering and
Evolved Neural Networks",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "476--484",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The pathway for novel lead drug discovery has many
major deficiencies, the most significant of which is
the immense size of small molecule diversity space.
Methods that increase the search efficiency and/or
reduce the size of the search space, increase the rate
at which useful lead compounds are identified.
Artificial neural networks optimized via evolutionary
computation provide a cost and time-effective solution
to this problem. Here, we present results that suggest
preclustering of small molecules prior to neural
network optimization is useful for generating models of
quantitative structure-activity relationships for a set
of HIV inhibitors. Using these methods, it is possible
to prescreen compounds to separate active from inactive
compounds or even actives and mildly active compounds
from inactive compounds with high predictive accuracy
while simultaneously reducing the feature space. It is
also possible to identify `human interpretable'
features from the best models that can be used for
proposal and synthesis of new compounds in order to
optimize potency and specificity.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "artificial neural networks; computational
intelligence; evolutionary computation; medicine and
science",
}
@Article{Zhang:2007:MCU,
author = "Runxuan Zhang and Guang-Bin Huang and N. Sundararajan
and P. Saratchandran",
title = "Multicategory Classification Using An Extreme Learning
Machine for Microarray Gene Expression Cancer
Diagnosis",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "485--495",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, the recently developed Extreme Learning
Machine (ELM) is used for direct multicategory
classification problems in the cancer diagnosis area.
ELM avoids problems like local minima, improper
learning rate and overfitting commonly faced by
iterative learning methods and completes the training
very fast. We have evaluated the multi-category
classification performance of ELM on three benchmark
microarray datasets for cancer diagnosis, namely, the
GCM dataset, the Lung dataset and the Lymphoma dataset.
The results indicate that ELM produces comparable or
better classification accuracies with reduced training
time and implementation complexity compared to
artificial neural networks methods like conventional
back-propagation ANN, Linder's SANN, and Support Vector
Machine methods like SVM-OVO and Ramaswamy's SVM-OVA.
ELM also achieves better accuracies for classification
of individual categories.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "extreme learning machine; gene expression; microarray;
multi-category classification; SVM",
}
@Article{Zhang:2007:SSS,
author = "Louxin Zhang",
title = "Superiority of Spaced Seeds for Homology Search",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "496--505",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In homology search, good spaced seeds have higher
sensitivity for the same cost (weight). However,
elucidating the mechanism that confers power to spaced
seeds and characterizing optimal spaced seeds still
remain unsolved. This paper investigates these two
important open questions by formally analyzing the
average number of non-overlapping hits and the hit
probability of a spaced seed in the Bernoulli sequence
model. We prove that when the length of a non-uniformly
spaced seed is bounded above by an exponential function
of the seed weight, the seed outperforms strictly the
traditional consecutive seed of the same weight in both
(i) the average number of non-overlapping hits and (ii)
the asymptotic hit probability. This clearly answers
the first problem mentioned above in the Bernoulli
sequence model. The theoretical study in this paper
also gives a new solution to finding long optimal
seeds.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "homology search; pattern matching; renewal theory; run
statistics; sequence alignment; spaced seeds",
}
@Article{Matsen:2007:OCT,
author = "Frederick Matsen",
title = "Optimization Over a Class of Tree Shape Statistics",
journal = j-TCBB,
volume = "4",
number = "3",
pages = "506--512",
month = jul,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:24 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Tree shape statistics quantify some aspect of the
shape of a phylogenetic tree. They are commonly used to
compare reconstructed trees to evolutionary models and
to find evidence of tree reconstruction bias.
Historically, to find a useful tree shape statistic,
formulas have been invented by hand and then evaluated
for utility. This article presents the first method
which is capable of optimizing over a class of tree
shape statistics, called Binary Recursive Tree Shape
Statistics (BRTSS). After defining the BRTSS class, a
set of algebraic expressions is defined which can be
used in the recursions. The tree shape statistics
definable using these expressions in the BRTSS is very
general, and includes many of the statistics with which
phylogenetic researchers are already familiar. We then
present a practical genetic algorithm which is capable
of performing optimization over BRTSS given any
objective function. The chapter concludes with a
successful application of the methods to find a new
statistic which indicates a significant difference
between two distributions on trees which were
previously postulated to have similar properties.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biology and genetics; evolutionary computing and
genetic algorithms",
}
@Article{Mandoiu:2007:GEI,
author = "Ion I. M{\~a}ndoiu and Yi Pan and Alexander
Zelikovsky",
title = "{Guest Editors}' Introduction to the {Special Section
on Bioinformatics Research and Applications}",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "513--514",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zheng:2007:RNA,
author = "Chunfang Zheng and Qian Zhu and David Sankoff",
title = "Removing Noise and Ambiguities from Comparative Maps
in Rearrangement Analysis",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "515--522",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Comparison of genomic maps is hampered by errors and
ambiguities introduced by mapping technology,
incorrectly resolved paralogy, small samples of markers
and extensive genome rearrangement. We design an
analysis to remove or resolve most of these problems
and to extract corrected data where markers occur in
consecutive strips in both genomes. To do this we
introduce the notion of pre-strip, an efficient way of
generating these, and a compatibility analysis
culminating in a Maximum Weighted Clique (MWC) search.
The output can be directly analyzed with genome
rearrangement algorithms, allowing the restoration of
some of the data not incorporated into the clique
solution. We investigate the trade-off between criteria
for discarding excessive pre-strips to make MWC
feasible, in terms of retaining as many markers as
possible in the solution and producing an economical
rearrangement analysis. We explore these questions
through simulation and through comparison of the rice
and sorghum genomes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "genome rearrangements; Maximum Weight Clique; rice;
sorghum; synteny blocks",
}
@Article{Blin:2007:CGD,
author = "Guillaume Blin and Cedric Chauve and Guillaume Fertin
and Romeo Rizzi and Stephane Vialette",
title = "Comparing Genomes with Duplications: a Computational
Complexity Point of View",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "523--534",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, we are interested in the computational
complexity of computing (dis)similarity measures
between two genomes when they contain duplicated genes
or genomic markers, a problem that happens frequently
when comparing whole nuclear genomes. Recently, several
methods ([1], [2]) have been proposed that are based on
two steps to compute a given (dis)similarity measure
$M$ between two genomes $ G_1 $ and $ G_2 $: first, one
establishes a one-to-one correspondence between genes
of $ G_1 $ and genes of $ G_2 $; second, once this
correspondence is established, it defines explicitly a
permutation and it is then possible to quantify their
similarity using classical measures defined for
permutations, like the number of breakpoints. Hence
these methods rely on two elements: a way to establish
a one-to-one correspondence between genes of a pair of
genomes, and a (dis)similarity measure for
permutations. The problem is then, given a
(dis)similarity measure for permutations, to compute a
correspondence that defines an optimal permutation for
this measure. We are interested here in two models to
compute a one-to-one correspondence: the exemplar
model, where all but one copy are deleted in both
genomes for each gene family, and the matching model,
that computes a maximal correspondence for each gene
family. We show that for these two models, and for
three (dis)similarity measures on permutations, namely
the number of common intervals, the maximum adjacency
disruption (MAD) number and the summed adjacency
disruption (SAD) number, the problem of computing an
optimal correspondence is NP-complete, and even APXhard
for the MAD number and SAD number.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "common intervals; comparative genomics; computational
complexity; maximum adjacency disruption number; summed
adjacency disruption number",
}
@Article{Bonizzoni:2007:ELC,
author = "Paola Bonizzoni and Gianluca Della Vedova and Riccardo
Dondi and Guillaume Fertin and Raffaella Rizzi and
Stephane Vialette",
title = "Exemplar Longest Common Subsequence",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "535--543",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, we investigate the computational and
approximation complexity of the Exemplar Longest Common
Subsequence of a set of sequences (ELCS problem), a
generalization of the Longest Common Subsequence
problem, where the input sequences are over the union
of two disjoint sets of symbols, a set of mandatory
symbols and a set of optional symbols. We show that
different versions of the problem are APX-hard even for
instances with two sequences. Moreover, we show that
the related problem of determining the existence of a
feasible solution of the Exemplar Longest Common
Subsequence of two sequences is NP-hard. On the
positive side, we first present an efficient algorithm
for the ELCS problem over instances of two sequences
where each mandatory symbol can appear in total at most
three times in the sequences. Furthermore, we present
two fixed-parameter algorithms for the ELCS problem
over instances of two sequences where the parameter is
the number of mandatory symbols.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "algorithm design and analysis; analysis of algorithms
and problem complexity; combinatorial algorithms;
comparative genomics; longest common subsequence",
}
@Article{Davila:2007:FPA,
author = "Jaime Davila and Sudha Balla and Sanguthevar
Rajasekaran",
title = "Fast and Practical Algorithms for Planted $ (l, d) $
Motif Search",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "544--552",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We consider the planted $ (l, d) $ motif search
problem, which consists of finding a substring of
length $l$ that occurs in a set of input sequences $ \{
s_1, \ldots {}, s_n \} $ with up to $d$ errors, a
problem that arises from the need to find transcription
factor-binding sites in genomic information. We propose
a sequence of practical algorithms, which start based
on the ideas considered in PMS1. These algorithms are
exact, have little space requirements, and are able to
tackle challenging instances with bigger $d$, taking
less time in the instances reported solved by exact
algorithms. In particular, one of the proposed
algorithms, PMSprune, is able to solve the challenging
instances, such as (17, 6) and (19, 7), which were not
previously reported as solved in the literature.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "branch and bound algorithms; challenging instances;
exact algorithms; planted motif search problem",
}
@Article{Schneider:2007:SDM,
author = "Adrian Schneider and Gaston Gonnet and Gina
Cannarozzi",
title = "{SynPAM---A} Distance Measure Based on Synonymous
Codon Substitutions",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "553--560",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Measuring evolutionary distances between DNA or
protein sequences forms the basis of many applications
in computational biology and evolutionary studies. Of
particular interest are distances based on synonymous
substitutions, since these substitutions are considered
to be under very little selection pressure and
therefore assumed to accumulate in an almost clock-like
manner. SynPAM, the method presented here, allows the
estimation of distances between coding DNA sequences
based on synonymous codon substitutions. The problem of
estimating an accurate distance from the observed
substitution pattern is solved by maximum-likelihood
with empirical codon substitution matrices employed for
the underlying Markov model. Comparisons with
established measures of synonymous distance indicate
that SynPAM has less variance and yields useful results
over a longer time range.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "dS; evolutionary distance; molecular evolution;
synonymous substitutions; SynPAM",
}
@Article{Sridhar:2007:AEN,
author = "Srinath Sridhar and Kedar Dhamdhere and Guy Blelloch
and Eran Halperin and R. Ravi and Russell Schwartz",
title = "Algorithms for Efficient Near-Perfect Phylogenetic
Tree Reconstruction in Theory and Practice",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "561--571",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We consider the problem of reconstructing near-perfect
phylogenetic trees using binary character states
(referred to as BNPP). A perfect phylogeny assumes that
every character mutates at most once in the
evolutionary tree, yielding an algorithm for binary
character states that is computationally efficient but
not robust to imperfections in real data. A
near-perfect phylogeny relaxes the perfect phylogeny
assumption by allowing at most a constant number of
additional mutations. We develop two algorithms for
constructing optimal near-perfect phylogenies and
provide empirical evidence of their performance. The
first simple algorithm is fixed parameter tractable
when the number of additional mutations and the number
of characters that share four gametes with some other
character are constants. The second, more involved
algorithm for the problem is fixed parameter tractable
when only the number of additional mutations is fixed.
We have implemented both algorithms and shown them to
be extremely efficient in practice on biologically
significant data sets. This work proves the BNPP
problem fixed parameter tractable and provides the
first practical phylogenetic tree reconstruction
algorithms that find guaranteed optimal solutions while
being easily implemented and computationally feasible
for data sets of biologically meaningful size and
complexity.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biology and genetics; computations on discrete
structures; trees",
}
@Article{Chen:2007:CBR,
author = "Jinmiao Chen and Narendra Chaudhari",
title = "Cascaded Bidirectional Recurrent Neural Networks for
Protein Secondary Structure Prediction",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "572--582",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein secondary structure (PSS) prediction is an
important topic in bioinformatics. Our study on a large
set of non-homologous proteins shows that long-range
interactions commonly exist and negatively affect PSS
prediction. Besides, we also reveal strong correlations
between secondary structure (SS) elements. In order to
take into account the long-range interactions and SS-SS
correlations, we propose a novel prediction system
based on cascaded bidirectional recurrent neural
network (BRNN). We compare the cascaded BRNN against
another two BRNN architectures, namely the original
BRNN architecture used for speech recognition as well
as Pollastri's BRNN that was proposed for PSS
prediction. Our cascaded BRNN achieves an overall three
state accuracy Q3 of 74.38\%, and reaches a high
Segment OVerlap (SOV) of 66.0455. It outperforms the
original BRNN and Pollastri's BRNN in both Q3 and SOV.
Specifically, it improves the SOV score by 4-6\%.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xiong:2007:DDK,
author = "Huilin Xiong and Ya Zhang and Xue-Wen Chen",
title = "Data-Dependent Kernel Machines for Microarray Data
Classification",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "583--595",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "One important application of gene expression analysis
is to classify tissue samples according to their gene
expression levels. Gene expression data are typically
characterized by high dimensionality and small sample
size, which makes the classification task quite
challenging. In this paper, we present a data-dependent
kernel for microarray data classification. This kernel
function is engineered so that the class separability
of the training data is maximized. A
bootstrapping-based resampling scheme is introduced to
reduce the possible training bias. The effectiveness of
this adaptive kernel for microarray data classification
is illustrated with a k-Nearest Neighbor (KNN)
classifier. Our experimental study shows that the
data-dependent kernel leads to a significant
improvement in the accuracy of KNN classifiers.
Furthermore, this kernel-based KNN scheme has been
demonstrated to be competitive to, if not better than,
more sophisticated classifiers such as Support Vector
Machines (SVMs) and the Uncorrelated Linear
Discriminant Analysis (ULDA) for classifying gene
expression data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "bootstrapping resampling; cancer classification;
kernel machines; kernel optimization; microarray data
analysis",
}
@Article{Michal:2007:FCM,
author = "Shahar Michal and Tor Ivry and Omer Cohen and Moshe
Sipper and Danny Barash",
title = "Finding a Common Motif of {RNA} Sequences Using
Genetic Programming: The {GeRNAMo} System",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "596--610",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We focus on finding a consensus motif of a set of
homologous or functionally related RNA molecules.
Recent approaches to this problem have been limited to
simple motifs, require sequence alignment, and make
prior assumptions concerning the data set. We use
genetic programming to predict RNA consensus motifs
based solely on the data set. Our system -- dubbed
GeRNAMo (Genetic programming of RNA Motifs) -- predicts
the most common motifs without sequence alignment and
is capable of dealing with any motif size. Our program
only requires the maximum number of stems in the motif,
and if prior knowledge is available the user can
specify other attributes of the motif (e.g., the range
of the motif's minimum and maximum sizes), thereby
increasing both sensitivity and speed. We describe
several experiments using either ferritin iron response
element (IRE); signal recognition particle (SRP); or
microRNA sequences, showing that the most common motif
is found repeatedly, and that our system offers
substantial advantages over previous methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{McIntosh:2007:HCR,
author = "Tara McIntosh and Sanjay Chawla",
title = "High Confidence Rule Mining for Microarray Analysis",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "611--623",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present an association rule mining method for
mining high confidence rules, which describe
interesting gene relationships from microarray
datasets. Microarray datasets typically contain an
order of magnitude more genes than experiments,
rendering many data mining methods impractical as they
are optimised for sparse datasets. A new family of
row-enumeration rule mining algorithms have emerged to
facilitate mining in dense datasets. These algorithms
rely on pruning infrequent relationships to reduce the
search space by using the support measure. This major
shortcoming results in the pruning of many potentially
interesting rules with low support but high confidence.
We propose a new row-enumeration rule mining method,
MaxConf, to mine high confidence rules from microarray
data. MaxConf is a support-free algorithm which
directly uses the confidence measure to effectively
prune the search space. Experiments on three microarray
datasets show that MaxConf outperforms support-based
rule mining with respect to scalability and rule
extraction. Furthermore, detailed biological analyses
demonstrate the effectiveness of our approach -- the
rules discovered by MaxConf are substantially more
interesting and meaningful compared with support-based
methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "association rules; data mining; high confidence rule
mining; microarray analysis",
}
@Article{Ponzoni:2007:IAR,
author = "Ignacio Ponzoni and Francisco Azuaje and Juan Augusto
and David Glass",
title = "Inferring Adaptive Regulation Thresholds and
Association Rules from Gene Expression Data through
Combinatorial Optimization Learning",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "624--634",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "There is a need to design computational methods to
support the prediction of gene regulatory networks.
Such models should offer both biologically-meaningful
and computationally-accurate predictions, which in
combination with other techniques may improve
large-scale, integrative studies. This paper presents a
new machine learning method for the prediction of
putative regulatory associations from expression data,
which exhibit properties never or only partially
addressed by other techniques recently published. The
method was tested on a Saccharomyces cerevisiae gene
expression dataset. The results were statistically
validated and compared with the relationships inferred
by two machine learning approaches to gene regulatory
network prediction. Furthermore, the resulting
predictions were assessed using domain knowledge. The
proposed algorithm may be able to accurately predict
relevant biological associations between genes. One of
the most relevant features of this new method is the
prediction of adaptive regulation thresholds for the
discretization of gene expression values, which is
required prior to the rule association learning
process. Moreover, an important advantage consists of
its low computational cost to infer association rules.
The proposed system may significantly support
exploratory, large-scale studies of automated
identification of potentially-relevant gene expression
associations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "combinatorial optimization; decision trees; gene
expression data; genetic regulatory networks;
machine-learning",
}
@Article{Noman:2007:IGR,
author = "Nasimul Noman and Hitoshi Iba",
title = "Inferring Gene Regulatory Networks using Differential
Evolution with Local Search Heuristics",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "634--647",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present a memetic algorithm for evolving the
structure of biomolecular interactions and inferring
the effective kinetic parameters from the time series
data of gene expression using the decoupled system
formalism. We propose an Information Criteria based
fitness evaluation for gene network model selection
instead of the conventional Mean Squared Error (MSE)
based fitness evaluation. A hill-climbing local-search
method has been incorporated in our evolutionary
algorithm for efficiently attaining the skeletal
architecture which is most frequently observed in
biological networks. The suitability of the method is
tested in gene circuit reconstruction experiments,
varying the network dimension and/or characteristics,
the amount of gene expression data used for inference
and the noise level present in expression profiles. The
reconstruction method inferred the network topology and
the regulatory parameters with high accuracy.
Nevertheless, the performance is limited to the amount
of expression data used and the noise level present in
the data. The proposed fitness function has been found
more suitable for identifying correct network topology
and for estimating the accurate parameter values
compared to the existing ones. Finally, we applied the
methodology for analyzing the cell-cycle gene
expression data of budding yeast and reconstructed the
network of some key regulators.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biology and genetics; gene regulatory system; global
optimization; inverse problems; medicine and science;
memetic algorithm; microarray data; transcriptional
regulation",
}
@Article{Ho:2007:ITS,
author = "Shinn-Ying Ho and Chih-Hung Hsieh and Fu-Chieh Yu and
Hui-Ling Huang",
title = "An Intelligent Two-Stage Evolutionary Algorithm for
Dynamic Pathway Identification From Gene Expression
Profiles",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "648--704",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "From gene expression profiles, it is desirable to
rebuild cellular dynamic regulation networks to
discover more delicate and substantial functions in
molecular biology, biochemistry, bioengineering and
pharmaceutics. S-system model is suitable to
characterize biochemical network systems and capable to
analyze the regulatory system dynamics. However,
inference of an S-system model of N-gene genetic
networks has 2N(N+1) parameters in a set of non-linear
differential equations to be optimized. This paper
proposes an intelligent two-stage evolutionary
algorithm (iTEA) to efficiently infer the S-system
models of genetic networks from time-series data of
gene expression. To cope with curse of dimensionality,
the proposed algorithm consists of two stages where
each uses a divide-and-conquer strategy. The
optimization problem is first decomposed into $N$
subproblems having 2(N+1) parameters each. At the first
stage, each subproblem is solved using a novel
intelligent genetic algorithm (IGA) with intelligent
crossover based on orthogonal experimental design
(OED). At the second stage, the obtained $N$ solutions
to the $N$ subproblems are combined and refined using
an OED-based simulated annealing algorithm for handling
noisy gene expression profiles. The effectiveness of
iTEA is evaluated using simulated expression patterns
with and without noise running on a single-processor
PC. It is shown that (1) IGA is efficient enough to
solve subproblems; (2) IGA is significantly superior to
the existing method SPXGA; and (3) iTEA performs well
in inferring S-system models for dynamic pathway
identification.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "divide-and-conquer; evolutionary algorithm; genetic
network; orthogonal experimental design; pathway
identification; S-system model",
}
@Article{Bereg:2007:PNB,
author = "Sergey Bereg and Yuanyi Zhang",
title = "Phylogenetic Networks Based on the Molecular Clock
Hypothesis",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "661--667",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A classical result in phylogenetic trees is that a
binary phylogenetic tree adhering to the molecular
clock hypothesis exists if and only if the matrix of
distances between taxa is ultrametric. The ultrametric
condition is very restrictive. In this paper we study
phylogenetic networks that can be constructed assuming
the molecular clock hypothesis. We characterize
distance matrices that admit such networks for 3 and 4
taxa. We also design two algorithms for constructing
networks optimizing the least-squares fit.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "least-squares fit; molecular clock hypothesis;
Phylogenetic Networks",
}
@Article{Blazewicz:2007:SPD,
author = "Jacek Blazewicz and Edmund Burke and Marta Kasprzak
and Alexandr Kovalev and Mikhail Kovalyov",
title = "Simplified Partial Digest Problem: Enumerative and
Dynamic Programming Algorithms",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "668--680",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We study the Simplified Partial Digest Problem (SPDP),
which is a mathematical model for a new simplified
partial digest method of genome mapping. This method is
easy for laboratory implementation and robust with
respect to the experimental errors. SPDP is NP-hard in
the strong sense. We present an $ O(n2^n) $ time
enumerative algorithm and an $ O(n^{2q}) $ time dynamic
programming algorithm for the error-free SPDP, where
$n$ is the number of restriction sites and $q$ is the
number of distinct intersite distances. We also give
examples of the problem, in which there are $ 2^{\frac
{n + 23} - 1} $ non-congruent solutions. These examples
partially answer a question recently posed in the
literature about the number of solutions of SPDP. We
adapt our enumerative algorithm for handling SPDP with
imprecise input data. Finally, we describe and discuss
the results of the computer experiments with our
algorithms.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "algorithm design and analysis; dynamic programming;
genome mapping; imprecise information; restriction site
analysis",
}
@Article{Xu:2007:IGR,
author = "Rui Xu and Donald {Wunsch II} and Ronald Frank",
title = "Inference of Genetic Regulatory Networks with
Recurrent Neural Network Models Using Particle Swarm
Optimization",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "681--692",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genetic regulatory network inference is critically
important for revealing fundamental cellular processes,
investigating gene functions, and understanding their
relations. The availability of time series gene
expression data makes it possible to investigate the
gene activities of whole genomes, rather than those of
only a pair of genes or among several genes. However,
current computational methods do not sufficiently
consider the temporal behavior of this type of data and
lack the capability to capture the complex nonlinear
system dynamics. We propose a recurrent neural network
(RNN) and particle swarm optimization (PSO) approach to
infer genetic regulatory networks from time series gene
expression data. Under this framework, gene interaction
is explained through a connection weight matrix. Based
on the fact that the measured time points are limited
and the assumption that the genetic networks are
usually sparsely connected, we present a PSO-based
search algorithm to unveil potential genetic network
constructions that fit well with the time series data
and explore possible gene interactions. Furthermore,
PSO is used to train the RNN and determine the network
parameters. Our approach has been applied to both
synthetic and real data sets. The results demonstrate
that the RNN\slash PSO can provide meaningful insights
in understanding the nonlinear dynamics of the gene
expression time series and revealing potential
regulatory interactions between genes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "genetic regulatory networks; particle swarm
optimization; recurrent neural networks; time series
gene expression data",
}
@Article{Agius:2007:TSA,
author = "Phaedra Agius and Barry Kreiswirth and Steve Naidich
and Kristin Bennett",
title = "Typing \bioname{Staphylococcus aureus} Using the spa
Gene and Novel Distance Measures",
journal = j-TCBB,
volume = "4",
number = "4",
pages = "693--704",
month = oct,
year = "2007",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:58:47 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We developed an approach for identifying groups or
families of Staphylococcus aureus bacteria based on
genotype data. With the emergence of drug resistant
strains, \bioname{S. aureus} represents a significant human
health threat. Identifying the family types efficiently
and quickly is crucial in community settings. Here, we
develop a hybrid sequence algorithm approach to type
this bacterium using only its spa gene. Two of the
sequence algorithms we used are well established, while
the third, the Best Common Gap-Weighted Sequence
(BCGS), is novel. We combined the sequence algorithms
with a weighted match/mismatch algorithm for the spa
sequence ends. Normalized similarity scores and
distances between the sequences were derived and used
within unsupervised clustering methods. The resulting
spa groupings correlated strongly with the groups
defined by the well-established Multi locus sequence
typing (MLST) method. Spa typing is preferable to MLST
typing which types seven genes instead of just one.
Furthermore, our spa clustering methods can be
fine-tuned to be more discriminative than MLST,
identifying new strains that the MLST method may not.
Finally, we performed a multidimensional scaling of our
distance matrices to visualize the relationship between
isolates. The proposed methodology provides a promising
new approach to molecular epidemiology.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "clustering; genotyping; molecular epidemiology;
sequence algorithms; staphylococcus aureus",
}
@Article{Congdon:2008:EIC,
author = "Clare Bates Congdon and Joseph C. Aman and Gerardo M.
Nava and H. Rex Gaskins and Carolyn J. Mattingly",
title = "An Evaluation of Information Content as a Metric for
the Inference of Putative Conserved Noncoding Regions
in {DNA} Sequences Using a Genetic Algorithms
Approach",
journal = j-TCBB,
volume = "5",
number = "1",
pages = "1--14",
month = jan,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In previous work, we presented GAMI [1], an approach
to motif inference that uses a genetic algorithms
search. GAMI is designed specifically to find putative
conserved regulatory motifs in noncoding regions of
divergent species, and is designed to allow for
analysis of long nucleotide sequences. In this work, we
compare GAMI's performance when run with its original
fitness function (a simple count of the number of
matches) and when run with information content, as well
as several variations on these metrics. Results
indicate that information content does not identify
highly conserved regions, and thus is not the
appropriate metric for this task, while variations on
information content as well as the original metric
succeed in identifying putative conserved regions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biology and genetics; evolutionary computing and
genetic algorithms",
}
@Article{Boscolo:2008:ITE,
author = "Riccardo Boscolo and James C. Liao and Vwani P.
Roychowdhury",
title = "An Information Theoretic Exploratory Method for
Learning Patterns of Conditional Gene Coexpression from
Microarray Data",
journal = j-TCBB,
volume = "5",
number = "1",
pages = "15--24",
month = jan,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this article, we introduce an exploratory framework
for learning patterns of conditional co-expression in
gene expression data. The main idea behind the proposed
approach consists of estimating how the information
content shared by a set of $M$ nodes in a network
(where each node is associated to an expression
profile) varies upon conditioning on a set of L
conditioning variables (in the simplest case
represented by a separate set of expression profiles).
The method is non-parametric and it is based on the
concept of statistical co-information, which, unlike
conventional correlation based techniques, is not
restricted in scope to linear conditional dependency
patterns. Moreover, such conditional co-expression
relationships can potentially indicate regulatory
interactions that do not manifest themselves when only
pair-wise relationships are considered. A moment based
approximation of the co-information measure is derived
that efficiently gets around the problem of estimating
high-dimensional multi-variate probability density
functions from the data, a task usually not viable due
to the intrinsic sample size limitations that
characterize expression level measurements. By applying
the proposed exploratory method, we analyzed a whole
genome microarray assay of the eukaryote Saccharomices
cerevisiae and were able to learn statistically
significant patterns of conditional co-expression. A
selection of such interactions that carry a meaningful
biological interpretation are discussed.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "co-information; entropy; gene expression data;
information theory; statistical analysis",
}
@Article{Wiese:2008:REA,
author = "Kay C. Wiese and Alain A. Deschenes and Andrew G.
Hendriks",
title = "{RnaPredict---An} Evolutionary Algorithm for {RNA}
Secondary Structure Prediction",
journal = j-TCBB,
volume = "5",
number = "1",
pages = "25--41",
month = jan,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper presents two in-depth studies on
RnaPredict, an evolutionary algorithm for RNA secondary
structure prediction. The first study is an analysis of
the performance of two thermodynamic models, INN and
INN-HB. The correlation between the free energy of
predicted structures and the sensitivity is analyzed
for 19 RNA sequences. Although some variance is shown,
there is a clear trend between a lower free energy and
an increase in true positive base pairs. With
increasing sequence length, this correlation generally
decreases. In the second experiment, the accuracy of
the predicted structures for these 19 sequences are
compared against the accuracy of the structures
generated by the mfold dynamic programming algorithm
(DPA) and also to known structures. RnaPredict is shown
to outperform the minimum free energy structures
produced by mfold and has comparable performance when
compared to sub-optimal structures produced by mfold.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "evolutionary computation; RNA secondary structure
prediction; RnaPredict",
}
@Article{Rother:2008:SCP,
author = "Diego Rother and Guillermo Sapiro and Vijay Pande",
title = "Statistical Characterization of Protein Ensembles",
journal = j-TCBB,
volume = "5",
number = "1",
pages = "42--55",
month = jan,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "When accounting for structural fluctuations or
measurement errors, a single rigid structure may not be
sufficient to represent a protein. One approach to
solve this problem is to represent the possible
conformations as a discrete set of observed
conformations, an ensemble. In this work, we follow a
different richer approach, and introduce a framework
for estimating probability density functions in very
high dimensions, and then apply it to represent
ensembles of folded proteins. This proposed approach
combines techniques such as kernel density estimation,
maximum likelihood, cross-validation, and
bootstrapping. We present the underlying theoretical
and computational framework and apply it to artificial
data and protein ensembles obtained from molecular
dynamics simulations. We compare the results with those
obtained experimentally, illustrating the potential and
advantages of this representation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Bayesian networks; bootstrapping; cross-validation;
density estimation; graphical models; maximum
likelihood; protein ensembles",
}
@Article{Cui:2008:AAU,
author = "Yun Cui and Lusheng Wang and Daming Zhu and Xiaowen
Liu",
title = "A $ (1.5 + {\epsilon }) $-Approximation Algorithm for
Unsigned Translocation Distance",
journal = j-TCBB,
volume = "5",
number = "1",
pages = "56--66",
month = jan,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genome rearrangement is an important area in
computational biology and bioinformatics. The
translocation operation is one of the popular
operations for genome rearrangement. It was proved that
computing the unsigned translocation distance is
NP-hard. In this paper, we present a $ (1.5 + \epsilon)
$-approximation algorithm for computing unsigned
translocation distance which improves upon the best
known 1.75-ratio. The running time of our algorithm is
$ O(n^2 + (4 / \epsilon)^1.5 \surd \log (4 / \epsilon)2
4^\epsilon) $, where $n$ is the total number of genes
in the genome.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "and approximation algorithms; genome rearrangement;
unsigned translocation",
}
@Article{Tan:2008:NBP,
author = "Tuan Zea Tan and Geok See Ng and Chai Quek",
title = "A Novel Biologically and Psychologically Inspired
Fuzzy Decision Support System: Hierarchical
Complementary Learning",
journal = j-TCBB,
volume = "5",
number = "1",
pages = "67--79",
month = jan,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A computational intelligent system that models the
human cognitive abilities may promise significant
performance in problem learning because human is
effective in learning and problem solving. Functionally
modeling the human cognitive abilities not only avoids
the details of the underlying neural mechanisms
performing the tasks, but also reduces the complexity
of the system. The complementary learning mechanism is
responsible for human pattern recognition, i.e. human
attends to positive and negative samples when making
decision. Furthermore, human concept learning is
organized in a hierarchical fashion. Such hierarchical
organization allows the divide-and-conquer approach to
the problem. Thus, integrating the functional models of
hierarchical organization and complementary learning
can potentially improve the performance in pattern
recognition. Hierarchical complementary learning
exhibits many of the desirable features of pattern
recognition. It is further supported by the
experimental results that verify the rationale of the
integration and that the hierarchical complementary
learning system is a promising pattern recognition
tool.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "cognitive learning; complementary learning; decision
support; fuzzy neural network; hierarchical model",
}
@Article{Ciocchetta:2008:ATS,
author = "Federica Ciocchetta and Corrado Priami and Paola
Quaglia",
title = "An Automatic Translation of {SBML} into Beta-Binders",
journal = j-TCBB,
volume = "5",
number = "1",
pages = "80--90",
month = jan,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A translation of SBML (Systems Biology Markup
Language) into a process algebra is proposed in order
to allow the formal specification, the simulation and
the formal analysis of biological models. Beta-binders,
a language with a quantitative stochastic extension, is
chosen for the translation. The proposed translation
focuses on the main components of SBML models, as
species and reactions. Furthermore, it satisfies the
compositional property, i.e. the translation of the
whole model is obtained by composing the translation of
the subcomponents. An automatic translator tool of SBML
models into Beta-binders has been implemented as well.
Finally, the translation of a simple model is
reported.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biological systems; modeling; Process algebras;
systems biology; Systems Biology Markup Language
(SBML); translation tool",
}
@Article{Bocker:2008:CAM,
author = "Sebastian Bocker and Veli Makinen",
title = "Combinatorial Approaches for Mass Spectra
Recalibration",
journal = j-TCBB,
volume = "5",
number = "1",
pages = "91--100",
month = jan,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Mass spectrometry has become one of the most popular
analysis techniques in Proteomics and Systems Biology.
With the creation of larger datasets, the automated
recalibration of mass spectra becomes important to
ensure that every peak in the sample spectrum is
correctly assigned to some peptide and protein.
Algorithms for recalibrating mass spectra have to be
robust with respect to wrongly assigned peaks, as well
as efficient due to the amount of mass spectrometry
data. The recalibration of mass spectra leads us to the
problem of finding an optimal matching between mass
spectra under measurement errors. We have developed two
deterministic methods that allow robust computation of
such a matching: The first approach uses a
computational geometry interpretation of the problem,
and tries to find two parallel lines with constant
distance that stab a maximal number of points in the
plane. The second approach is based on finding a
maximal common approximate subsequence, and improves
existing algorithms by one order of magnitude
exploiting the sequential nature of the matching
problem. We compare our results to a computational
geometry algorithm using a topological line-sweep.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biotechnology; combinatorial pattern matching;
computational geometry; mass spectrometry",
}
@Article{Barzuza:2008:CPP,
author = "Tamar Barzuza and Jacques S. Beckmann and Ron Shamir
and Itsik Pe'er",
title = "Computational Problems in Perfect Phylogeny
Haplotyping: Typing without Calling the Allele",
journal = j-TCBB,
volume = "5",
number = "1",
pages = "101--109",
month = jan,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A haplotype is an m-long binary vector. The
xor-genotype of two haplotypes is the m-vector of their
coordinate-wise xor. We study the following problem:
Given a set of xor-genotypes, reconstruct their
haplotypes so that the set of resulting haplotypes can
be mapped onto a perfect phylogeny tree. The question
is motivated by studying population evolution in human
genetics, and is a variant of the perfect phylogeny
haplotyping problem that has received intensive
attention recently. Unlike the latter problem, in which
the input is `full' genotypes, here we assume less
informative input, and so may be more economical to
obtain experimentally. Building on ideas of Gusfield,
we show how to solve the problem in polynomial time, by
a reduction to the graph realization problem. The
actual haplotypes are not uniquely determined by that
tree they map onto, and the tree itself may or may not
be unique. We show that tree uniqueness implies
uniquely determined haplotypes, up to inherent degrees
of freedom, and give a sufficient condition for the
uniqueness. To actually determine the haplotypes given
the tree, additional information is necessary. We show
that two or three full genotypes suffice to reconstruct
all the haplotypes, and present a linear algorithm for
identifying those genotypes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "graph realization; haplotypes; perfect phylogeny;
XOR-genotypes",
}
@Article{Chin:2008:DMR,
author = "Francis Chin and Henry C. M. Leung",
title = "{DNA} Motif Representation with Nucleotide
Dependency",
journal = j-TCBB,
volume = "5",
number = "1",
pages = "110--119",
month = jan,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The problem of discovering novel motifs of binding
sites is important to the understanding of gene
regulatory networks. Motifs are generally represented
by matrices (PWM or PSSM) or strings. However, these
representations cannot model biological binding sites
well because they fail to capture nucleotide
interdependence. It has been pointed out by many
researchers that the nucleotides of the DNA binding
site cannot be treated independently, e.g. the binding
sites of zinc finger in proteins. In this paper, a new
representation called Scored PositionSpecific Pattern
(SPSP), which is a generalization of the matrix and
string representations, is introduced which takes into
consideration the dependent occurrences of neighboring
nucleotides. Even though the problem of discovering the
optimal motif in SPSP representation is proved to
beNP-hard, we introduce a heuristic algorithm called
SPSP-Finder, which can effectively find optimal motifs
in most simulated cases and some real cases for which
existing popular motif finding software, such as
Weeder, MEME and AlignACE, fail.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Computing methodologies; design methodology; pattern
analysis; pattern recognition",
}
@Article{Yin:2008:NAC,
author = "Zong-Xian Yin and Jung-Hsien Chiang",
title = "Novel Algorithm for Coexpression Detection in
Time-Varying Microarray Data Sets",
journal = j-TCBB,
volume = "5",
number = "1",
pages = "120--135",
month = jan,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "When analyzing the results of microarray experiments,
biologists generally use unsupervised categorization
tools. However, such tools regard each time point as an
independent dimension and utilize the Euclidean
distance to compute the similarities between
expressions. Furthermore, some of these methods require
the number of clusters to be determined in advance,
which is clearly impossible in the case of a new
dataset. Therefore, this study proposes a novel scheme,
designated as the Variation-based Co-expression
Detection (VCD) algorithm, to analyze the trends of
expressions based on their variation over time. The
proposed algorithm has two advantages. First, it is
unnecessary to determine the number of clusters in
advance since the algorithm automatically detects those
genes whose profiles are grouped together and creates
patterns for these groups. Second, the algorithm
features a new measurement criterion for calculating
the degree of change of the expressions between
adjacent time points and evaluating their trend
similarities. Three real-world microarray datasets are
employed to evaluate the performance of the proposed
algorithm.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "bioinformatics; clustering; data mining; gene
expression; pattern analysis; time series analysis",
}
@Article{Goeffon:2008:PTN,
author = "Adrien Goeffon and Jean-Michel Richer and Jin-Kao
Hao",
title = "Progressive Tree Neighborhood Applied to the Maximum
Parsimony Problem",
journal = j-TCBB,
volume = "5",
number = "1",
pages = "136--145",
month = jan,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The Maximum Parsimony problem aims at reconstructing a
phylogenetic tree from DNA sequences while minimizing
the number of genetic transformations. To solve this
NP-complete problem, heuristic methods have been
developed, often based on local search. In this
article, we focus on the influence of the neighborhood
relations. After analyzing the advantages and drawbacks
of the well-known NNI, SPR and TBR neighborhoods, we
introduce the concept of Progressive Neighborhood which
consists in constraining progressively the size of the
neighborhood as the search advances. We empirically
show that applied to the Maximum Parsimony problem,
this progressive neighborhood turns out to be more
efficient and robust than the classic neighborhoods
using a descent algorithm. Indeed, it allows to find
better solutions with a smaller number of iterations or
trees evaluated.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "combinatorial algorithms; maximum parsimony;
optimization; phylogeny reconstruction",
}
@Article{Anonymous:2008:RL,
author = "Anonymous",
title = "2007 Reviewers List",
journal = j-TCBB,
volume = "5",
number = "1",
pages = "146--147",
month = jan,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2008:AI,
author = "Anonymous",
title = "2007 Annual Index",
journal = j-TCBB,
volume = "5",
number = "1",
pages = "148--158",
month = jan,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2008:CAE,
author = "Anonymous",
title = "Call for Applications for {Editor-in-Chief}",
journal = j-TCBB,
volume = "5",
number = "1",
pages = "159--159",
month = jan,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:11 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jackson:2008:CGM,
author = "Benjamin N. Jackson and Patrick S. Schnable and
Srinivas Aluru",
title = "Consensus Genetic Maps as Median Orders from
Inconsistent Sources",
journal = j-TCBB,
volume = "5",
number = "2",
pages = "161--171",
month = apr,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A genetic map is an ordering of genetic markers
calculated from a population of known lineage. While
traditionally a map has been generated from a single
population for each species, recently researchers have
created maps from multiple populations. In the face of
these new data, we address the need to find a consensus
map --- a map that combines the information from
multiple partial and possibly inconsistent input maps.
We model each input map as a partial order and
formulate the consensus problem as finding a median
partial order. Finding the median of multiple total
orders (preferences or rankings)is a well studied
problem in social choice. We choose to find the median
using the weighted symmetric difference distance, a
more general version of both the symmetric difference
distance and the Kemeny distance. Finding a median
order using this distance is NP-hard. We show that for
our chosen weight assignment, a median order satisfies
the positive responsiveness, extended Condorcet,and
unanimity criteria. Our solution involves finding the
maximum acyclic subgraph of a weighted directed graph.
We present a method that dynamically switches between
an exact branch and bound algorithm and a heuristic
algorithm, and show that for real data from closely
related organisms, an exact median can often be found.
We present experimental results using seven populations
of the crop plant \bioname{Zea mays}.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "genetic map; Kemeny distance; median order; path and
circuit problems; symmetric difference distance.",
}
@Article{Gupta:2008:EDS,
author = "Anupam Gupta and Ziv Bar-Joseph",
title = "Extracting Dynamics from Static Cancer Expression
Data",
journal = j-TCBB,
volume = "5",
number = "2",
pages = "172--182",
month = apr,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Static expression experiments analyze samples from
many individuals. These samples are often snapshots of
the progression of a certain disease such as cancer.
This raises an intriguing question: Can we determine a
temporal order for these samples? Such an ordering can
lead to better understanding of the dynamics of the
disease and to the identification of genes associated
with its progression. In this paper we formally prove,
for the first time, that under a model for the dynamics
of the expression levels of a single gene, it is indeed
possible to recover the correct ordering of the static
expression datasets by solving an instance of the
traveling salesman problem (TSP). In addition, we
devise an algorithm that combines a TSP heuristic and
probabilistic modeling for inferring the underlying
temporal order of the microarray experiments. This
algorithm constructs probabilistic continuous curves to
represent expression profiles leading to accurate
temporal reconstruction for human data. Applying our
method to cancer expression data we show that the
ordering derived agrees well with survival duration. A
classifier that utilizes this ordering improves upon
other classifiers suggested for this task. The set of
genes displaying consistent behavior for the determined
ordering are enriched for genes associated with cancer
progression.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "EM; glioma; microarrays; traveling salesman",
}
@Article{Thomas:2008:GMR,
author = "John Thomas and Naren Ramakrishnan and Chris
Bailey-Kellogg",
title = "Graphical Models of Residue Coupling in Protein
Families",
journal = j-TCBB,
volume = "5",
number = "2",
pages = "183--197",
month = apr,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Many statistical measures and algorithmic techniques
have been proposed for studying residue coupling in
protein families. Generally speaking, two residue
positions are considered coupled if, in the sequence
record, some of their amino acid type combinations are
significantly more common than others. While the
proposed approaches have proven useful in finding and
describing coupling, a significant missing component is
a formal probabilistic model that explicates and
compactly represents the coupling, integrates
information about sequence,structure, and function, and
supports inferential procedures for analysis,
diagnosis, and prediction. We present an approach to
learning and using probabilistic graphical models of
residue coupling. These models capture significant
conservation and coupling constraints observable in a
multiply-aligned set of sequences. Our approach can
place a structural prior on considered couplings, so
that all identified relationships have direct
mechanistic explanations. It can also incorporate
information about functional classes, and thereby learn
a differential graphical model that distinguishes
constraints common to all classes from those unique to
individual classes. Such differential models separately
account for class-specific conservation and family-wide
coupling, two different sources of sequence
covariation. They are then able to perform
interpretable functional classification of new
sequences, explaining classification decisions in terms
of the underlying conservation and coupling
constraints. We apply our approach in studies of both G
protein-coupled receptors and PDZ domains, identifying
and analyzing family-wide and class-specific
constraints, and performing functional classification.
The results demonstrate that graphical models of
residue coupling provide a powerful tool for
uncovering, representing, and utilizing significant
sequence structure-function relationships in protein
families.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "correlated mutations; evolutionary covariation;
functional classification; graphical models;
sequence-structure-function relationships",
}
@Article{Mena-Chalco:2008:IPC,
author = "Jesus Mena-Chalco and Helaine Carrer and Yossi Zana
and Roberto M. {Cesar Jr.}",
title = "Identification of Protein Coding Regions Using the
Modified {Gabor}-Wavelet Transform",
journal = j-TCBB,
volume = "5",
number = "2",
pages = "198--207",
month = apr,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "An important topic in genomic sequence analysis is the
identification of protein coding regions. In this
context, several coding DNA model-independent methods,
based on the occurrence of specific patterns of
nucleotides at coding regions, have been proposed.
Nonetheless, these methods have not been completely
suitable due to their dependence on an empirically
pre-defined window length required for a local analysis
of a DNA region. We introduce a method, based on a
modified Gabor-wavelet transform (MGWT), for the
identification of protein coding regions. This novel
transform is tuned to analyze periodic signal
components and presents the advantage of being
independent of the window length. We compared the
performance of the MGWT with other methods using
eukaryote datasets. The results show that the MGWT
outperforms all assessed model-independent methods with
respect to identification accuracy. These results
indicate that the source of at least part of the
identification errors produced by the previous methods
is the fixed working scale. The new method not only
avoids this source of errors, but also makes available
a tool for detailed exploration of the nucleotide
occurrence.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biology and genetics; pattern recognition; signal
processing",
}
@Article{deJong:2008:SSS,
author = "Hidde de Jong and Michel Page",
title = "Search for Steady States of Piecewise-Linear
Differential Equation Models of Genetic Regulatory
Networks",
journal = j-TCBB,
volume = "5",
number = "2",
pages = "208--222",
month = apr,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Analysis of the attractors of a genetic regulatory
network gives a good indication of the possible
functional modes of the system. In this paper we are
concerned with the problem of finding all steady states
of genetic regulatory networks described by
piecewise-linear differential equation (PLDE) models.
We show that the problem is NP-hard and translate it
into a propositional satisfiability (SAT) problem. This
allows the use of existing, efficient SAT solvers and
has enabled the development of a steady state search
module of the computer tool Genetic Network Analyzer
(GNA). The practical use of this module is demonstrated
by means of the analysis of a number of relatively
small bacterial regulatory networks as well as randomly
generated networks of several hundreds of genes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "genetic regulatory networks; large-scale systems;
piecewise-linear differential equations; SAT problem;
steady states",
}
@Article{Sadot:2008:TVB,
author = "Avital Sadot and Jasmin Fisher and Dan Barak and
Yishai Admanit and Michael J. Stern and E. Jane Albert
Hubbard and David Harel",
title = "Toward Verified Biological Models",
journal = j-TCBB,
volume = "5",
number = "2",
pages = "223--234",
month = apr,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The last several decades have witnessed a vast
accumulation of biological data and data analysis. Many
of these data sets represent only a small fraction of
the system's behavior, making the visualization of full
system behavior difficult. A more complete
understanding of a biological system is gained when
different types of data (and/or conclusions drawn from
the data) are integrated into a larger-scale
representation or model of the system. Ideally, this
type of model is consistent with all available data
about the system, and it is then used to generate
additional hypotheses to be tested. Computer-based
methods intended to formulate models that integrate
various events and to test the consistency of these
models with respect to the laboratory-based
observations on which they are based are potentially
very useful. In addition, in contrast to informal
models, the consistency of such formal computer-based
models with laboratory data can be tested rigorously by
methods of formal verification. We combined two formal
modeling approaches in computer science that were
originally developed for non-biological system design.
One is the inter-object approach using the language of
live sequence charts (LSCs) with the Play-Engine tool,
and the other is the intra-object approach using the
language of statecharts and Rhapsody as the tool.
Integration is carried out using InterPlay, a
simulation engine coordinator. Using these tools, we
constructed a combined model comprising three modules.
One module represents the early lineage of the somatic
gonad of \bioname{C. elegans} in LSCs, while a second more
detailed module in statecharts represents an
interaction between two cells within this lineage that
determine their developmental outcome. Using the
advantages of the tools, we created a third module
representing a set of key experimental data using LSCs.
We tested the combined statechart-LSC model by showing
that the simulations were consistent with the set of
experimental LSCs. This small-scale modular example
demonstrates the potential for using similar approaches
for verification by exhaustive testing of models by
LSCs. It also shows the advantages of these approaches
for modeling biology.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "C. elegans; modeling; statecharts; verification",
}
@Article{Spillner:2008:CPD,
author = "Andreas Spillner and Binh T. Nguyen and Vincent
Moulton",
title = "Computing Phylogenetic Diversity for Split Systems",
journal = j-TCBB,
volume = "5",
number = "2",
pages = "235--244",
month = apr,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In conservation biology it is a central problem to
measure, predict, and preserve biodiversity as species
face extinction. In 1992 Faith proposed measuring the
diversity of a collection of species in terms of their
relationships on a phylogenetic tree, and to use this
information to identify collections of species with
high diversity. Here we are interested in some variants
of the resulting optimization problem that arise when
considering species whose evolution is better
represented by a network rather than a tree. More
specifically, we consider the problem of computing
phylogenetic diversity relative to a split system on a
collection of species of size $n$. We show that for
general split systems this problem is NP-hard. In
addition we provide some efficient algorithms for some
special classes of split systems, in particular
presenting an optimal $ O(n) $ time algorithm for
phylogenetic trees and an $ O(n \log n + n k) $ time
algorithm for choosing an optimal subset of size $k$
relative to a circular split system.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biology and genetics; life and medical sciences",
}
@Article{Lancia:2008:HDA,
author = "Giuseppe Lancia and R. Ravi and Romeo Rizzi",
title = "Haplotyping for Disease Association: a Combinatorial
Approach",
journal = j-TCBB,
volume = "5",
number = "2",
pages = "245--251",
month = apr,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We consider a combinatorial problem derived from
haplotyping a population with respect to a genetic
disease, either recessive or dominant. Given a set of
individuals, partitioned into healthy and diseased, and
the corresponding sets of genotypes, we want to infer
``bad'' and ``good'' haplotypes to account for these
genotypes and for the disease. Assume e.g. the disease
is recessive. Then, the resolving haplotypes must
consist of {\em bad\/} and {\em good\/} haplotypes, so
that (i) each genotype belonging to a diseased
individual is explained by a pair of bad haplotypes and
(ii) each genotype belonging to a healthy individual is
explained by a pair of haplotypes of which at least one
is good. We prove that the associated decision problem
is NP-complete. However, we also prove that there is a
simple solution, provided the data satisfy a very weak
requirement.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biology and genetics; combinatorics; discrete
mathematics",
}
@Article{Gusev:2008:HSG,
author = "Alexander Gusev and Ion I. M{\~a}ndoiu and Bogdan
Pa{\c{s}}aniuc",
title = "Highly Scalable Genotype Phasing by Entropy
Minimization",
journal = j-TCBB,
volume = "5",
number = "2",
pages = "252--261",
month = apr,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A Single Nucleotide Polymorphism (SNP) is a position
in the genome at which two or more of the possible four
nucleotides occur in a large percentage of the
population. SNPsaccount for most of the genetic
variability between individuals,and mapping SNPs in the
human population has become the next high-priority in
genomics after the completion of the HumanGenome
project. In diploid organisms such as humans, there are
two non-identical copies of each autosomal chromosome.
A description of the SNPs in a chromosome is called a
haplotype. At present, it is prohibitively expensive to
directly determine the haplotypes of an individual, but
it is possible to obtain rather easily the conflated
SNP information in the so called genotype.
Computational methods for genotype phasing, i.e.,
inferring haplotypes from genotype data, have received
much attention in recent years as haplotype information
leads to increased statistical power of disease
association tests. However, many of the existing
algorithms have impractical running time for phasing
large genotype datasets such as those generated by the
international HapMap project. In this paper we propose
a highly scalable algorithm based on entropy
minimization. Our algorithm is capable of phasing both
unrelated and related genotypes coming from complex
pedigrees. Experimental results on both real and
simulated datasets show that our algorithm achieves a
phasing accuracy worse but close to that of best
existing methods while being several orders of
magnitude faster. The open source code implementation
of the algorithm and a web interface are publicly
available at
\path=http://dna.engr.uconn.edu/~software/ent/=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "algorithm; genotype phasing; haplotype; Single
Nucleotide Polymorphism",
}
@Article{Zhao:2008:ICG,
author = "Wentao Zhao and Erchin Serpedin and Edward R.
Dougherty",
title = "Inferring Connectivity of Genetic Regulatory Networks
Using Information-Theoretic Criteria",
journal = j-TCBB,
volume = "5",
number = "2",
pages = "262--274",
month = apr,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recently, the concept of mutual information has been
proposed for infering the structure of genetic
regulatory networks from gene expression profiling.
After analyzing the limitations of mutual information
in inferring the gene-to-gene interactions, this paper
introduces the concept of conditional mutual
information and based on it proposes two novel
algorithms to infer the connectivity structure of
genetic regulatory networks. One of the proposed
algorithms exhibits a better accuracy while the other
algorithm excels in simplicity and flexibility. By
exploiting the mutual information and conditional
mutual information, a practical metric is also proposed
to assess the likeliness of direct connectivity between
genes. This novel metric resolves a common limitation
associated with the current inference algorithms,
namely the situations where the gene connectivity is
established in terms of the dichotomy of being either
connected or disconnected. Based on the data sets
generated by synthetic networks, the performance of the
proposed algorithms is compared favorably relative to
existing state-of-the-art schemes. The proposed
algorithms are also applied on realistic biological
measurements, such as the cutaneous melanoma data set,
and biological meaningful results are inferred.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biology and genetics; DNA microarray; genetic
regulatory network; information theory",
}
@Article{Bordewich:2008:NRS,
author = "Magnus Bordewich and Charles Semple",
title = "Nature Reserve Selection Problem: a Tight
Approximation Algorithm",
journal = j-TCBB,
volume = "5",
number = "2",
pages = "275--280",
month = apr,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The Nature Reserve Selection Problem is a problem that
arises in the context of studying biodiversity
conservation. Subject to budgetary constraints, the
problem is to select a set of regions to conserve so
that the phylogenetic diversity of the set of species
contained within those regions is maximized. Recently,
it was shown in a paper by Moulton {\em et al.} that
this problem is NP-hard. In this paper, we establish a
tight polynomial-time approximation algorithm for the
Nature Reserve Section Problem. Furthermore, we resolve
a question on the computational complexity of a related
problem left open in Moulton {\em et al.}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "combinatorial algorithms; trees",
}
@Article{Hsieh:2008:OAI,
author = "Yong-Hsiang Hsieh and Chih-Chiang Yu and Biing-Feng
Wang",
title = "Optimal Algorithms for the Interval Location Problem
with Range Constraints on Length and Average",
journal = j-TCBB,
volume = "5",
number = "2",
pages = "281--290",
month = apr,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Let $A$ be a sequence of $n$ real numbers, $ L_1 $ and
$ L_2 $ be two integers such that $ L_1 \leq L_2 $, and
$ R_1 $ and $ R_2 $ be two real numbers such that $ R_1
\leq R_2 $. An interval of $A$ is feasible if its
length is between $ L_1 $ and $ L_2 $ and its average
is between $ R_1 $ and $ R_2 $. In this paper, we study
the following problems: finding all feasible intervals
of $A$, counting all feasible intervals of $A$, finding
a maximum cardinality set of non-overlapping feasible
intervals of $A$, locating a longest feasible interval
of $A$, and locating a shortest feasible interval of
$A$. The problems are motivated from the problem of
locating CpG islands in biomolecular sequences. In this
paper, we firstly show that all the problems have an $
\Omega (n \log n) $-time lower bound in the comparison
model. Then, we use geometric approaches to design
optimal algorithms for the problems. All the presented
algorithms run in an on-line manner and use $ O(n) $
space.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "algorithms; analysis of algorithms; data structures;
geometrical problems and computations",
}
@Article{Lamers:2008:PRX,
author = "Susanna L. Lamers and Marco Salemi and Michael S.
McGrath and Gary B. Fogel",
title = "Prediction of {R5}, {X4}, and {R5X4} {HIV}-1
Coreceptor Usage with Evolved Neural Networks",
journal = j-TCBB,
volume = "5",
number = "2",
pages = "291--300",
month = apr,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The HIV-1 genome is highly heterogeneous. This
variation affords the virus a wide range of molecular
properties, including the ability to infect cell types,
such as macrophages and lymphocytes, expressing
different chemokine receptors on the cell surface. In
particular, R5 HIV-1 viruses use CCR5 as co-receptor
for viral entry, X4 viruses use CXCR4, whereas some
viral strains, known as R5X4 or D-tropic, have the
ability to utilize both co-receptors. X4 and R5X4
viruses are associated with rapid disease progression
to AIDS. R5X4 viruses differ in that they have yet to
be characterized by the examination of the genetic
sequence of HIV-1 alone. In this study, a series of
experiments was performed to evaluate different
strategies of feature selection and neural network
optimization. We demonstrate the use of artificial
neural networks trained via evolutionary computation to
predict viral co-receptor usage. The results indicate
identification of R5X4 viruses with predictive accuracy
of 75.5\%.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "AIDS; artificial neural networks; Computational
intelligence; dual-tropic viruses; evolutionary
computation; HIV; phenotype prediction; tropism",
}
@Article{vanIersel:2008:SIT,
author = "Leo van Iersel and Judith Keijsper and Steven Kelk and
Leen Stougie",
title = "Shorelines of Islands of Tractability: Algorithms for
Parsimony and Minimum Perfect Phylogeny Haplotyping
Problems",
journal = j-TCBB,
volume = "5",
number = "2",
pages = "301--312",
month = apr,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The problem Parsimony Haplotyping (PH) asks for the
smallest set of haplotypes which can explain a given
set of genotypes, and the problem Minimum Perfect
Phylogeny Haplotyping (MPPH) asks for the smallest such
set which also allows the haplotypes to be embedded in
a perfect phylogeny, an evolutionary tree with
biologically-motivated restrictions. For PH, we extend
recent work by further mapping the interface between
``easy'' and ``hard'' instances, within the framework
of $ (k, l) $-bounded instances where the number of 2's
per column and row of the input matrix is restricted.
By exploring, in the same way, the tractability
frontier of MPPH we provide the first concrete,
positive results for this problem. In addition, we
construct for both PH and MPPH polynomial time
approximation algorithms, based on properties of the
columns of the input matrix.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biology and genetics; combinatorial algorithms;
complexity hierarchies",
}
@Article{Brinza:2008:SPM,
author = "Dumitru Brinza and Alexander Zelikovsky",
title = "{2SNP}: Scalable Phasing Method for Trios and
Unrelated Individuals",
journal = j-TCBB,
volume = "5",
number = "2",
pages = "313--318",
month = apr,
year = "2008",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jun 12 16:59:29 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Emerging microarray technologies allow affordable
typing of very long genome sequences. A key challenge
in analyzing of such huge amount of data is scalable
and accurate computational inferring of haplotypes
(i.e., splitting of each genotype into a pair of
corresponding haplotypes). In this paper, we first
phase genotypes consisting only of two SNPs using
genotypes frequencies adjusted to the random mating
model and then extend phasing of two-SNP genotypes to
phasing of complete genotypes using maximum spanning
trees. Runtime of the proposed 2SNP algorithm is $ O(n
m (n + \log m)) $, where $n$ and $m$ are the numbers of
genotypes and SNPs, respectively, and it can handle
genotypes spanning entire chromosomes in a matter of
hours. On datasets across 23 chromosomal regions from
HapMap[11], 2SNP is several orders of magnitude faster
than GERBIL and PHASE while matching them in quality
measured by the number of correctly phased genotypes,
single-site and switching errors. For example the 2SNP
software phases entire chromosome ($ 10^5 $ SNPs from
HapMap) for 30 individuals in 2 hours with average
switching error 7.7\%. We have also enhanced 2SNP
algorithm to phase family trio data and compared it
with four other well-known phasing methods on simulated
data from [15]. 2SNP is much faster than all of them
while losing in quality only to PHASE. 2SNP software is
publicly available at
\path=http://alla.cs.gsu.edu/~software/2SNP=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "algorithm; genotype; haplotype; phasing; SNP",
}
@Article{Mandoiu:2008:GEI,
author = "Ion I. Mandoiu and Yi Pan and Alexander Zelikovsky",
title = "{Guest Editors}' Introduction to the Special Section
on Bioinformatics Research and Applications",
journal = j-TCBB,
volume = "5",
number = "3",
pages = "321--322",
month = jul,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.85",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Oct 10 12:59:44 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sridhar:2008:MIL,
author = "Srinath Sridhar and Fumei Lam and Guy E. Blelloch and
R. Ravi and Russell Schwartz",
title = "Mixed Integer Linear Programming for Maximum-Parsimony
Phylogeny Inference",
journal = j-TCBB,
volume = "5",
number = "3",
pages = "323--331",
month = jul,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.26",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Oct 10 12:59:44 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Reconstruction of phylogenetic trees is a fundamental
problem in computational biology. While excellent
heuristic methods are available for many variants of
this problem, new advances in phylogeny inference will
be required if we are to be able to continue to make
effective use of the rapidly growing stores of
variation data now being gathered. In this paper, we
present two integer linear programming (ILP)
formulations to find the most parsimonious phylogenetic
tree from a set of binary variation data. One method
uses a flow-based formulation that can produce
exponential numbers of variables and constraints in the
worst case. The method has, however, proven extremely
efficient in practice on datasets that are well beyond
the reach of the available provably efficient methods,
solving several large mtDNA and Y-chromosome instances
within a few seconds and giving provably optimal
results in times competitive with fast heuristics than
cannot guarantee optimality. An alternative formulation
establishes that the problem can be solved with a
polynomial-sized ILP. We further present a web server
developed based on the exponential-sized ILP that
performs fast maximum parsimony inferences and serves
as a front end to a database of precomputed phylogenies
spanning the human genome.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "algorithms; computational biology; integer linear
programming; maximum parsimony; phylogenetic tree
reconstruction; Steiner tree problem",
}
@Article{Bernt:2008:SPR,
author = "Matthias Bernt and Daniel Merkle and Martin
Middendorf",
title = "Solving the Preserving Reversal Median Problem",
journal = j-TCBB,
volume = "5",
number = "3",
pages = "332--347",
month = jul,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.39",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Oct 10 12:59:44 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genomic rearrangement operations can be very useful to
infer the phylogenetic relationship of gene orders
representing species. We study the problem of finding
potential ancestral gene orders for the gene orders of
given taxa, such that the corresponding rearrangement
scenario has a minimal number of reversals, and where
each of the reversals has to preserve the common
intervals of the given input gene orders. Common
intervals identify sets of genes that occur
consecutively in all input gene orders. The problem of
finding such an ancestral gene order is called the
preserving reversal median problem (pRMP). A tree-based
data structure for the representation of the common
intervals of all input gene orders is used in our exact
algorithm TCIP for solving the pRMP. It is known that
the minimum number of reversals to transform one gene
order into another can be computed in polynomial time,
whereas the corresponding problem with the restriction
that common intervals should not be destroyed is
already NP-hard. It is shown theoretically that TCIP
can solve a large class of pRMP instances in polynomial
time. Empirically we show the good performance of TCIP
on biological and artificial data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biology and genetics; permutations and combinations",
}
@Article{Braga:2008:ESS,
author = "Mar{\'\i}lia D. V. Braga and Marie-France Sagot and
Celine Scornavacca and Eric Tannier",
title = "Exploring the Solution Space of Sorting by Reversals,
with Experiments and an Application to Evolution",
journal = j-TCBB,
volume = "5",
number = "3",
pages = "348--356",
month = jul,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.16",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Oct 10 12:59:44 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In comparative genomics, algorithms that sort
permutations by reversals are often used to propose
evolutionary scenarios of rearrangements between
species. One of the main problems of such methods is
that they give one solution while the number of optimal
solutions is huge, with no criteria to discriminate
among them. Bergeron et al. started to give some
structure to the set of optimal solutions, in order to
be able to deliver more presentable results than only
one solution or a complete list of all solutions.
However, no algorithm exists so far to compute this
structure except through the enumeration of all
solutions, which takes too much time even for small
permutations. Bergeron et al. state as an open problem
the design of such an algorithm. We propose in this
paper an answer to this problem, that is, an algorithm
which gives all the classes of solutions and counts the
number of solutions in each class, with a better
theoretical and practical complexity than the complete
enumeration method. We give an example of how to reduce
the number of classes obtained, using further
constraints. Finally, we apply our algorithm to analyse
the possible scenarios of rearrangement between
mammalian sex chromosomes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "common intervals; evolution; genome rearrangements;
perfect sorting; sex chromosomes; signed permutations;
sorting by reversals",
}
@Article{Vassura:2008:RSP,
author = "Marco Vassura and Luciano Margara and Pietro {Di Lena}
and Filippo Medri and Piero Fariselli and Rita
Casadio",
title = "Reconstruction of {$3$D} Structures From Protein
Contact Maps",
journal = j-TCBB,
volume = "5",
number = "3",
pages = "357--367",
month = jul,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.27",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Oct 10 12:59:44 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The prediction of the protein tertiary structure from
solely its residue sequence (the so called Protein
Folding Problem) is one of the most challenging
problems in Structural Bioinformatics. We focus on the
protein residue contact map. When this map is assigned
it is possible to reconstruct the 3D structure of the
protein backbone. The general problem of recovering a
set of 3D coordinates consistent with some given
contact map is known as a unit-disk-graph realization
problem and it has been recently proven to be NP-Hard.
In this paper we describe a heuristic method (COMAR)
that is able to reconstruct with an unprecedented rate
(3-15 seconds) a 3D model that exactly matches the
target contact map of a protein. Working with a
non-redundant set of 1760 proteins, we find that the
scoring efficiency of finding a 3D model very close to
the protein native structure depends on the threshold
value adopted to compute the protein residue contact
map. Contact maps whose threshold values range from 10
to 18 {\AA}ngstroms allow reconstructing 3D models that
are very similar to the proteins native structure.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "combinatorial algorithms; contact map; molecular
modeling; protein structure prediction",
}
@Article{Lee:2008:IEN,
author = "George Lee and Carlos Rodriguez and Anant Madabhushi",
title = "Investigating the Efficacy of Nonlinear Dimensionality
Reduction Schemes in Classifying Gene and Protein
Expression Studies",
journal = j-TCBB,
volume = "5",
number = "3",
pages = "368--384",
month = jul,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.36",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Oct 10 12:59:44 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The recent explosion in procurement and availability
of high-dimensional gene- and protein-expression
profile datasets for cancer diagnostics has
necessitated the development of sophisticated machine
learning tools with which to analyze them. A major
limitation in the ability to accurate classify these
high-dimensional datasets stems from the `curse of
dimensionality', occurring in situations where the
number of genes or peptides significantly exceeds the
total number of patient samples. Previous attempts at
dealing with this issue have mostly centered on the use
of a dimensionality reduction (DR) scheme, Principal
Component Analysis (PCA), to obtain a low-dimensional
projection of the high-dimensional data. However,
linear PCA and other linear DR methods, which rely on
Euclidean distances to estimate object similarity, do
not account for the inherent underlying nonlinear
structure associated with most biomedical data. The
motivation behind this work is to identify the
appropriate DR methods for analysis of high-dimensional
gene- and protein-expression studies. Towards this end,
we empirically and rigorously compare three nonlinear
(Isomap, Locally Linear Embedding, Laplacian Eigenmaps)
and three linear DR schemes (PCA, Linear Discriminant
Analysis, Multidimensional Scaling) with the intent of
determining a reduced subspace representation in which
the individual object classes are more easily
discriminable.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "and association rules; Bioinformatics (genome or
protein) databases; classification; clustering; data
and knowledge visualization; data mining; feature
extraction or construction",
}
@Article{Cho:2008:CHC,
author = "Hyuk Cho and Inderjit S. Dhillon",
title = "Coclustering of Human Cancer Microarrays Using Minimum
Sum-Squared Residue Coclustering",
journal = j-TCBB,
volume = "5",
number = "3",
pages = "385--400",
month = jul,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70268",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Oct 10 12:59:44 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "It is a consensus in microarray analysis that
identifying potential local patterns, characterized by
coherent groups of genes and conditions, may shed light
on the discovery of previously undetectable biological
cellular processes of genes as well as macroscopic
phenotypes of related samples. In order to
simultaneously cluster genes and conditions, we have
previously developed a fast co-clustering algorithm,
Minimum Sum-Squared Residue Co-clustering (MSSRCC),
which employs an alternating minimization scheme and
generates what we call co-clusters in a checkerboard
structure. In this paper, we propose specific
strategies that enable MSSRCC to escape poor local
minima and resolve the degeneracy problem in
partitional clustering algorithms. The strategies
include binormalization, deterministic spectral
initialization, and incremental local search. We assess
the effects of various strategies on both synthetic
gene expression datasets and real human cancer
microarrays and provide empirical evidence that MSSRCC
with the proposed strategies performs better than
existing co-clustering and clustering algorithms. In
particular, the combination of all the three strategies
leads to the best performance. Furthermore, we
illustrate coherence of the resulting co-clusters in a
checkerboard structure, where genes in a co-cluster
manifest the phenotype structure of corresponding
specific samples, and evaluate the enrichment of
functional annotations in Gene Ontology (GO).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "binormalization; co-clustering; deterministic spectral
initialization; gene ontology; local search; microarray
analysis",
}
@Article{Wei:2008:IGF,
author = "Peng Wei and Wei Pan",
title = "Incorporating Gene Functions into Regression Analysis
of {DNA}-Protein Binding Data and Gene Expression Data
to Construct Transcriptional Networks",
journal = j-TCBB,
volume = "5",
number = "3",
pages = "401--415",
month = jul,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.1062",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Oct 10 12:59:44 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Useful information on transcriptional networks has
been extracted by regression analyses of gene
expression data and DNA-protein binding data. However,
a potential limitation of these approaches is their
assumption on the common and constant activity level of
a transcription factor (TF) on all the genes in any
given experimental condition; for example, any TF is
assumed to be either an activator or a repressor, but
not both, while it is known that some TFs can be dual
regulators. Rather than assuming a common linear
regression model for all the genes, we propose using
separate regression models for various gene groups; the
genes can be grouped based on their functions or some
clustering results. Furthermore, to take advantage of
the hierarchical structure of many existing gene
function annotation systems, such as Gene Ontology
(GO), we propose a shrinkage method that borrows
information from relevant gene groups. Applications to
a yeast dataset and simulations lend support for our
proposed methods. In particular, we find that the
shrinkage method consistently works well under various
scenarios. We recommend the use of the shrinkage method
as a useful alternative to the existing methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "LASSO; microarray; shrinkage estimator; stratified
analysis; transcription factor",
}
@Article{Mak:2008:PPS,
author = "Man-Wai Mak and Jian Guo and Sun-Yuan Kung",
title = "{PairProSVM}: Protein Subcellular Localization Based
on Local Pairwise Profile Alignment and {SVM}",
journal = j-TCBB,
volume = "5",
number = "3",
pages = "416--422",
month = jul,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70256",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Oct 10 12:59:44 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The subcellular locations of proteins are important
functional annotations. An effective and reliable
subcellular localization method is necessary for
proteomics research. This paper introduces a new
method---PairProSVM---to automatically predict the
subcellular locations of proteins. The profiles of all
protein sequences in the training set are constructed
by PSI-BLAST and the pairwise profile-alignment scores
are used to form feature vectors for training a support
vector machine (SVM) classifier. It was found that
PairProSVM outperforms the methods that are based on
sequence alignment and amino-acid compositions even if
most of the homologous sequences have been removed.
This paper also demonstrates that the performance of
PairProSVM is sensitive (and somewhat proportional) to
the degree of its kernel matrix meeting the Mercer's
condition. PairProSVM was evaluated on Reinhardt and
Hubbard's, Huang and Li's, and Gardy et al.'s protein
datasets. The overall accuracies on these three
datasets reach 99.3\%, 76.5\%, and 91.9\%,
respectively, which are higher than or comparable to
those obtained by sequence alignment and by the methods
compared in this paper.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "kernel methods; Mercer condition; profile alignment;
subcellular localization; support vector machines",
}
@Article{Elo:2008:ROT,
author = "Laura L. Elo and Sanna Filen and Riitta Lahesmaa and
Tero Aittokallio",
title = "Reproducibility-Optimized Test Statistic for Ranking
Genes in Microarray Studies",
journal = j-TCBB,
volume = "5",
number = "3",
pages = "423--431",
month = jul,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/tcbb.2007.1078",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Oct 10 12:59:44 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A principal goal of microarray studies is to identify
the genes showing differential expression under
distinct conditions. In such studies, the selection of
an optimal test statistic is a crucial challenge, which
depends on the type and amount of data under analysis.
While previous studies on simulated or spike-in
datasets do not provide practical guidance on how to
choose the best method for a given real dataset, we
introduce an enhanced reproducibility-optimization
procedure, which enables the selection of a suitable
gene- anking statistic directly from the data. In
comparison with existing ranking methods, the
reproducibility-optimized statistic shows good
performance consistently under various simulated
conditions and on Affymetrix spike-in dataset. Further,
the feasibility of the novel statistic is confirmed in
a practical research setting using data from an
in-house cDNA microarray study of asthma-related gene
expression changes. These results suggest that the
procedure facilitates the selection of an appropriate
test statistic for a given dataset without relying on a
priori assumptions, which may bias the findings and
their interpretation. Moreover, the general
reproducibility-optimization procedure is not limited
to detecting differential expression only but could be
extended to a wide range of other applications as
well.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "bootstrap; differential expression; gene expression;
gene ranking; microarray; reproducibility",
}
@Article{Parker:2008:SPT,
author = "Douglass Stott Parker and Ruey-Lung Hsiao and Yi Xing
and Alissa M. Resch and Christopher J. Lee",
title = "Solving the Problem of Trans-Genomic Query with
Alignment Tables",
journal = j-TCBB,
volume = "5",
number = "3",
pages = "432--447",
month = jul,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.1073",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Oct 10 12:59:44 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The trans-genomic query (TGQ) problem -- enabling the
free query of biological information, even across
genomes -- is a central challenge facing
bioinformatics. Solutions to this problem can alter the
nature of the field, moving it beyond the jungle of
data integration and expanding the number and scope of
questions that can be answered. An alignment table is a
binary relationship on locations (sequence segments).
An important special case of alignment tables are hit
tables --- tables of pairs of highly similar segments
produced by alignment tools like BLAST. However,
alignment tables also include general binary
relationships, and can represent any useful connection
between sequence locations. They can be curated, and
provide a high-quality queryable backbone of
connections between biological information. Alignment
tables thus can be a natural foundation for TGQ, as
they permit a central part of the TGQ problem to be
reduced to purely technical problems involving tables
of locations. Key challenges in implementing alignment
tables include efficient representation and indexing of
sequence locations. We define a location datatype that
can be incorporated naturally into common off-the-shelf
database systems. We also describe an implementation of
alignment tables in BLASTGRES, an extension of the
open-source POSTGRESQL database system that provides
indexing and operators on locations required for
querying alignment tables. This paper also reviews
several successful large-scale applications of
alignment tables for Trans-Genomic Query. Tables with
millions of alignments have been used in queries about
alternative splicing, an area of genomic analysis
concerning the way in which a single gene can yield
multiple transcripts. Comparative genomics is a large
potential application area for TGQ and alignment
tables.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dawy:2008:FSG,
author = "Zaher Dawy and Michel Sarkis and Joachim Hagenauer and
Jakob C. Mueller",
title = "Fine-Scale Genetic Mapping Using Independent Component
Analysis",
journal = j-TCBB,
volume = "5",
number = "3",
pages = "448--460",
month = jul,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.1072",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Oct 10 12:59:44 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The aim of genetic mapping is to locate the loci
responsible for specific traits such as complex
diseases. These traits are normally caused by mutations
at multiple loci of unknown locations and interactions.
In this work, we model the biological system that
relates DNA polymorphisms with complex traits as a
linear mixing process. Given this model, we propose a
new fine-scale genetic mapping method based on
independent component analysis. The proposed method
outputs both independent associated groups of SNPs in
addition to specific associated SNPs with the
phenotype. It is applied to a clinical data set for the
Schizophrenia disease with 368 individuals and 42 SNPs.
It is also applied to a simulation study to investigate
in more depth its performance. The obtained results
demonstrate the novel characteristics of the proposed
method compared to other genetic mapping methods.
Finally, we study the robustness of the proposed method
with missing genotype values and limited sample
sizes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "association mapping; complex diseases; independent
component analysis (ICA); linkage disequilibrium;
principal component analysis (PCA); single nucleotide
polymorphisms (SNPs)",
}
@Article{Hendy:2008:HCK,
author = "Michael D. Hendy and Sagi Snir",
title = "{Hadamard} Conjugation for the {Kimura} {3ST} Model:
Combinatorial Proof Using Path Sets",
journal = j-TCBB,
volume = "5",
number = "3",
pages = "461--471",
month = jul,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70227",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Oct 10 12:59:44 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Under a stochastic model of molecular sequence
evolution the probability of each possible pattern of a
characters is well defined. The Kimura's
three-substitution-types (K3ST) model of evolution,
allows analytical expression for these probabilities of
by means of the Hadamard conjugation as a function of
the phylogeny T and the substitution probabilities on
each edge of TM. In this paper we produce a direct
combinatorial proof of these results, using pathset
distances which generalise pairwise distances between
sequences. This interpretation provides us with tools
that were proved useful in related problems in the
mathematical analysis of sequence evolution.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Hadamard conjugation; K3ST model; path-sets;
phylogenetic invariants; phylogenetic trees",
}
@Article{Gambette:2008:ILP,
author = "Philippe Gambette and Daniel H. Huson",
title = "Improved Layout of Phylogenetic Networks",
journal = j-TCBB,
volume = "5",
number = "3",
pages = "472--479",
month = jul,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/tcbb.2007.1046",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Oct 10 12:59:44 MDT 2008",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Split networks are increasingly being used in
phylogenetic analysis. Usually, a simple equal angle
algorithm is used to draw such networks, producing
layouts that leave much room for improvement.
Addressing the problem of producing better layouts of
split networks, this paper presents an algorithm for
maximizing the area covered by the network, describes
an extension of the equal-daylight algorithm to
networks, looks into using a spring embedder and
discusses how to construct rooted split networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "algorithms; graph drawing; phylogenetic networks;
phylogenetics",
}
@Article{Gusfield:2008:EE,
author = "Dan Gusfield",
title = "{EIC} Editorial",
journal = j-TCBB,
volume = "5",
number = "4",
pages = "481--481",
month = oct,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.115",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 14 12:51:33 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Giancarlo:2008:GEI,
author = "Raffaele Giancarlo and Sridhar Hannenhalli",
title = "{Guest Editors}' Introduction to the Special Section
on Algorithms in Bioinformatics",
journal = j-TCBB,
volume = "5",
number = "4",
pages = "482--483",
month = oct,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.116",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 14 12:51:33 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jeong:2008:ISP,
author = "Jieun Jeong and Piotr Berman and Teresa M. Przytycka",
title = "Improving Strand Pairing Prediction through Exploring
Folding Cooperativity",
journal = j-TCBB,
volume = "5",
number = "4",
pages = "484--491",
month = oct,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.88",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 14 12:51:33 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The topology of $ \beta $-sheets is defined by the
pattern of hydrogen-bonded strand pairing. Therefore,
predicting hydrogen bonded strand partners is a
fundamental step towards predicting $ \beta $-sheet
topology. At the same time, finding the correct
partners is very difficult due to long range
interactions involved in strand pairing. Additionally,
patterns of aminoacids involved, in $ \beta $-sheet
formations are very general and therefore difficult to
use for computational recognition of specific contacts
between strands. In this work, we report a new strand
pairing algorithm. To address above mentioned
difficulties, our algorithm attempts to mimic elements
of the folding process. Namely, in addition to ensuring
that the predicted hydrogen bonded strand pairs satisfy
basic global consistency constraints, it takes into
account hypothetical folding pathways. Consistently
with this view, introducing hydrogen bonds between a
pair of strands changes the probabilities of forming
hydrogen bonds between other pairs of strand. We
demonstrate that this approach provides an improvement
over previously proposed algorithms. We also compare
the performance of this method to that of a global
optimization algorithm that poses the problem as
integer linear programming optimization problem and
solves it using ILOG CPLEX\TM{} package.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Biology and genetics; Combinatorial algorithms",
}
@Article{Genovese:2008:SAH,
author = "Loredana M. Genovese and Filippo Geraci and Marco
Pellegrini",
title = "{SpeedHap}: An Accurate Heuristic for the Single
Individual {SNP} Haplotyping Problem with Many Gaps,
High Reading Error Rate and Low Coverage",
journal = j-TCBB,
volume = "5",
number = "4",
pages = "492--502",
month = oct,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.67",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 14 12:51:33 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Single nucleotide polymorphism (SNP) is the most
frequent form of DNA variation. The set of SNP's
present in a chromosome (called the em haplotype) is of
interest in a wide area of applications in molecular
biology and biomedicine, including diagnostic and
medical therapy. In this paper we propose a new
heuristic method for the problem of haplotype
reconstruction for (portions of) a pair of homologous
human chromosomes from a single individual (SIH). The
problem is well known in literature and exact
algorithms have been proposed for the case when no (or
few) gaps are allowed in the input fragments. These
algorithms, though exact and of polynomial complexity,
are slow in practice. When gaps are considered no exact
method of polynomial complexity is known. The problem
is also hard to approximate with guarantees. Therefore
fast heuristics have been proposed. In this paper we
describe SpeedHap, a new heuristic method that is able
to tackle the case of many gapped fragments and retains
its effectiveness even when the input fragments have
high rate of reading errors (up to 20\%) and low
coverage (as low as 3). We test SpeedHap on real data
from the HapMap Project.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Algorithms; Biology and genetics",
}
@Article{Lozano:2008:STA,
author = "Antoni Lozano and Ron Y. Pinter and Oleg Rokhlenko and
Gabriel Valiente and Michal Ziv-Ukelson",
title = "Seeded Tree Alignment",
journal = j-TCBB,
volume = "5",
number = "4",
pages = "503--513",
month = oct,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.59",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 14 12:51:33 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The optimal transformation of one tree into another by
means of elementary edit operations is an important
algorithmic problem that has several interesting
applications to computational biology. Here we
introduce a constrained form of this problem in which a
partial mapping of a set of nodes (the `seeds') in one
tree to a corresponding set of nodes in the other tree
is given, and present efficient algorithms for both
ordered and unordered trees. Whereas ordered tree
matching based on seeded nodes has applications in
pattern matching of RNA structures, unordered tree
matching based on seeded nodes has applications in
co-speciation and phylogeny reconciliation. The latter
involves the solution of the planar tanglegram layout
problem, for which a polynomial-time algorithm is given
here.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Biology and genetics; Computer Applications; Discrete
Mathematics; Graph algorithms; Graph Theory; Life and
Medical Sciences; Mathematics of Computing; Trees",
}
@Article{Bansal:2008:STH,
author = "Mukul S. Bansal and Oliver Eulenstein",
title = "An {$ \Omega (n^2 / \log n) $} Speed-Up of {TBR}
Heuristics for the Gene-Duplication Problem",
journal = j-TCBB,
volume = "5",
number = "4",
pages = "514--524",
month = oct,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.69",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 14 12:51:33 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The gene-duplication problem is to infer a species
supertree from gene trees that are confounded by
complex histories of gene duplications. This problem is
NP-hard and thus requires efficient and effective
heuristics. Existing heuristics perform a stepwise
search of the tree space, where each step is guided by
an exact solution to an instance of a local search
problem. We improve on the time complexity of the local
search problem by a factor of $ n^2 = \log n $, where
$n$ is the size of the resulting species supertree.
Typically, several thousand instances of the local
search problem are solved throughout a stepwise
heuristic search. Hence, our improvement makes the
gene-duplication problem much more tractable for
large-scale phylogenetic analyses.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Algorithms; Computational Biology; Gene Duplication;
Phylogenetics; Supertrees",
}
@Article{Wang:2008:DCO,
author = "Xueyi Wang and Jack Snoeyink",
title = "Defining and Computing Optimum {RMSD} for Gapped and
Weighted Multiple-Structure Alignment",
journal = j-TCBB,
volume = "5",
number = "4",
pages = "525--533",
month = oct,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.92",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 14 12:51:33 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Pairwise structure alignment commonly uses root mean
square deviation (RMSD) to measure the structural
similarity, and methods for optimizing RMSD are well
established. We extend RMSD to weighted RMSD for
multiple structures. By using multiplicative weights,
we show that weighted RMSD for all pairs is the same as
weighted RMSD to an average of the structures. Thus,
using RMSD or weighted RMSD implies that the average is
a consensus structure. Although we show that in
general, the two tasks of finding the optimal
translations and rotations for minimizing weighted RMSD
cannot be separated for multiple structures like they
can for pairs, an inherent difficulty and a fact
ignored by previous work, we develop a near-linear
iterative algorithm to converge weighted RMSD to a
local minimum. 10,000 experiments of gapped alignment
done on each of 23 protein families from HOMSTRAD
(where each structure starts with a random translation
and rotation) converge rapidly to the same minimum.
Finally we propose a heuristic method to iteratively
remove the effect of outliers and find well-aligned
positions that determine the structural conserved
region by modeling B-factors and deviations from the
average positions as weights and iteratively assigning
higher weights to better aligned atoms.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "multiple structure alignment; optimization methods;
structural conserved region; weighted RMSD",
}
@Article{Yao:2008:EAE,
author = "Peggy Yao and Ankur Dhanik and Nathan Marz and Ryan
Propper and Charles Kou and Guanfeng Liu and Henry van
den Bedem and Jean-Claude Latombe and Inbal
Halperin-Landsberg and Russ B. Altman",
title = "Efficient Algorithms to Explore Conformation Spaces of
Flexible Protein Loops",
journal = j-TCBB,
volume = "5",
number = "4",
pages = "534--545",
month = oct,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.96",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 14 12:51:33 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Several applications in biology --- e.g.,
incorporation of protein flexibility in ligand docking
algorithms, interpretation of fuzzy X-ray
crystallographic data, and homology modeling ---
require computing the internal parameters of a flexible
fragment (usually, a loop) of a protein in order to
connect its termini to the rest of the protein without
causing any steric clash. One must often sample many
such conformations in order to explore and adequately
represent the conformational range of the studied loop.
While sampling must be fast, it is made difficult by
the fact that two conflicting constraints --- kinematic
closure and clash avoidance --- must be satisfied
concurrently. This paper describes two efficient and
complementary sampling algorithms to explore the space
of closed clash-free conformations of a flexible
protein loop. The `seed sampling' algorithm samples
broadly from this space, while the `deformation
sampling' algorithm uses seed conformations as starting
points to explore the conformation space around them at
a finer grain. Computational results are presented for
various loops ranging from 5 to 25 residues. More
specific results also show that the combination of the
sampling algorithms with a functional site prediction
software (FEATURE) makes it possible to compute and
recognize calcium-binding loop conformations. The
sampling algorithms are implemented in a toolkit
(LoopTK), which is available at
\path=https://simtk.org/home/looptk=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Biology and genetics; Robotics",
}
@Article{Kim:2008:LSS,
author = "Eagu Kim and John Kececioglu",
title = "Learning Scoring Schemes for Sequence Alignment from
Partial Examples",
journal = j-TCBB,
volume = "5",
number = "4",
pages = "546--556",
month = oct,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.57",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 14 12:51:33 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "When aligning biological sequences, the choice of
parameter values for the alignment scoring function is
critical. Small changes in gap penalties, for example,
can yield radically different alignments. A rigorous
way to compute parameter values that are appropriate
for aligning biological sequences is through inverse
parametric sequence alignment. Given a collection of
examples of biologically correct alignments, this is
the problem of finding parameter values that make the
scores of the example alignments close to those of
optimal alignments for their sequences. We extend prior
work on inverse parametric alignment to partial
examples, which contain regions where the alignment is
left unspecified, and to an improved formulation based
on minimizing the average error between the score of an
example and the score of an optimal alignment.
Experiments on benchmark biological alignments show we
can find parameters that generalize across protein
families and that boost the accuracy of multiple
sequence alignment by as much as 25\%.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Analysis of Algorithms and Problem Complexity; Biology
and genetics; Linear programming; Pattern matching",
}
@Article{Schliep:2008:EAC,
author = "Alexander Schliep and Roland Krause",
title = "Efficient Algorithms for the Computational Design of
Optimal Tiling Arrays",
journal = j-TCBB,
volume = "5",
number = "4",
pages = "557--567",
month = oct,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.50",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 14 12:51:33 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The representation of a genome by oligonucleotide
probes is a prerequisite for the analysis of many of
its basic properties, such as transcription factor
binding sites, chromosomal breakpoints, gene expression
of known genes and detection of novel genes, in
particular those coding for small RNAs. An ideal
representation would consist of a high density set of
oligonucleotides with similar melting temperatures that
do not cross-hybridize with other regions of the genome
and are equidistantly spaced. The implementation of
such design is typically called a tiling array or
genome array. We formulate the minimal cost tiling path
problem for the selection of oligonucleotides from a
set of candidates. Computing the selection of probes
requires multi-criterion optimization, which we cast
into a shortest path problem. Standard algorithms
running in linear time allow us to compute globally
optimal tiling paths from millions of candidate
oligonucleotides on a standard desktop computer for
most problem variants. The solutions to this
multi-criterion optimization are spatially adaptive to
the problem instance. Our formulation incorporates
experimental constraints with respect to specific
regions of interest and trade offs between
hybridization parameters, probe quality and tiling
density easily.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Biology and genetics; Graph Theory",
}
@Article{Yu:2008:CAA,
author = "Zeyun Yu and Chandrajit Bajaj",
title = "Computational Approaches for Automatic Structural
Analysis of Large Biomolecular Complexes",
journal = j-TCBB,
volume = "5",
number = "4",
pages = "568--582",
month = oct,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70226",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 14 12:51:33 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present computational solutions to two problems of
macromolecular structure interpretation from
reconstructed three-dimensional electron microscopy
(3D-EM) maps of large bio-molecular complexes at
intermediate resolution (5A-15A). The two problems
addressed are: (a) 3D structural alignment
(matching)between identified and segmented 3D maps of
structure units(e.g. trimeric configuration of
proteins), and (b) the secondary structure
identification of a segmented protein 3D map (i.e.,
locations of $ \alpha $-helices, $ \beta $-sheets). For
problem (a), we present an efficient algorithm to
correlate spatially (and structurally)two 3D maps of
structure units. Besides providing a similarity score
between structure units, the algorithm yields an
effective technique for resolution refinement of
repeated structure units,by 3D alignment and averaging.
For problem (b), we present an efficient algorithm to
compute eigenvalues and link eigenvectors of a Gaussian
convoluted structure tensor derived from the protein 3D
Map, thereby identifying and locating secondary
structural motifs of proteins. The efficiency and
performance of our approach is demonstrated on several
experimentally reconstructed 3D maps of virus capsid
shells from single-particle cryo-EM, as well as
computationally simulated protein structure density 3D
maps generated from protein model entries in the
Protein Data Bank.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "3D Reconstruction; Alignment; Cryo-EM Maps; Secondary
Structure Detection; Segmentation; Similarity Measure;
Skeletonization; Structure Analysis",
}
@Article{Christinat:2008:GED,
author = "Yann Christinat and Bernd Wachmann and Lei Zhang",
title = "Gene Expression Data Analysis Using a Novel Approach
to Biclustering Combining Discrete and Continuous
Data",
journal = j-TCBB,
volume = "5",
number = "4",
pages = "583--593",
month = oct,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70251",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 14 12:51:33 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Many different methods exist for pattern detection in
gene expression data. In contrast to classical methods,
biclustering has the ability to cluster a group of
genes together with a group of conditions (replicates,
set of patients or drug compounds). However, since the
problem is NP-complex, most algorithms use heuristic
search functions and therefore might converge towards
local maxima. By using the results of biclustering on
discrete data as a starting point for a local search
function on continuous data, our algorithm avoids the
problem of heuristic initialization. Similar to OPSM,
our algorithm aims to detect biclusters whose rows and
columns can be ordered such that row values are growing
across the bicluster's columns and vice-versa. Results
have been generated on the yeast genome (Saccharomyces
cerevisiae), a human cancer dataset and random data.
Results on the yeast genome showed that 89\% of the one
hundred biggest non-overlapping biclusters were
enriched with Gene Ontology annotations. A comparison
with OPSM and ISA demonstrated a better efficiency when
using gene and condition orders. We present results on
random and real datasets that show the ability of our
algorithm to capture statistically significant and
biologically relevant biclusters.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Bioinformatics (genome or protein) databases; Data and
knowledge visualization; Data mining; Graph and tree
search strategies; Machine learning",
}
@Article{Lacroix:2008:IMN,
author = "Vincent Lacroix and Ludovic Cottret and Patricia
Th{\'e}bault and Marie-France Sagot",
title = "An Introduction to Metabolic Networks and Their
Structural Analysis",
journal = j-TCBB,
volume = "5",
number = "4",
pages = "594--617",
month = oct,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.79",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 14 12:51:33 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "There has been a renewed interest for metabolism in
the computational biology community, leading to an
avalanche of papers coming from methodological network
analysis as well as experimental and theoretical
biology. This paper is meant to serve as an initial
guide for both the biologists interested in formal
approaches and the mathematicians or computer
scientists wishing to inject more realism into their
models. The paper is focused on the structural aspects
of metabolism only. The literature is vast enough
already, and the thread through it difficult to follow
even for the more experienced worker in the field. We
explain methods for acquiring data and reconstructing
metabolic networks, and review the various models that
have been used for their structural analysis. Several
concepts such as modularity are introduced, as are the
controversies that have beset the field these past few
years, for instance, on whether metabolic networks are
small-world or scale-free, and on which model better
explains the evolution of metabolism. Clarifying the
work that has been done also helps in identifying open
questions and in proposing relevant future directions
in the field, which we do along the paper and in the
conclusion.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Biology and genetics; evolution; Graph Theory;
Introductory and Survey; metabolic networks; modelling;
modularity; reconstruction",
}
@Article{Satya:2008:UIP,
author = "Ravi Vijaya Satya and Amar Mukherjee",
title = "The Undirected Incomplete Perfect Phylogeny Problem",
journal = j-TCBB,
volume = "5",
number = "4",
pages = "618--629",
month = oct,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70218",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 14 12:51:33 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The incomplete perfect phylogeny (IPP) problem and the
incomplete perfect phylogeny haplotyping (IPPH) problem
deal with constructing a phylogeny for a given set of
haplotypes or genotypes with missing entries. The
earlier approaches for both of these problems dealt
with restricted versions of the problems, where the
root is either available or can be trivially
re-constructed from the data, or certain assumptions
were made about the data. In this paper, we deal with
the unrestricted versions of the problems, where the
root of the phylogeny is neither available nor
trivially recoverable from the data. Both IPP and IPPH
problems have previously been proven to be NP complete.
Here, we present efficient enumerative algorithms that
can handle practical instances of the problem.
Empirical analysis on simulated data shows that the
algorithms perform very well both in terms of speed and
in terms accuracy of the recovered data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Haplotype Inference; Incomplete Perfect Phylogeny;
Perfect Phylogeny; Phylogenetics",
}
@Article{Gondro:2008:OCM,
author = "Cedric Gondro and Brian P. Kinghorn",
title = "Optimization of {cDNA} Microarray Experimental Designs
Using an Evolutionary Algorithm",
journal = j-TCBB,
volume = "5",
number = "4",
pages = "630--638",
month = oct,
year = "2008",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70222",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 14 12:51:33 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The cDNA microarray is an important tool for
generating large datasets of gene expression
measurements. An efficient design is critical to ensure
that the experiment will be able to address relevant
biological questions. Microarray experimental design
can be treated as a multicriterion optimization
problem. For this class of problems evolutionary
algorithms (EAs) are well suited, as they can search
the solution space and evolve a design that optimizes
the parameters of interest based on their relative
value to the researcher under a given set of
constraints. This paper introduces the use of EAs for
optimization of experimental designs of spotted
microarrays using a weighted objective function. The EA
and the various criteria relevant to design
optimization are discussed. Evolved designs are
compared with designs obtained through exhaustive
search with results suggesting that the EA can find
just as efficient optimal or near-optimal designs
within a tractable timeframe.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Evolutionary computing and genetic algorithms;
experimental design; global optimization; microarrays",
}
@Article{Gusfield:2009:FFY,
author = "Dan Gusfield",
title = "Final, Five-Year End, Editorial",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "1--2",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sagot:2009:NEE,
author = "Marie-France Sagot",
title = "New {EIC} Editorial",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "3--3",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huson:2009:SSP,
author = "Daniel H. Huson and Vincent Moulton and Mike Steel",
title = "Special Section: Phylogenetics",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "4--6",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2009:BWT,
author = "Kevin Liu and Serita Nelesen and Sindhu Raghavan and
C. Randal Linder and Tandy Warnow",
title = "Barking Up The Wrong Treelength: The Impact of Gap
Penalty on Alignment and Tree Accuracy",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "7--21",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Minh:2009:BPD,
author = "Bui Quang Minh and Fabio Pardi and Steffen Klaere and
Arndt von Haeseler",
title = "Budgeted Phylogenetic Diversity on Circular Split
Systems",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "22--29",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Linz:2009:HNT,
author = "Simone Linz and Charles Semple",
title = "Hybridization in Nonbinary Trees",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "30--45",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cardona:2009:MPNa,
author = "Gabriel Cardona and Merc{\`e} Llabr{\'e}s and Francesc
Rossell{\'o} and Gabriel Valiente",
title = "Metrics for Phylogenetic Networks {I}: Generalizations
of the {Robinson--Foulds} Metric",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "46--61",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Willson:2009:RTS,
author = "Stephen J. Willson",
title = "Robustness of Topological Supertree Methods for
Reconciling Dense Incompatible Data",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "62--75",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Allman:2009:ICM,
author = "Elizabeth S. Allman and John A. Rhodes",
title = "The Identifiability of Covarion Models in
Phylogenetics",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "76--88",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Matsen:2009:FTI,
author = "Frederick A. Matsen",
title = "{Fourier} Transform Inequalities for Phylogenetic
Trees",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "89--95",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gusfield:2009:OEE,
author = "Dan Gusfield",
title = "Outgoing {EIC} Editorial for this Special Section of
{TCBB} with the Theme of Phylogenetics",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "96--96",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Grunewald:2009:MPT,
author = "Stefan Gr{\"u}newald and Vincent Moulton",
title = "Maximum Parsimony for Tree Mixtures",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "97--102",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huson:2009:DRP,
author = "Daniel H. Huson",
title = "Drawing Rooted Phylogenetic Networks",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "103--109",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bordewich:2009:CTM,
author = "Magnus Bordewich and Olivier Gascuel and Katharina T.
Huber and Vincent Moulton",
title = "Consistency of Topological Moves Based on the Balanced
Minimum Evolution Principle of Phylogenetic Inference",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "110--117",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wu:2009:RPT,
author = "Taoyang Wu and Vincent Moulton and Mike Steel",
title = "Refining Phylogenetic Trees Given Additional Data: An
Algorithm Based on Parsimony",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "118--125",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mossel:2009:SEA,
author = "Elchanan Mossel and Sebastien Roch and Mike Steel",
title = "Shrinkage Effect in Ancestral Maximum Likelihood",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "126--133",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ma:2009:GCU,
author = "Jianmin Ma and Minh N. Nguyen and Jagath C.
Rajapakse",
title = "Gene Classification Using Codon Usage and Support
Vector Machines",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "134--143",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Maitra:2009:IPO,
author = "Ranjan Maitra",
title = "Initializing Partition-Optimization Algorithms",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "144--157",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Narasimhan:2009:SPG,
author = "Sridharakumar Narasimhan and Raghunathan Rengaswamy
and Rajanikanth Vadigepalli",
title = "Structural Properties of Gene Regulatory Networks:
Definitions and Connections",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "158--170",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2009:RL,
author = "Anonymous",
title = "2008 Reviewers List",
journal = j-TCBB,
volume = "6",
number = "1",
pages = "171--173",
month = jan,
year = "2009",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 2 18:46:49 MST 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sagot:2009:EE,
author = "Marie-France Sagot",
title = "{EIC} Editorial",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "177--177",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.44",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mandoiu:2009:GEI,
author = "Ion Mandoiu and Yi Pan and Raj Sunderraman and
Alexander Zelikovsky",
title = "{Guest Editors}' Introduction to the Special Section
on Bioinformatics Research and Applications",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "178--179",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.45",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Treangen:2009:NHL,
author = "Todd J. Treangen and Aaron E. Darling and Guillaume
Achaz and Mark A. Ragan and Xavier Messeguer and
Eduardo P. C. Rocha",
title = "A Novel Heuristic for Local Multiple Alignment of
Interspersed {DNA} Repeats",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "180--189",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.9",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Pairwise local sequence alignment methods have been
the prevailing technique to identify homologous
nucleotides between related species. However, existing
methods that identify and align all homologous
nucleotides in one or more genomes have suffered from
poor scalability and limited accuracy. We propose a
novel method that couples a gapped extension heuristic
with an efficient filtration method for identifying
interspersed repeats in genome sequences. During gapped
extension, we use the MUSCLE implementation of
progressive global multiple alignment with iterative
refinement. The resulting gapped extensions potentially
contain alignments of unrelated sequence. We detect and
remove such undesirable alignments using a hidden
Markov model (HMM) to predict the posterior probability
of homology. The HMM emission frequencies for
nucleotide substitutions can be derived from any
time-reversible nucleotide substitution matrix. We
evaluate the performance of our method and previous
approaches on a hybrid data set of real genomic DNA
with simulated interspersed repeats. Our method
outperforms a related method in terms of sensitivity,
positive predictive value, and localizing boundaries of
homology. The described methods have been implemented
in freely available software, Repeatoire, available
from:
\path=http://wwwabi.snv.jussieu.fr/public/Repeatoire=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "DNA repeats; gapped extension.; genome comparison;
hidden Markov model; local multiple alignment; Sequence
alignment",
}
@Article{Qiu:2009:FMK,
author = "Shibin Qiu and Terran Lane",
title = "A Framework for Multiple Kernel Support Vector
Regression and Its Applications to {siRNA} Efficacy
Prediction",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "190--199",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.139",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The cell defense mechanism of RNA interference has
applications in gene function analysis and promising
potentials in human disease therapy. To effectively
silence a target gene, it is desirable to select
appropriate initiator siRNA molecules having
satisfactory silencing capabilities. Computational
prediction for silencing efficacy of siRNAs can assist
this screening process before using them in biological
experiments. String kernel functions, which operate
directly on the string objects representing siRNAs and
target mRNAs, have been applied to support vector
regression for the prediction and improved accuracy
over numerical kernels in multidimensional vector
spaces constructed from descriptors of siRNA design
rules. To fully utilize information provided by string
and numerical data, we propose to unify the two in a
kernel feature space by devising a multiple kernel
regression framework where a linear combination of the
kernels is used. We formulate the multiple kernel
learning into a quadratically constrained quadratic
programming (QCQP) problem, which although yields
global optimal solution, is computationally demanding
and requires a commercial solver package. We further
propose three heuristics based on the principle of
kernel-target alignment and predictive accuracy.
Empirical results demonstrate that multiple kernel
regression can improve accuracy, decrease model
complexity by reducing the number of support vectors,
and speed up computational performance dramatically. In
addition, multiple kernel regression evaluates the
importance of constituent kernels, which for the siRNA
efficacy prediction problem, compares the relative
significance of the design rules. Finally, we give
insights into the multiple kernel regression mechanism
and point out possible extensions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "multiple kernel heuristics; Multiple kernel learning;
QCQP optimization; RNA interference; siRNA efficacy.;
support vector regression",
}
@Article{Park:2009:NBI,
author = "Yongjin Park and Stanley Shackney and Russell
Schwartz",
title = "Network-Based Inference of Cancer Progression from
Microarray Data",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "200--212",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.126",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Cancer cells exhibit a common phenotype of
uncontrolled cell growth, but this phenotype may arise
from many different combinations of mutations. By
inferring how cells evolve in individual tumors, a
process called cancer progression, we may be able to
identify important mutational events for different
tumor types, potentially leading to new therapeutics
and diagnostics. Prior work has shown that it is
possible to infer frequent progression pathways by
using gene expression profiles to estimate
``distances'' between tumors. Here, we apply gene
network models to improve these estimates of
evolutionary distance by controlling for correlations
among coregulated genes. We test three variants of this
approach: one using an optimized best-fit network,
another using sampling to infer a high-confidence
subnetwork, and one using a modular network inferred
from clusters of similarly expressed genes. Application
to lung cancer and breast cancer microarray data sets
shows small improvements in phylogenies when correcting
from the optimized network and more substantial
improvements when correcting from the sampled or
modular networks. Our results suggest that a network
correction approach improves estimates of tumor
similarity, but sophisticated network models are needed
to control for the large hypothesis space and sparse
data currently available.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Biology and genetics; graphs and networks; machine
learning.; trees",
}
@Article{Zhu:2009:GGA,
author = "Qian Zhu and Zaky Adam and Vicky Choi and David
Sankoff",
title = "Generalized Gene Adjacencies, Graph Bandwidth, and
Clusters in Yeast Evolution",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "213--220",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.121",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present a parameterized definition of gene clusters
that allows us to control the emphasis placed on
conserved order within a cluster. Though motivated by
biological rather than mathematical considerations,
this parameter turns out to be closely related to the
bandwidth parameter of a graph. Our focus will be on
how this parameter affects the characteristics of
clusters: how numerous they are, how large they are,
how rearranged they are, and to what extent they are
preserved from ancestor to descendant in a phylogenetic
tree. We infer the latter property by dynamic
programming optimization of the presence of individual
edges at the ancestral nodes of the phylogeny. We apply
our analysis to a set of genomes drawn from the Yeast
Gene Order Browser.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Ashbya gossypii; Candida glabrata; Comparative
genomics; dynamic programming; evolution; gene
clusters; genome rearrangements; graph bandwidth;
Kluyveromyces lactis.; Kluyveromyces waltii; phylogeny;
Saccharomyces cerevisiae; yeast",
}
@Article{Bansal:2009:GDP,
author = "Mukul S. Bansal and Oliver Eulenstein and Andr{\'e}
Wehe",
title = "The Gene-Duplication Problem: Near-Linear Time
Algorithms for {NNI}-Based Local Searches",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "221--231",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.7",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The gene-duplication problem is to infer a species
supertree from a collection of gene trees that are
confounded by complex histories of gene-duplication
events. This problem is NP-complete and thus requires
efficient and effective heuristics. Existing heuristics
perform a stepwise search of the tree space, where each
step is guided by an exact solution to an instance of a
local search problem. A classical local search problem
is the {\tt NNI} search problem, which is based on the
nearest neighbor interchange operation. In this work,
we (1) provide a novel near-linear time algorithm for
the {\tt NNI} search problem, (2) introduce extensions
that significantly enlarge the search space of the {\tt
NNI} search problem, and (3) present algorithms for
these extended versions that are asymptotically just as
efficient as our algorithm for the {\tt NNI} search
problem. The exceptional speedup achieved in the
extended {\tt NNI} search problems makes the
gene-duplication problem more tractable for large-scale
phylogenetic analyses. We verify the performance of our
algorithms in a comparison study using sets of large
randomly generated gene trees.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Computational phylogenetics; gene-duplication; local
search; supertrees; {\tt NNI}.",
}
@Article{Sun:2009:DPP,
author = "Yanni Sun and Jeremy Buhler",
title = "Designing Patterns and Profiles for Faster {HMM}
Search",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "232--243",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.14",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Profile HMMs are powerful tools for modeling conserved
motifs in proteins. They are widely used by search
tools to classify new protein sequences into families
based on domain architecture. However, the
proliferation of known motifs and new proteomic
sequence data poses a computational challenge for
search, requiring days of CPU time to annotate an
organism's proteome. It is highly desirable to speed up
HMM search in large databases. We design PROSITE-like
patterns and short profiles that are used as filters to
rapidly eliminate protein-motif pairs for which a full
profile HMM comparison does not yield a significant
match. The design of the pattern-based filters is
formulated as a multichoice knapsack problem.
Profile-based filters with high sensitivity are
extracted from a profile HMM based on their theoretical
sensitivity and false positive rate. Experiments show
that our profile-based filters achieve high sensitivity
(near 100 percent) while keeping around $ 20 \times $
speedup with respect to the unfiltered search program.
Pattern-based filters typically retain at least 90
percent of the sensitivity of the source HMM with $ 30
$--$ 40 \times $ speedup. The profile-based filters
have sensitivity comparable to the multistage filtering
strategy HMMERHEAD [15] and are faster in most of our
experiments.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "bioinformatics databases; Biology and genetics; hidden
Markov models.; sequence similarity search",
}
@Article{Shaik:2009:FAS,
author = "Jahangheer Shaik and Mohammed Yeasin",
title = "Fuzzy-Adaptive-Subspace-Iteration-Based Two-Way
Clustering of Microarray Data",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "244--259",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.15",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper presents
Fuzzy-Adaptive-Subspace-Iteration-based Two-way
Clustering (FASIC) of microarray data for finding
differentially expressed genes (DEGs) from two-sample
microarray experiments. The concept of fuzzy membership
is introduced to transform the hard adaptive subspace
iteration (ASI) algorithm into a fuzzy-ASI algorithm to
perform two-way clustering. The proposed approach
follows a progressive framework to assign a relevance
value to genes associated with each cluster.
Subsequently, each gene cluster is scored and ranked
based on its potential to provide a correct
classification of the sample classes. These ranks are
converted into $P$ values using the $R$-test, and the
significance of each gene is determined. A fivefold
validation is performed on the DEGs selected using the
proposed approach. Empirical analyses on a number of
simulated microarray data sets are conducted to
quantify the results obtained using the proposed
approach. To exemplify the efficacy of the proposed
approach, further analyses on different real microarray
data sets are also performed.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "classification and association rules; Clustering; data
and knowledge visualization; data mining; feature
extraction or construction.",
}
@Article{Vignes:2009:GCI,
author = "Matthieu Vignes and Florence Forbes",
title = "Gene Clustering via Integrated {Markov} Models
Combining Individual and Pairwise Features",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "260--270",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70248",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Clustering of genes into groups sharing common
characteristics is a useful exploratory technique for a
number of subsequent computational analysis. A wide
range of clustering algorithms have been proposed in
particular to analyze gene expression data, but most of
them consider genes as independent entities or include
relevant information on gene interactions in a
suboptimal way. We propose a probabilistic model that
has the advantage to account for individual data (e.g.,
expression) and pairwise data (e.g., interaction
information coming from biological networks)
simultaneously. Our model is based on hidden Markov
random field models in which parametric probability
distributions account for the distribution of
individual data. Data on pairs, possibly reflecting
distance or similarity measures between genes, are then
included through a graph, where the nodes represent the
genes, and the edges are weighted according to the
available interaction information. As a probabilistic
model, this model has many interesting theoretical
features. In addition, preliminary experiments on
simulated and real data show promising results and
points out the gain in using such an approach.
Availability: The software used in this work is written
in C++ and is available with other supplementary
material at
\path=http://mistis.inrialpes.fr/people/forbes/transparentia/supplementary.html=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "gene expression.; Markov random fields; metabolic
networks; model-based clustering",
}
@Article{Heath:2009:SMN,
author = "Lenwood S. Heath and Allan A. Sioson",
title = "Semantics of Multimodal Network Models",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "271--280",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70242",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A multimodal network (MMN) is a novel graph-theoretic
formalism designed to capture the structure of
biological networks and to represent relationships
derived from multiple biological databases. MMNs
generalize the standard notions of graphs and
hypergraphs, which are the bases of current
diagrammatic representations of biological phenomena,
and incorporate the concept of mode. Each vertex of an
MMN is a biological entity, a biot, while each modal
hyperedge is a typed relationship, where the type is
given by the mode of the hyperedge. The semantics of
each modal hyperedge $e$ is given through denotational
semantics, where a valuation function $ f \_ {e} $
defines the relationship among the values of the
vertices incident on $e$. The meaning of an MMN is
denoted in terms of the semantics of a hyperedge
sequence. A companion paper defines MMNs and
concentrates on the structural aspects of MMNs. This
paper develops MMN denotational semantics when used as
a representation of the semantics of biological
networks and discusses applications of MMNs in managing
complex biological data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biological model; biological networks; biot;
denotational semantics.; graph; hypergraph; mode;
Multimodal network",
}
@Article{Arribas-Gil:2009:SAS,
author = "Ana Arribas-Gil and Dirk Metzler and Jean-Louis
Plouhinec",
title = "Statistical Alignment with a Sequence Evolution Model
Allowing Rate Heterogeneity along the Sequence",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "281--295",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70246",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present a stochastic sequence evolution model to
obtain alignments and estimate mutation rates between
two homologous sequences. The model allows two possible
evolutionary behaviors along a DNA sequence in order to
determine conserved regions and take its heterogeneity
into account. In our model, the sequence is divided
into slow and fast evolution regions. The boundaries
between these sections are not known. It is our aim to
detect them. The evolution model is based on a fragment
insertion and deletion process working on fast regions
only and on a substitution process working on fast and
slow regions with different rates. This model induces a
pair hidden Markov structure at the level of
alignments, thus making efficient statistical alignment
algorithms possible. We propose two complementary
estimation methods, namely, a Gibbs sampler for
Bayesian estimation and a stochastic version of the EM
algorithm for maximum likelihood estimation. Both
algorithms involve the sampling of alignments. We
propose a partial alignment sampler, which is
computationally less expensive than the typical whole
alignment sampler. We show the convergence of the two
estimation algorithms when used with this partial
sampler. Our algorithms provide consistent estimates
for the mutation rates and plausible alignments and
sequence segmentations on both simulated and real
data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biology and genetics.; Markov processes; mathematics
and statistics; probabilistic algorithms; sequence
evolution",
}
@Article{Weber:2009:VET,
author = "Gunther H. Weber and Oliver Rubel and Min-Yu Huang and
Angela H. DePace and Charless C. Fowlkes and Soile V.
E. Keranen and Cris L. Luengo Hendriks and Hans Hagen
and David W. Knowles and Jitendra Malik and Mark D.
Biggin and Bernd Hamann",
title = "Visual Exploration of Three-Dimensional Gene
Expression Using Physical Views and Linked Abstract
Views",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "296--309",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70249",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "During animal development, complex patterns of gene
expression provide positional information within the
embryo. To better understand the underlying gene
regulatory networks, the Berkeley Drosophila
Transcription Network Project (BDTNP) has developed
methods that support quantitative computational
analysis of three-dimensional (3D) gene expression in
early Drosophila embryos at cellular resolution. We
introduce PointCloudXplore (PCX), an interactive
visualization tool that supports visual exploration of
relationships between different genes' expression using
a combination of established visualization techniques.
Two aspects of gene expression are of particular
interest: (1) gene expression patterns defined by the
spatial locations of cells expressing a gene and (2)
relationships between the expression levels of multiple
genes. PCX provides users with two corresponding
classes of data views: (1) Physical Views based on the
spatial relationships of cells in the embryo and (2)
Abstract Views that discard spatial information and
plot expression levels of multiple genes with respect
to each other. Cell Selectors highlight data associated
with subsets of embryo cells within a View. Using
linking, these selected cells can be viewed in multiple
representations. We describe PCX as a 3D gene
expression visualization tool and provide examples of
how it has been used by BDTNP biologists to generate
new hypotheses.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "brushing; information visualization; Interactive data
exploration; multiple linked views; physical views;
scatter plots.; spatial expression patterns;
three-dimensional gene expression; visualization",
}
@Article{Dougherty:2009:CBM,
author = "Edward R. Dougherty and Marcel Brun and Jeffrey M.
Trent and Michael L. Bittner",
title = "Conditioning-Based Modeling of Contextual Genomic
Regulation",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "310--320",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70247",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A more complete understanding of the alterations in
cellular regulatory and control mechanisms that occur
in the various forms of cancer has been one of the
central targets of the genomic and proteomic methods
that allow surveys of the abundance and/or state of
cellular macromolecules. This preference is driven both
by the intractability of cancer to generic therapies,
assumed to be due to the highly varied molecular
etiologies observed in cancer, and by the opportunity
to discern and dissect the regulatory and control
interactions presented by the highly diverse assortment
of perturbations of regulation and control that arise
in cancer. Exploiting the opportunities for inference
on the regulatory and control connections offered by
these revealing system perturbations is fraught with
the practical problems that arise from the way
biological systems operate. Two classes of regulatory
action in biological systems are particularly inimical
to inference, convergent regulation, where a variety of
regulatory actions result in a common set of control
responses (crosstalk), and divergent regulation, where
a single regulatory action produces entirely different
sets of control responses, depending on cellular
context (conditioning). We have constructed a coarse
mathematical model of the propagation of regulatory
influence in such distributed, context-sensitive
regulatory networks that allows a quantitative
estimation of the amount of crosstalk and conditioning
associated with a candidate regulatory gene taken from
a set of genes that have been profiled over a series of
samples where the candidate's activity varies.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Microarray; regulatory networks.",
}
@Article{Heath:2009:MNS,
author = "Lenwood S. Heath and Allan A. Sioson",
title = "Multimodal Networks: Structure and Operations",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "321--332",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70243",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A multimodal network (MMN) is a novel graph-theoretic
formalism designed to capture the structure of
biological networks and to represent relationships
derived from multiple biological databases. MMNs
generalize the standard notions of graphs and
hypergraphs, which are the bases of current
diagrammatic representations of biological phenomena
and incorporate the concept of mode. Each vertex of an
MMN is a biological entity, a biot, while each modal
hyperedge is a typed relationship, where the type is
given by the mode of the hyperedge. The current paper
defines MMNs and concentrates on the structural aspects
of MMNs. A companion paper develops MMNs as a
representation of the semantics of biological networks
and discusses applications of the MMNs in managing
complex biological data. The MMN model has been
implemented in a database system containing multiple
kinds of biological networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biological networks; biot.; graph; hypergraph; mode;
Multimodal network",
}
@Article{Yukinawa:2009:OAB,
author = "Naoto Yukinawa and Shigeyuki Oba and Kikuya Kato and
Shin Ishii",
title = "Optimal Aggregation of Binary Classifiers for
Multiclass Cancer Diagnosis Using Gene Expression
Profiles",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "333--343",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70239",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Multiclass classification is one of the fundamental
tasks in bioinformatics and typically arises in cancer
diagnosis studies by gene expression profiling. There
have been many studies of aggregating binary
classifiers to construct a multiclass classifier based
on one-versus-the-rest (1R), one-versus-one (11), or
other coding strategies, as well as some comparison
studies between them. However, the studies found that
the best coding depends on each situation. Therefore, a
new problem, which we call the ``optimal coding
problem,'' has arisen: how can we determine which
coding is the optimal one in each situation? To
approach this optimal coding problem, we propose a
novel framework for constructing a multiclass
classifier, in which each binary classifier to be
aggregated has a weight value to be optimally tuned
based on the observed data. Although there is no a
priori answer to the optimal coding problem, our weight
tuning method can be a consistent answer to the
problem. We apply this method to various classification
problems including a synthesized data set and some
cancer diagnosis data sets from gene expression
profiling. The results demonstrate that, in most
situations, our method can improve classification
accuracy over simple voting heuristics and is better
than or comparable to state-of-the-art multiclass
predictors.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "cancer diagnosis.; error correcting output coding;
gene expression profiling; Multiclass classification",
}
@Article{Olman:2009:PCA,
author = "Victor Olman and Fenglou Mao and Hongwei Wu and Ying
Xu",
title = "Parallel Clustering Algorithm for Large Data Sets with
Applications in Bioinformatics",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "344--352",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70272",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Large sets of bioinformatical data provide a challenge
in time consumption while solving the cluster
identification problem, and that is why a parallel
algorithm is so needed for identifying dense clusters
in a noisy background. Our algorithm works on a graph
representation of the data set to be analyzed. It
identifies clusters through the identification of
densely intraconnected subgraphs. We have employed a
minimum spanning tree (MST) representation of the graph
and solve the cluster identification problem using this
representation. The computational bottleneck of our
algorithm is the construction of an MST of a graph, for
which a parallel algorithm is employed. Our high-level
strategy for the parallel MST construction algorithm is
to first partition the graph, then construct MSTs for
the partitioned subgraphs and auxiliary bipartite
graphs based on the subgraphs, and finally merge these
MSTs to derive an MST of the original graph. The
computational results indicate that when running on 150
CPUs, our algorithm can solve a cluster identification
problem on a data set with 1,000,000 data points almost
100 times faster than on single CPU, indicating that
this program is capable of handling very large data
clustering problems in an efficient manner. We have
implemented the clustering algorithm as the software
CLUMP.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "clustering algorithm; genome application; parallel
processing.; Pattern recognition",
}
@Article{Paul:2009:PCC,
author = "Topon Kumar Paul and Hitoshi Iba",
title = "Prediction of Cancer Class with Majority Voting
Genetic Programming Classifier Using Gene Expression
Data",
journal = j-TCBB,
volume = "6",
number = "2",
pages = "353--367",
month = apr,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70245",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 1 17:03:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In order to get a better understanding of different
types of cancers and to find the possible biomarkers
for diseases, recently, many researchers are analyzing
the gene expression data using various machine learning
techniques. However, due to a very small number of
training samples compared to the huge number of genes
and class imbalance, most of these methods suffer from
overfitting. In this paper, we present a majority
voting genetic programming classifier (MVGPC) for the
classification of microarray data. Instead of a single
rule or a single set of rules, we evolve multiple rules
with genetic programming (GP) and then apply those
rules to test samples to determine their labels with
majority voting technique. By performing experiments on
four different public cancer data sets, including
multiclass data sets, we have found that the test
accuracies of MVGPC are better than those of other
methods, including AdaBoost with GP. Moreover, some of
the more frequently occurring genes in the
classification rules are known to be associated with
the types of cancers being studied in this paper.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Classifier design and evaluation; data mining;
evolutionary computing and genetic algorithm; feature
extraction; gene expression; majority voting.",
}
@Article{Sagot:2009:EEI,
author = "Marie-France Sagot",
title = "{EIC} Editorial: Introducing New {Associate Editors}",
journal = j-TCBB,
volume = "6",
number = "3",
pages = "369--369",
month = jul,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.66",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 11 18:13:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bi:2009:MCE,
author = "Chengpeng Bi",
title = "A {Monte Carlo} {EM} Algorithm for {De Novo Motif}
Discovery in Biomolecular Sequences",
journal = j-TCBB,
volume = "6",
number = "3",
pages = "370--386",
month = jul,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.103",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 11 18:13:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Motif discovery methods play pivotal roles in
deciphering the genetic regulatory codes (i.e., motifs)
in genomes as well as in locating conserved domains in
protein sequences. The Expectation Maximization (EM)
algorithm is one of the most popular methods used in de
novo motif discovery. Based on the position weight
matrix (PWM) updating technique, this paper presents a
Monte Carlo version of the EM motif-finding algorithm
that carries out stochastic sampling in local alignment
space to overcome the conventional EM's main drawback
of being trapped in a local optimum. The newly
implemented algorithm is named as Monte Carlo EM Motif
Discovery Algorithm (MCEMDA). MCEMDA starts from an
initial model, and then it iteratively performs Monte
Carlo simulation and parameter update until
convergence. A log-likelihood profiling technique
together with the top-$k$ strategy is introduced to
cope with the phase shifts and multiple modal issues in
motif discovery problem. A novel grouping motif
alignment (GMA) algorithm is designed to select motifs
by clustering a population of candidate local
alignments and successfully applied to subtle motif
discovery. MCEMDA compares favorably to other popular
PWM-based and word enumerative motif algorithms tested
using simulated $ (l, d) $-motif cases, documented
prokaryotic, and eukaryotic DNA motif sequences.
Finally, MCEMDA is applied to detect large blocks of
conserved domains using protein benchmarks and exhibits
its excellent capacity while compared with other
multiple sequence alignment methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Expectation maximization (EM); Monte Carlo EM; motif
discovery; multiple sequence alignment; transcriptional
regulation.",
}
@Article{Stoye:2009:UAR,
author = "Jens Stoye and Roland Wittler",
title = "A Unified Approach for Reconstructing Ancient Gene
Clusters",
journal = j-TCBB,
volume = "6",
number = "3",
pages = "387--400",
month = jul,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.135",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 11 18:13:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The order of genes in genomes provides extensive
information. In comparative genomics, differences or
similarities of gene orders are determined to predict
functional relations of genes or phylogenetic relations
of genomes. For this purpose, various combinatorial
models can be used to identify gene clusters --- groups
of genes that are colocated in a set of genomes. We
introduce a unified approach to model gene clusters and
define the problem of labeling the inner nodes of a
given phylogenetic tree with sets of gene clusters. Our
optimization criterion in this context combines two
properties: parsimony, i.e., the number of gains and
losses of gene clusters has to be minimal, and
consistency, i.e., for each ancestral node, there must
exist at least one potential gene order that contains
all the reconstructed clusters. We present and evaluate
an exact algorithm to solve this problem. Despite its
exponential worst-case time complexity, our method is
suitable even for large-scale data. We show the
effectiveness and efficiency on both simulated and real
data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Comparative genomics; consistency.; gene cluster; gene
cluster reconstruction; gene order; parsimony;
phylogeny",
}
@Article{Chen:2009:AAM,
author = "Xin Chen and Yun Cui",
title = "An Approximation Algorithm for the Minimum Breakpoint
Linearization Problem",
journal = j-TCBB,
volume = "6",
number = "3",
pages = "401--409",
month = jul,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.3",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 11 18:13:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In the recent years, there has been a growing interest
in inferring the total order of genes or markers on a
chromosome, since current genetic mapping efforts might
only suffice to produce a partial order. Many
interesting optimization problems were thus formulated
in the framework of genome rearrangement. As an
important one among them, the minimum breakpoint
linearization (MBL) problem is to find the total order
of a partially ordered genome that minimizes its
breakpoint distance to a reference genome whose genes
are already totally ordered. It was previously shown to
be NP-hard, and the algorithms proposed so far are all
heuristic. In this paper, we present an $ m^2 + m \over
2 $-approximation algorithm for the MBL problem, where
$m$ is the number of gene maps that are combined
together to form a partial order of the genome under
investigation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "approximation algorithms.; breakpoint distance;
Comparative genomics; partially ordered genomes",
}
@Article{Wang:2009:EKF,
author = "Zidong Wang and Xiaohui Liu and Yurong Liu and Jinling
Liang and Veronica Vinciotti",
title = "An Extended {Kalman} Filtering Approach to Modeling
Nonlinear Dynamic Gene Regulatory Networks via Short
Gene Expression Time Series",
journal = j-TCBB,
volume = "6",
number = "3",
pages = "410--419",
month = jul,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.5",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 11 18:13:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, the extended Kalman filter (EKF)
algorithm is applied to model the gene regulatory
network from gene time series data. The gene regulatory
network is considered as a nonlinear dynamic stochastic
model that consists of the gene measurement equation
and the gene regulation equation. After specifying the
model structure, we apply the EKF algorithm for
identifying both the model parameters and the actual
value of gene expression levels. It is shown that the
EKF algorithm is an online estimation algorithm that
can identify a large number of parameters (including
parameters of nonlinear functions) through iterative
procedure by using a small number of observations. Four
real-world gene expression data sets are employed to
demonstrate the effectiveness of the EKF algorithm, and
the obtained models are evaluated from the viewpoint of
bioinformatics.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "clustering; DNA microarray technology; extended Kalman
filtering; gene expression; Modeling; time series
data.",
}
@Article{Bryant:2009:CDT,
author = "David Bryant and Mike Steel",
title = "Computing the Distribution of a Tree Metric",
journal = j-TCBB,
volume = "6",
number = "3",
pages = "420--426",
month = jul,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.32",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 11 18:13:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The Robinson--Foulds (RF) distance is by far the most
widely used measure of dissimilarity between trees.
Although the distribution of these distances has been
investigated for 20 years, an algorithm that is
explicitly polynomial time has yet to be described for
computing the distribution for trees around a given
tree. In this paper, we derive a polynomial-time
algorithm for this distribution. We show how the
distribution can be approximated by a Poisson
distribution determined by the proportion of leaves
that lie in ``cherries'' of the given tree. We also
describe how our results can be used to derive
normalization constants that are required in a recently
proposed maximum likelihood approach to supertree
construction.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Biology and genetics; discrete mathematics
applications; normalization constant.; phylogenetics;
Poisson approximation; Robinson--Foulds distance;
trees",
}
@Article{Hulsman:2009:EOK,
author = "Marc Hulsman and Marcel J. T. Reinders and Dick de
Ridder",
title = "Evolutionary Optimization of Kernel Weights Improves
Protein Complex Comembership Prediction",
journal = j-TCBB,
volume = "6",
number = "3",
pages = "427--437",
month = jul,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.137",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 11 18:13:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In recent years, more and more high-throughput data
sources useful for protein complex prediction have
become available (e.g., gene sequence, mRNA expression,
and interactions). The integration of these different
data sources can be challenging. Recently, it has been
recognized that kernel-based classifiers are well
suited for this task. However, the different kernels
(data sources) are often combined using equal weights.
Although several methods have been developed to
optimize kernel weights, no large-scale example of an
improvement in classifier performance has been shown
yet. In this work, we employ an evolutionary algorithm
to determine weights for a larger set of kernels by
optimizing a criterion based on the area under the ROC
curve. We show that setting the right kernel weights
can indeed improve performance. We compare this to the
existing kernel weight optimization methods (i.e.,
(regularized) optimization of the SVM criterion or
aligning the kernel with an ideal kernel) and find that
these do not result in a significant performance
improvement and can even cause a decrease in
performance. Results also show that an expert approach
of assigning high weights to features with high
individual performance is not necessarily the best
strategy.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "biology and genetics; Classifier design and
evaluation; evolutionary computing and genetic
algorithms.",
}
@Article{Chen:2009:IAA,
author = "Zhi-Zhong Chen and Lusheng Wang",
title = "Improved Approximation Algorithms for Reconstructing
the History of Tandem Repeats",
journal = j-TCBB,
volume = "6",
number = "3",
pages = "438--453",
month = jul,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.122",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 11 18:13:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Some genetic diseases in human beings are dominated by
short sequences repeated consecutively called tandem
repeats. Once a region containing tandem repeats is
found, it is of great interest to study the history of
creating the repeats. The computational problem of
reconstructing the duplication history of tandem
repeats has been studied extensively in the literature.
Almost all previous studies focused on the simplest
case where the size of each duplication block is 1.
Only recently we succeeded in giving the first
polynomial-time approximation algorithm with a
guaranteed ratio for a more general case where the size
of each duplication block is at most $2$; the algorithm
achieves a ratio of $6$ and runs in $ O(n^{11}) $ time.
In this paper, we present two new polynomial-time
approximation algorithms for this more general case.
One of them achieves a ratio of $5$ and runs in $
O(n^9) $ time, while the other achieves a ratio of $
2.5 + \epsilon $ for any constant $ \epsilon > 0 $ but
runs slower.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "approximation algorithms.; Computational biology",
}
@Article{Cardona:2009:MPNb,
author = "Gabriel Cardona and Merce Llabres and Francesc
Rossello and Gabriel Valiente",
title = "Metrics for Phylogenetic Networks {II}: Nodal and
Triplets Metrics",
journal = j-TCBB,
volume = "6",
number = "3",
pages = "454--469",
month = jul,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.127",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 11 18:13:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The assessment of phylogenetic network reconstruction
methods requires the ability to compare phylogenetic
networks. This is the second in a series of papers
devoted to the analysis and comparison of metrics for
tree-child time consistent phylogenetic networks on the
same set of taxa. In this paper, we generalize to
phylogenetic networks two metrics that have already
been introduced in the literature for phylogenetic
trees: the nodal distance and the triplets distance. We
prove that they are metrics on any class of tree-child
time consistent phylogenetic networks on the same set
of taxa, as well as some basic properties for them. To
prove these results, we introduce a reduction/expansion
procedure that can be used not only to establish
properties of tree-child time consistent phylogenetic
networks by induction, but also to generate all
tree-child time consistent phylogenetic networks with a
given number of leaves.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "nodal distance; partition distance; Phylogenetic
network; temporal representation; time consistency;
tree-child phylogenetic network; triplets distance.",
}
@Article{Sotiropoulos:2009:MRM,
author = "Vassilios Sotiropoulos and Marrie-Nathalie
Contou-Carrere and Prodromos Daoutidis and Yiannis N.
Kaznessis",
title = "Model Reduction of Multiscale Chemical {Langevin}
Equations: a Numerical Case Study",
journal = j-TCBB,
volume = "6",
number = "3",
pages = "470--482",
month = jul,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.23",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 11 18:13:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Two very important characteristics of biological
reaction networks need to be considered carefully when
modeling these systems. First, models must account for
the inherent probabilistic nature of systems far from
the thermodynamic limit. Often, biological systems
cannot be modeled with traditional
continuous-deterministic models. Second, models must
take into consideration the disparate spectrum of time
scales observed in biological phenomena, such as slow
transcription events and fast dimerization reactions.
In the last decade, significant efforts have been
expended on the development of stochastic chemical
kinetics models to capture the dynamics of biomolecular
systems, and on the development of robust multiscale
algorithms, able to handle stiffness. In this paper,
the focus is on the dynamics of reaction sets governed
by stiff chemical Langevin equations, i.e., stiff
stochastic differential equations. These are
particularly challenging systems to model, requiring
prohibitively small integration step sizes. We describe
and illustrate the application of a semianalytical
reduction framework for chemical Langevin equations
that results in significant gains in computational
cost.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "chemical Langevin equations (CLEs); Model reduction;
multiscale models; stiff biomolecular systems.;
stochastic chemical kinetics",
}
@Article{Roytberg:2009:SSP,
author = "Mikhail Roytberg and Anna Gambin and Laurent Noe and
Slawomir Lasota and Eugenia Furletova and Ewa Szczurek
and Gregory Kucherov",
title = "On Subset Seeds for Protein Alignment",
journal = j-TCBB,
volume = "6",
number = "3",
pages = "483--494",
month = jul,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.4",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 11 18:13:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We apply the concept of subset seeds proposed in [1]
to similarity search in protein sequences. The main
question studied is the design of efficient seed
alphabets to construct seeds with optimal
sensitivity/selectivity trade-offs. We propose several
different design methods and use them to construct
several alphabets. We then perform a comparative
analysis of seeds built over those alphabets and
compare them with the standard Blastp seeding method
[2], [3], as well as with the family of vector seeds
proposed in [4]. While the formalism of subset seeds is
less expressive (but less costly to implement) than the
cumulative principle used in Blastp and vector seeds,
our seeds show a similar or even better performance
than Blastp on Bernoulli models of proteins compatible
with the common BLOSUM62 matrix. Finally, we perform a
large-scale benchmarking of our seeds against several
main databases of protein alignments. Here again, the
results show a comparable or better performance of our
seeds versus Blastp.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "local alignment; multiple seeds; protein databases;
Protein sequences; seed alphabet; seeds; selectivity.;
sensitivity; similarity search; subset seeds",
}
@Article{Jin:2009:PSP,
author = "Guohua Jin and Luay Nakhleh and Sagi Snir and Tamir
Tuller",
title = "Parsimony Score of Phylogenetic Networks: Hardness
Results and a Linear-Time Heuristic",
journal = j-TCBB,
volume = "6",
number = "3",
pages = "495--505",
month = jul,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.119",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 11 18:13:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Phylogenies --- the evolutionary histories of groups
of organisms --- play a major role in representing the
interrelationships among biological entities. Many
methods for reconstructing and studying such
phylogenies have been proposed, almost all of which
assume that the underlying history of a given set of
species can be represented by a binary tree. Although
many biological processes can be effectively modeled
and summarized in this fashion, others cannot:
recombination, hybrid speciation, and horizontal gene
transfer result in networks of relationships rather
than trees of relationships. In previous works, we
formulated a maximum parsimony (MP) criterion for
reconstructing and evaluating phylogenetic networks,
and demonstrated its quality on biological as well as
synthetic data sets. In this paper, we provide further
theoretical results as well as a very fast heuristic
algorithm for the MP criterion of phylogenetic
networks. In particular, we provide a novel
combinatorial definition of phylogenetic networks in
terms of ``forbidden cycles,'' and provide detailed
hardness and hardness of approximation proofs for the
``small'' MP problem. We demonstrate the performance of
our heuristic in terms of time and accuracy on both
biological and synthetic data sets. Finally, we explain
the difference between our model and a similar one
formulated by Nguyen et al., and describe the
implications of this difference on the hardness and
approximation results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "hardness and approximation.; horizontal gene transfer;
Maximum parsimony; phylogenetic networks",
}
@Article{Thomas:2009:PDS,
author = "John Thomas and Naren Ramakrishnan and Chris
Bailey-Kellogg",
title = "Protein Design by Sampling an Undirected Graphical
Model of Residue Constraints",
journal = j-TCBB,
volume = "6",
number = "3",
pages = "506--516",
month = jul,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.124",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 11 18:13:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper develops an approach for designing protein
variants by sampling sequences that satisfy residue
constraints encoded in an undirected probabilistic
graphical model. Due to evolutionary pressures on
proteins to maintain structure and function, the
sequence record of a protein family contains valuable
information regarding position-specific residue
conservation and coupling (or covariation) constraints.
Representing these constraints with a graphical model
provides two key benefits for protein design: a
probabilistic semantics enabling evaluation of possible
sequences for consistency with the constraints, and an
explicit factorization of residue dependence and
independence supporting efficient exploration of the
constrained sequence space. We leverage these benefits
in developing two complementary MCMC algorithms for
protein design: constrained shuffling mixes wild-type
sequences positionwise and evaluates graphical model
likelihood, while component sampling directly generates
sequences by sampling clique values and propagating to
other cliques. We apply our methods to design WW
domains. We demonstrate that likelihood under a model
of wild-type WWs is highly predictive of foldedness of
new WWs. We then show both theoretical and rapid
empirical convergence of our algorithms in generating
high-likelihood, diverse new sequences. We further show
that these sequences capture the original sequence
constraints, yielding a model as predictive of
foldedness as the original one.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "graphical models; Markov chain Monte Carlo (MCMC).;
Protein design; residue coupling",
}
@Article{Smith:2009:RSD,
author = "Jennifer A. Smith",
title = "{RNA} Search with Decision Trees and Partial
Covariance Models",
journal = j-TCBB,
volume = "6",
number = "3",
pages = "517--527",
month = jul,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.120",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 11 18:13:22 MDT 2009",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The use of partial covariance models to search for RNA
family members in genomic sequence databases is
explored. The partial models are formed from contiguous
subranges of the overall RNA family multiple alignment
columns. A binary decision-tree framework is presented
for choosing the order to apply the partial models and
the score thresholds on which to make the decisions.
The decision trees are chosen to minimize computation
time subject to the constraint that all of the training
sequences are passed to the full covariance model for
final evaluation. Computational intelligence methods
are suggested to select the decision tree since the
tree can be quite complex and there is no obvious
method to build the tree in these cases. Experimental
results from seven RNA families shows execution times
of 0.066-0.268 relative to using the full covariance
model alone. Tests on the full sets of known sequences
for each family show that at least 95 percent of these
sequences are found for two families and 100 percent
for five others. Since the full covariance model is run
on all sequences accepted by the partial model decision
tree, the false alarm rate is at least as low as that
of the full model alone.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Bioinformatics; computational intelligence; covariance
models; decision trees; RNA database search.",
}
@Article{Chen:2009:SCP,
author = "Jie Chen and Yu-Ping Wang",
title = "A Statistical Change Point Model Approach for the
Detection of {DNA} Copy Number Variations in Array
{CGH} Data",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "529--541",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.129",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Array comparative genomic hybridization (aCGH)
provides a high-resolution and high-throughput
technique for screening of copy number variations
(CNVs) within the entire genome. This technique,
compared to the conventional CGH, significantly
improves the identification of chromosomal
abnormalities. However, due to the random noise
inherited in the imaging and hybridization process,
identifying statistically significant DNA copy number
changes in aCGH data is challenging. We propose a novel
approach that uses the mean and variance change point
model (MVCM) to detect CNVs or breakpoints in aCGH data
sets. We derive an approximate p-value for the test
statistic and also give the estimate of the locus of
the DNA copy number change. We carry out simulation
studies to evaluate the accuracy of the estimate and
the p-value formulation. These simulation results show
that the approach is effective in identifying copy
number changes. The approach is also tested on
fibroblast cancer cell line data, breast tumor cell
line data, and breast cancer cell line aCGH data sets
that are publicly available. Changes that have not been
identified by the circular binary segmentation (CBS)
method but are biologically verified are detected by
our approach on these cell lines with higher
sensitivity and specificity than CBS.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Prakash:2009:ADM,
author = "Amol Prakash and Martin Tompa",
title = "Assessing the Discordance of Multiple Sequence
Alignments",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "542--551",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70271",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Multiple sequence alignments have wide applicability
in many areas of computational biology, including
comparative genomics, functional annotation of
proteins, gene finding, and modeling evolutionary
processes. Because of the computational difficulty of
multiple sequence alignment and the availability of
numerous tools, it is critical to be able to assess the
reliability of multiple alignments. We present a tool
called StatSigMA to assess whether multiple alignments
of nucleotide or amino acid sequences are contaminated
with one or more unrelated sequences. There are
numerous applications for which StatSigMA can be used.
Two such applications are to distinguish homologous
sequences from nonhomologous ones and to compare
alignments produced by various multiple alignment
tools. We present examples of both types of
applications.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cardona:2009:CTC,
author = "Gabriel Cardona and Francesc Rossello and Gabriel
Valiente",
title = "Comparison of Tree-Child Phylogenetic Networks",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "552--569",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70270",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Phylogenetic networks are a generalization of
phylogenetic trees that allow for the representation of
nontreelike evolutionary events, like recombination,
hybridization, or lateral gene transfer. While much
progress has been made to find practical algorithms for
reconstructing a phylogenetic network from a set of
sequences, all attempts to endorse a class of
phylogenetic networks (strictly extending the class of
phylogenetic trees) with a well-founded distance
measure have, to the best of our knowledge and with the
only exception of the bipartition distance on regular
networks, failed so far. In this paper, we present and
study a new meaningful class of phylogenetic networks,
called tree-child phylogenetic networks, and we provide
an injective representation of these networks as
multisets of vectors of natural numbers, their path
multiplicity vectors. We then use this representation
to define a distance on this class that extends the
well-known Robinson--Foulds distance for phylogenetic
trees and to give an alignment method for pairs of
networks in this class. Simple polynomial algorithms
for reconstructing a tree-child phylogenetic network
from its path multiplicity vectors, for computing the
distance between two tree-child phylogenetic networks
and for aligning a pair of tree-child phylogenetic
networks, are provided. They have been implemented as a
Perl package and a Java applet, which can be found at
http://bioinfo.uib.es/~recerca/phylonetworks/mudistance/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hong:2009:HRD,
author = "Changjin Hong and Ahmed H. Tewfik",
title = "Heuristic Reusable Dynamic Programming: Efficient
Updates of Local Sequence Alignment",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "570--582",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.30",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recomputation of the previously evaluated similarity
results between biological sequences becomes inevitable
when researchers realize errors in their sequenced data
or when the researchers have to compare nearly similar
sequences, e.g., in a family of proteins. We present an
efficient scheme for updating local sequence alignments
with an affine gap model. In principle, using the
previous matching result between two amino acid
sequences, we perform a forward-backward alignment to
generate heuristic searching bands which are bounded by
a set of suboptimal paths. Given a correctly updated
sequence, we initially predict a new score of the
alignment path for each contour to select the best
candidates among them. Then, we run the Smith-Waterman
algorithm in this confined space. Furthermore, our
heuristic alignment for an updated sequence shows that
it can be further accelerated by using reusable dynamic
programming (rDP), our prior work. In this study, we
successfully validate ``relative node tolerance bound''
(RNTB) in the pruned searching space. Furthermore, we
improve the computational performance by quantifying
the successful RNTB tolerance probability and switch to
rDP on perturbation-resilient columns only. In our
searching space derived by a threshold value of 90
percent of the optimal alignment score, we find that
98.3 percent of contours contain correctly updated
paths. We also find that our method consumes only 25.36
percent of the runtime cost of sparse dynamic
programming (sDP) method, and to only 2.55 percent of
that of a normal dynamic programming with the
Smith-Waterman algorithm.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2009:EPS,
author = "Yong Wang and Wu Ling-Yun and Ji-Hong Zhang and
Zhong-Wei Zhan and Zhang Xiang-Sun and Chen Luonan",
title = "Evaluating Protein Similarity from Coarse Structures",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "583--593",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70250",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "To unscramble the relationship between protein
function and protein structure, it is essential to
assess the protein similarity from different aspects.
Although many methods have been proposed for protein
structure alignment or comparison, alternative
similarity measures are still strongly demanded due to
the requirement of fast screening and query in
large-scale structure databases. In this paper, we
first formulate a novel representation of a protein
structure, i.e., Feature Sequence of Surface (FSS).
Then, a new score scheme is developed to measure the
similarity between two representations. To verify the
proposed method, numerical experiments are conducted in
four different protein data sets. We also classify SARS
coronavirus to verify the effectiveness of the new
method. Furthermore, preliminary results of fast
classification of the whole CATH v2.5.1 database based
on the new macrostructure similarity are given as a
pilot study. We demonstrate that the proposed approach
to measure the similarities between protein structures
is simple to implement, computationally efficient, and
surprisingly fast. In addition, the method itself
provides a new and quantitative tool to view a protein
structure.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Salicru:2009:ICA,
author = "Miquel Salicru and Sergi Vives and Tian Zheng",
title = "Inferential Clustering Approach for Microarray
Experiments with Replicated Measurements",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "594--604",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.106",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Cluster analysis has proven to be a useful tool for
investigating the association structure among genes in
a microarray data set. There is a rich literature on
cluster analysis and various techniques have been
developed. Such analyses heavily depend on an
appropriate (dis)similarity measure. In this paper, we
introduce a general clustering approach based on the
confidence interval inferential methodology, which is
applied to gene expression data of microarray
experiments. Emphasis is placed on data with low
replication (three or five replicates). The proposed
method makes more efficient use of the measured data
and avoids the subjective choice of a dissimilarity
measure. This new methodology, when applied to real
data, provides an easy-to-use bioinformatics solution
for the cluster analysis of microarray experiments with
replicates (see the Appendix). Even though the method
is presented under the framework of microarray
experiments, it is a general algorithm that can be used
to identify clusters in any situation. The method's
performance is evaluated using simulated and publicly
available data set. Our results also clearly show that
our method is not an extension of the conventional
clustering method based on correlation or euclidean
distance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Niijima:2009:LLD,
author = "Satoshi Niijima and Yasushi Okuno",
title = "{Laplacian} Linear Discriminant Analysis Approach to
Unsupervised Feature Selection",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "605--614",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70257",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Until recently, numerous feature selection techniques
have been proposed and found wide applications in
genomics and proteomics. For instance, feature/gene
selection has proven to be useful for biomarker
discovery from microarray and mass spectrometry data.
While supervised feature selection has been explored
extensively, there are only a few unsupervised methods
that can be applied to exploratory data analysis. In
this paper, we address the problem of unsupervised
feature selection. First, we extend Laplacian linear
discriminant analysis (LLDA) to unsupervised cases.
Second, we propose a novel algorithm for computing
LLDA, which is efficient in the case of high
dimensionality and small sample size as in microarray
data. Finally, an unsupervised feature selection
method, called LLDA-based Recursive Feature Elimination
(LLDA-RFE), is proposed. We apply LLDA-RFE to several
public data sets of cancer microarrays and compare its
performance with those of Laplacian score and
SVD-entropy, two state-of-the-art unsupervised methods,
and with that of Fisher score, a supervised filter
method. Our results demonstrate that LLDA-RFE
outperforms Laplacian score and shows favorable
performance against SVD-entropy. It performs even
better than Fisher score for some of the data sets,
despite the fact that LLDA-RFE is fully unsupervised.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rasmussen:2009:MVU,
author = "Carl Rasmussen and Bernard de la Cruz and Zoubin
Ghahramani and David Wild",
title = "Modeling and Visualizing Uncertainty in Gene
Expression Clusters Using {Dirichlet} Process
Mixtures",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "615--628",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70269",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Although the use of clustering methods has rapidly
become one of the standard computational approaches in
the literature of microarray gene expression data,
little attention has been paid to uncertainty in the
results obtained. Dirichlet process mixture (DPM)
models provide a nonparametric Bayesian alternative to
the bootstrap approach to modeling uncertainty in gene
expression clustering. Most previously published
applications of Bayesian model-based clustering methods
have been to short time series data. In this paper, we
present a case study of the application of
nonparametric Bayesian clustering methods to the
clustering of high-dimensional nontime series gene
expression data using full Gaussian covariances. We use
the probability that two genes belong to the same
cluster in a DPM model as a measure of the similarity
of these gene expression profiles. Conversely, this
probability can be used to define a dissimilarity
measure, which, for the purposes of visualization, can
be input to one of the standard linkage algorithms used
for hierarchical clustering. Biologically plausible
results are obtained from the Rosetta compendium of
expression profiles which extend previously published
cluster analyses of this data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cardona:2009:NMR,
author = "Gabriel Cardona and Merce Llabres and Francesc
Rossello and Gabriel Valiente",
title = "On {Nakhleh}'s Metric for Reduced Phylogenetic
Networks",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "629--638",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.33",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We prove that Nakhleh's metric for reduced
phylogenetic networks is also a metric on the classes
of tree-child phylogenetic networks, semibinary
tree-sibling time consistent phylogenetic networks, and
multilabeled phylogenetic trees. We also prove that it
separates distinguishable phylogenetic networks. In
this way, it becomes the strongest dissimilarity
measure for phylogenetic networks available so far.
Furthermore, we propose a generalization of that metric
that separates arbitrary phylogenetic networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chowriappa:2009:PSC,
author = "Pradeep Chowriappa and Sumeet Dua and Jinko Kanno and
Hilary W. Thompson",
title = "Protein Structure Classification Based on Conserved
Hydrophobic Residues",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "639--651",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.77",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein folding is frequently guided by local residue
interactions that form clusters in the protein core.
The interactions between residue clusters serve as
potential nucleation sites in the folding process.
Evidence postulates that the residue interactions are
governed by the hydrophobic propensities that the
residues possess. An array of hydrophobicity scales has
been developed to determine the hydrophobic
propensities of residues under different environmental
conditions. In this work, we propose a
graph-theory-based data mining framework to extract and
isolate protein structural features that sustain
invariance in evolutionary-related proteins, through
the integrated analysis of five well-known
hydrophobicity scales over the 3D structure of
proteins. We hypothesize that proteins of the same
homology contain conserved hydrophobic residues and
exhibit analogous residue interaction patterns in the
folded state. The results obtained demonstrate that
discriminatory residue interaction patterns shared
among proteins of the same family can be employed for
both the structural and the functional annotation of
proteins. We obtained on the average 90 percent
accuracy in protein classification with a significantly
small feature vector compared to previous results in
the area. This work presents an elaborate study, as
well as validation evidence, to illustrate the efficacy
of the method and the correctness of results
reported.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Uehara:2009:PDC,
author = "Hiroaki Uehara and Masakazu Jimbo",
title = "A Positive Detecting Code and Its Decoding Algorithm
for {DNA} Library Screening",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "652--666",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70266",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The study of gene functions requires high-quality DNA
libraries. However, a large number of tests and
screenings are necessary for compiling such libraries.
We describe an algorithm for extracting as much
information as possible from pooling experiments for
library screening. Collections of clones are called
pools, and a pooling experiment is a group test for
detecting all positive clones. The probability of
positiveness for each clone is estimated according to
the outcomes of the pooling experiments. Clones with
high chance of positiveness are subjected to
confirmatory testing. In this paper, we introduce a new
positive clone detecting algorithm, called the Bayesian
network pool result decoder (BNPD). The performance of
BNPD is compared, by simulation, with that of the
Markov chain pool result decoder (MCPD) proposed by
Knill et al. in 1996. Moreover, the combinatorial
properties of pooling designs suitable for the proposed
algorithm are discussed in conjunction with
combinatorial designs and d\hbox{-}{\rm disjunct}
matrices. We also show the advantage of utilizing
packing designs or BIB designs for the BNPD
algorithm.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{vanIersel:2009:CLP,
author = "Leo van Iersel and Judith Keijsper and Steven Kelk and
Leen Stougie and Ferry Hagen and Teun Boekhout",
title = "Constructing Level-2 Phylogenetic Networks from
Triplets",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "667--681",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.22",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Jansson and Sung showed that, given a dense set of
input triplets T (representing hypotheses about the
local evolutionary relationships of triplets of taxa),
it is possible to determine in polynomial time whether
there exists a level-1 network consistent with T, and
if so, to construct such a network [24]. Here, we
extend this work by showing that this problem is even
polynomial time solvable for the construction of
level-2 networks. This shows that, assuming density, it
is tractable to construct plausible evolutionary
histories from input triplets even when such histories
are heavily nontree-like. This further strengthens the
case for the use of triplet-based methods in the
construction of phylogenetic networks. We also
implemented the algorithm and applied it to yeast
data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mneimneh:2009:AOS,
author = "Saad Mneimneh",
title = "On the Approximation of Optimal Structures for
{RNA--RNA} Interaction",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "682--688",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2007.70258",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The interaction of two RNA molecules is a common
mechanism for many biological processes. Small
interfering RNAs represent a simple example of such an
interaction. But other more elaborate instances of
RNA-RNA interaction exist. Therefore, algorithms that
predict the structure of the RNA complex thus formed
are of great interest. Most of the proposed algorithms
are based on dynamic programming. RNA-RNA interaction
is generally NP-complete; therefore, these algorithms
(and other polynomial time algorithms for that matter)
are not expected to produce optimal structures. Our
goal is to characterize this suboptimality. We
demonstrate the existence of constant factor
approximation algorithms that are based on dynamic
programming. In particular, we describe 1/2 and 2/3
factor approximation algorithms. We define an entangler
and prove that 2/3 is a theoretical upper bound on the
approximation factor of algorithms that produce
entangler-free solutions, e.g., the mentioned dynamic
programming algorithms.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Diago:2009:EGC,
author = "Luis A. Diago and Ernesto Moreno",
title = "Evaluation of Geometric Complementarity between
Molecular Surfaces Using Compactly Supported Radial
Basis Functions",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "689--694",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.31",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "One of the challenges faced by all molecular docking
algorithms is that of being able to discriminate
between correct results and false positives obtained in
the simulations. The scoring or energetic function is
the one that must fulfill this task. Several scoring
functions have been developed and new methodologies are
still under development. In this paper, we have
employed the Compactly Supported Radial Basis Functions
(CSRBF) to create analytical representations of
molecular surfaces, which are then included as key
components of a new scoring function for molecular
docking. The method proposed here achieves a better
ranking of the solutions produced by the program DOCK,
as compared with the ranking done by its native contact
scoring function. Our new analytical scoring function
based on CSRBF can be easily included in different
available docking programs as a reliable and quick
filter in large-scale docking simulations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gonzalez:2009:MLT,
author = "Ana M. Gonzalez and Francisco J. Azuaje and Jose L.
Ramirez and Jose F. da Silveira and Jose R.
Dorronsoro",
title = "Machine Learning Techniques for the Automated
Classification of Adhesin-Like Proteins in the Human
Protozoan Parasite \bioname{Trypanosoma cruzi}",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "695--702",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.125",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper reports on the evaluation of different
machine learning techniques for the automated
classification of coding gene sequences obtained from
several organisms in terms of their functional role as
adhesins. Diverse, biologically-meaningful,
sequence-based features were extracted from the
sequences and used as inputs to the in silico
prediction models. Another contribution of this work is
the generation of potentially novel and testable
predictions about the surface protein DGF-1 family in
Trypanosoma cruzi. Finally, these techniques are
potentially useful for the automated annotation of
known adhesin-like proteins from the trans-sialidase
surface protein family in T. cruzi, the etiological
agent of Chagas disease.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2009:CPS,
author = "Anonymous",
title = "Call for Papers: Special Issue of Transactions in
Computational Biology and Bioinformatics: Special Issue
on {BioCreative II.5}",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "703",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.73",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2009:TAI,
author = "Anonymous",
title = "2009 {TCBB} Annual Index",
journal = j-TCBB,
volume = "6",
number = "4",
pages = "Not in Print",
month = oct,
year = "2009",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.72",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 1 16:16:42 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2010:EN,
author = "Anonymous",
title = "{Editor}'s Note",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "1--1",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Diaz:2010:ADL,
author = "Ester Diaz and Guillermo Ayala and Maria
Diaz-Fernandez and Liang Gong and Derek Toomre",
title = "Automatic Detection of Large Dense-Core Vesicles in
Secretory Cells and Statistical Analysis of Their
Intracellular Distribution",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "2--11",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lushbough:2010:BSI,
author = "Carol Lushbough and Michael K. Bergman and Carolyn J.
Lawrence and Doug Jennewein and Volker Brendel",
title = "{BioExtract Server} --- an Integrated
Workflow-Enabling System to Access and Analyze
Heterogeneous, Distributed Biomolecular Data",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "12--24",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhu:2010:FSG,
author = "Shenghuo Zhu and Dingding Wang and Kai Yu and Tao Li
and Yihong Gong",
title = "Feature Selection for Gene Expression Using
Model-Based Entropy",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "25--36",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pehkonen:2010:HBS,
author = "Petri Pehkonen and Garry Wong and Petri Toronen",
title = "Heuristic {Bayesian} Segmentation for Discovery of
Coexpressed Genes within Genomic Regions",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "37--49",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kustra:2010:DFC,
author = "Rafal Kustra and Adam Zagdanski",
title = "Data-Fusion in Clustering Microarray Data: Balancing
Discovery and Interpretability",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "50--63",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rubel:2010:IDC,
author = "Oliver Rubel and Gunther H. Weber and Min-Yu Huang and
E. Wes Bethel and Mark D. Biggin and Charless C.
Fowlkes and Cris L. Luengo Hendriks and Soile V. E.
Keranen and Michael B. Eisen and David W. Knowles and
Jitendra Malik and Hans Hagen and Bernd Hamann",
title = "Integrating Data Clustering and Visualization for the
Analysis of {$3$D} Gene Expression Data",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "64--79",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Han:2010:MPC,
author = "Ju Han and Hang Chang and Kumari Andarawewa and Paul
Yaswen and Mary Helen Barcellos-Hoff and Bahram
Parvin",
title = "Multidimensional Profiling of Cell Surface Proteins
and Nuclear Markers",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "80--90",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Done:2010:PNH,
author = "Bogdan Done and Purvesh Khatri and Arina Done and
Sorin Draghici",
title = "Predicting Novel Human Gene Ontology Annotations Using
Semantic Analysis",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "91--99",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2010:SSV,
author = "Zhenqiu Liu and Shili Lin and Ming Tan",
title = "Sparse Support Vector Machines with {$ L_p $} Penalty
for Biomarker Identification",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "100--107",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Leung:2010:MFM,
author = "Yukyee Leung and Yeungsam Hung",
title = "A Multiple-Filter-Multiple-Wrapper Approach to Gene
Selection and Microarray Data Classification",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "108--117",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Perkins:2010:TBS,
author = "Theodore J. Perkins and Michael T. Hallett",
title = "A Trade-Off between Sample Complexity and
Computational Complexity in Learning {Boolean} Networks
from Time-Series Data",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "118--125",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pelikan:2010:EPL,
author = "Richard Pelikan and Milos Hauskrecht",
title = "Efficient Peak-Labeling Algorithms for Whole-Sample
Mass Spectrometry Proteomics",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "126--137",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mahata:2010:ECH,
author = "Pritha Mahata",
title = "Exploratory Consensus of Hierarchical Clusterings for
Melanoma and Breast Cancer",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "138--152",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Madeira:2010:IRM,
author = "Sara C. Madeira and Miguel C. Teixeira and Isabel
Sa-Correia and Arlindo L. Oliveira",
title = "Identification of Regulatory Modules in Time Series
Gene Expression Data Using a Linear Time Biclustering
Algorithm",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "153--165",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mossel:2010:ILS,
author = "Elchanan Mossel and Sebastien Roch",
title = "Incomplete Lineage Sorting: Consistent Phylogeny
Estimation from Multiple Loci",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "166--171",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Freitas:2010:ICC,
author = "Alex A. Freitas and Daniela C. Wieser and Rolf
Apweiler",
title = "On the Importance of Comprehensible Classification
Models for Protein Function Prediction",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "172--182",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Alon:2010:AMP,
author = "Noga Alon and Benny Chor and Fabio Pardi and Anat
Rapoport",
title = "Approximate Maximum Parsimony and Ancestral Maximum
Likelihood",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "183--187",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2010:RL,
author = "Anonymous",
title = "2009 Reviewer's List",
journal = j-TCBB,
volume = "7",
number = "1",
pages = "188--190",
month = jan,
year = "2010",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 15 18:56:53 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sagot:2010:EE,
author = "Marie-France Sagot",
title = "{EIC} Editorial",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "193--194",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.29",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lonardi:2010:DMB,
author = "Stefano Lonardi and Jake Chen",
title = "Data Mining in Bioinformatics: Selected Papers from
{BIOKDD}",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "195--196",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.28",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Smalter:2010:GGP,
author = "Aaron Smalter and Jun Huan and Yi Jia and Gerald
Lushington",
title = "{GPD}: a Graph Pattern Diffusion Kernel for Accurate
Graph Classification with Applications in
Cheminformatics",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "197--207",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.80",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Graph data mining is an active research area. Graphs
are general modeling tools to organize information from
heterogeneous sources and have been applied in many
scientific, engineering, and business fields. With the
fast accumulation of graph data, building highly
accurate predictive models for graph data emerges as a
new challenge that has not been fully explored in the
data mining community. In this paper, we demonstrate a
novel technique called graph pattern diffusion (GPD)
kernel. Our idea is to leverage existing frequent
pattern discovery methods and to explore the
application of kernel classifier (e.g., support vector
machine) in building highly accurate graph
classification. In our method, we first identify all
frequent patterns from a graph database. We then map
subgraphs to graphs in the graph database and use a
process we call ``pattern diffusion'' to label nodes in
the graphs. Finally, we designed a graph alignment
algorithm to compute the inner product of two graphs.
We have tested our algorithm using a number of chemical
structure data. The experimental results demonstrate
that our method is significantly better than competing
methods such as those kernel functions based on paths,
cycles, and subgraphs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "frequent subgraph mining.; graph alignment; Graph
classification",
}
@Article{Bogdanov:2010:MFP,
author = "Petko Bogdanov and Ambuj K. Singh",
title = "Molecular Function Prediction Using Neighborhood
Features",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "208--217",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.81",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The recent advent of high-throughput methods has
generated large amounts of gene interaction data. This
has allowed the construction of genomewide networks. A
significant number of genes in such networks remain
uncharacterized and predicting the molecular function
of these genes remains a major challenge. A number of
existing techniques assume that genes with similar
functions are topologically close in the network. Our
hypothesis is that genes with similar functions observe
similar annotation patterns in their neighborhood,
regardless of the distance between them in the
interaction network. We thus predict molecular
functions of uncharacterized genes by comparing their
functional neighborhoods to genes of known function. We
propose a two-phase approach. First, we extract
functional neighborhood features of a gene using Random
Walks with Restarts. We then employ a KNN classifier to
predict the function of uncharacterized genes based on
the computed neighborhood features. We perform
leave-one-out validation experiments on two $S$.
cerevisiae interaction networks and show significant
improvements over previous techniques. Our technique
provides a natural control of the trade-off between
accuracy and coverage of prediction. We further propose
and evaluate prediction in sparse genomes by exploiting
features from well-annotated genomes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "classification; feature extraction; functional
interaction network.; Gene function prediction",
}
@Article{Nakhleh:2010:MSR,
author = "Luay Nakhleh",
title = "A Metric on the Space of Reduced Phylogenetic
Networks",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "218--222",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.2",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Phylogenetic networks are leaf-labeled, rooted,
acyclic, and directed graphs that are used to model
reticulate evolutionary histories. Several measures for
quantifying the topological dissimilarity between two
phylogenetic networks have been devised, each of which
was proven to be a metric on certain restricted classes
of phylogenetic networks. A biologically motivated
class of phylogenetic networks, namely, reduced
phylogenetic networks, was recently introduced. None of
the existing measures is a metric on the space of
reduced phylogenetic networks. In this paper, we
provide a metric on the space of reduced phylogenetic
networks that is computable in time polynomial in the
size of the networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "indistinguishability; metric.; phylogenetic network;
Phylogeny; reduced phylogenetic network",
}
@Article{Gupta:2010:AHD,
author = "Gunjan Gupta and Alexander Liu and Joydeep Ghosh",
title = "Automated Hierarchical Density Shaving: a Robust
Automated Clustering and Visualization Framework for
Large Biological Data Sets",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "223--237",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.32",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A key application of clustering data obtained from
sources such as microarrays, protein mass spectroscopy,
and phylogenetic profiles is the detection of
functionally related genes. Typically, only a small
number of functionally related genes cluster into one
or more groups, and the rest need to be ignored. For
such situations, we present Automated Hierarchical
Density Shaving (Auto-HDS), a framework that consists
of a fast hierarchical density-based clustering
algorithm and an unsupervised model selection strategy.
Auto-HDS can automatically select clusters of different
densities, present them in a compact hierarchy, and
rank individual clusters using an innovative stability
criteria. Our framework also provides a simple yet
powerful 2D visualization of the hierarchy of clusters
that is useful for further interactive exploration. We
present results on Gasch and Lee microarray data sets
to show the effectiveness of our methods. Additional
results on other biological data are included in the
supplemental material.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "bioinformatics.; clustering; data and knowledge
visualization; Mining methods and algorithms",
}
@Article{Raiford:2010:AIT,
author = "Douglas W. Raiford and Dan E. Krane and Travis E. Doom
and Michael L. Raymer",
title = "Automated Isolation of Translational Efficiency Bias
That Resists the Confounding Effect of
{GC(AT)}-Content",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "238--250",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.65",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genomic sequencing projects are an abundant source of
information for biological studies ranging from the
molecular to the ecological in scale; however, much of
the information present may yet be hidden from casual
analysis. One such information domain, trends in codon
usage, can provide a wealth of information about an
organism's genes and their expression. Degeneracy in
the genetic code allows more than one triplet codon to
code for the same amino acid, and usage of these codons
is often biased such that one or more of these
synonymous codons are preferred. Detection of this bias
is an important tool in the analysis of genomic data,
particularly as a predictor of gene expressivity.
Methods for identifying codon usage bias in genomic
data that rely solely on genomic sequence data are
susceptible to being confounded by the presence of
several factors simultaneously influencing codon
selection. Presented here is a new technique for
removing the effects of one of the more common
confounding factors, GC(AT)-content, and of visualizing
the search-space for codon usage bias through the use
of a solution landscape. This technique successfully
isolates expressivity-related codon usage trends, using
only genomic sequence information, where other
techniques fail due to the presence of GC(AT)-content
confounding influences.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Codon usage bias; GC-content; strand bias;
translational efficiency.",
}
@Article{Tenenhaus:2010:GAN,
author = "Arthur Tenenhaus and Vincent Guillemot and Xavier
Gidrol and Vincent Frouin",
title = "Gene Association Networks from Microarray Data Using a
Regularized Estimation of Partial Correlation Based on
{PLS} Regression",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "251--262",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.87",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Reconstruction of gene-gene interactions from
large-scale data such as microarrays is a first step
toward better understanding the mechanisms at work in
the cell. Two main issues have to be managed in such a
context: (1) choosing which measures have to be used to
distinguish between direct and indirect interactions
from high-dimensional microarray data and (2)
constructing networks with a low proportion of
false-positive edges. We present an efficient
methodology for the reconstruction of gene interaction
networks in a small-sample-size setting. The strength
of independence of any two genes is measured, in such
`high-dimensional network,' by a regularized estimation
of partial correlation based on Partial Least Squares
Regression. We finally emphasize specific properties of
the proposed method. To assess the sensitivity and
specificity of the method, we carried out the
reconstruction of networks from simulated data. We also
tested PLS-based partial correlation network on static
and dynamic real microarray data. An R implementation
of the proposed algorithm is available from
\path=http://biodev.extra.cea.fr/plspcnetwork/=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Gene association networks; high-dimensional data;
local false discovery rate.; partial correlation;
Partial Least Squares Regression",
}
@Article{Zhu:2010:IFP,
author = "Zexuan Zhu and Yew-Soon Ong and Jacek M. Zurada",
title = "Identification of Full and Partial Class Relevant
Genes",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "263--277",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.105",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Multiclass cancer classification on microarray data
has provided the feasibility of cancer diagnosis across
all of the common malignancies in parallel. Using
multiclass cancer feature selection approaches, it is
now possible to identify genes relevant to a set of
cancer types. However, besides identifying the relevant
genes for the set of all cancer types, it is deemed to
be more informative to biologists if the relevance of
each gene to specific cancer or subset of cancer types
could be revealed or pinpointed. In this paper, we
introduce two new definitions of multiclass relevancy
features, i.e., full class relevant (FCR) and partial
class relevant (PCR) features. Particularly, FCR
denotes genes that serve as candidate biomarkers for
discriminating all cancer types. PCR, on the other
hand, are genes that distinguish subsets of cancer
types. Subsequently, a Markov blanket embedded memetic
algorithm is proposed for the simultaneous
identification of both FCR and PCR genes. Results
obtained on commonly used synthetic and real-world
microarray data sets show that the proposed approach
converges to valid FCR and PCR genes that would assist
biologists in their research work. The identification
of both FCR and PCR genes is found to generate
improvement in classification accuracy on many
microarray data sets. Further comparison study to
existing state-of-the-art feature selection algorithms
also reveals the effectiveness and efficiency of the
proposed approach.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Bioinformatics; feature/gene selection; Markov
blanket.; memetic algorithm; microarray; multiclass
cancer classification",
}
@Article{Randhawa:2010:MCM,
author = "Ranjit Randhawa and Cliff Shaffer and John Tyson",
title = "Model Composition for Macromolecular Regulatory
Networks",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "278--287",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.64",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Models of regulatory networks become more difficult to
construct and understand as they grow in size and
complexity. Large models are usually built up from
smaller models, representing subsets of reactions
within the larger network. To assist modelers in this
composition process, we present a formal approach for
model composition, a wizard-style program for
implementing the approach, and suggested language
extensions to the Systems Biology Markup Language to
support model composition. To illustrate the features
of our approach and how to use the JigCell Composition
Wizard, we build up a model of the eukaryotic cell
cycle ``engine'' from smaller pieces.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "composition; flattening; fusion; Modeling; SBML.",
}
@Article{Bokhari:2010:RNI,
author = "Shahid H. Bokhari and Daniel Janies",
title = "Reassortment Networks for Investigating the Evolution
of Segmented Viruses",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "288--298",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.73",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Many viruses of interest, such as influenza A, have
distinct segments in their genome. The evolution of
these viruses involves mutation and reassortment, where
segments are interchanged between viruses that coinfect
a host. Phylogenetic trees can be constructed to
investigate the mutation-driven evolution of individual
viral segments. However, reassortment events among
viral genomes are not well depicted in such bifurcating
trees. We propose the concept of reassortment networks
to analyze the evolution of segmented viruses. These
are layered graphs in which the layers represent
evolutionary stages such as a temporal series of
seasons in which influenza viruses are isolated. Nodes
represent viral isolates and reassortment events
between pairs of isolates. Edges represent evolutionary
steps, while weights on edges represent edit costs of
reassortment and mutation events. Paths represent
possible transformation series among viruses. The
length of each path is the sum edit cost of the events
required to transform one virus into another. In order
to analyze $ \tau $ stages of evolution of $n$ viruses
with segments of maximum length $m$, we first compute
the pairwise distances between all corresponding
segments of all viruses in $ {\cal O}(m^2 n^2) $ time
using dynamic programming. The reassortment network,
with $ {\cal O}(\tau n^2) $ nodes, is then constructed
using these distances. The ancestors and descendents of
a specific virus can be traced via shortest paths in
this network, which can be found in $ {\cal O}(\tau
n^3) $ time.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "dynamic programming; Influenza A; reassortment;
segmented virus; shortest paths.",
}
@Article{Bergemann:2010:SQM,
author = "Tracy L. Bergemann and Lue Ping Zhao",
title = "Signal Quality Measurements for {cDNA} Microarray
Data",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "299--308",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.72",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Concerns about the reliability of expression data from
microarrays inspire ongoing research into measurement
error in these experiments. Error arises at both the
technical level within the laboratory and the
experimental level. In this paper, we will focus on
estimating the spot-specific error, as there are few
currently available models. This paper outlines two
different approaches to quantify the reliability of
spot-specific intensity estimates. In both cases, the
spatial correlation between pixels and its impact on
spot quality is accounted for. The first method is a
straightforward parametric estimate of within-spot
variance that assumes a Gaussian distribution and
accounts for spatial correlation via an overdispersion
factor. The second method employs a nonparametric
quality estimate referred to throughout as the mean
square prediction error (MSPE). The MSPE first smoothes
a pixel region and then measures the difference between
actual pixel values and the smoother. Both methods
herein are compared for real and simulated data to
assess numerical characteristics and the ability to
describe poor spot quality. We conclude that both
approaches capture noise in the microarray platform and
highlight situations where one method or the other is
superior.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "image analysis.; Microarray; prediction error; signal
quality",
}
@Article{Blin:2010:ARS,
author = "Guillaume Blin and Alain Denise and Serge Dulucq and
Claire Herrbach and Heleene Touzet",
title = "Alignments of {RNA} Structures",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "309--322",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.28",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We describe a theoretical unifying framework to
express the comparison of RNA structures, which we call
alignment hierarchy. This framework relies on the
definition of common supersequences for arc-annotated
sequences and encompasses the main existing models for
RNA structure comparison based on trees and
arc-annotated sequences with a variety of edit
operations. It also gives rise to edit models that have
not been studied yet. We provide a thorough analysis of
the alignment hierarchy, including a new
polynomial-time algorithm and an NP-completeness proof.
The polynomial-time algorithm involves biologically
relevant edit operations such as pairing or unpairing
nucleotides. It has been implemented in a software,
called {\tt gardenia}, which is available at the Web
server \path=http://bioinfo.lifl.fr/RNA/gardenia=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "algorithm.; arc-annotated sequences; Computational
biology; edit distance; NP-hardness; RNA structures",
}
@Article{Jiang:2010:AAP,
author = "Minghui Jiang",
title = "Approximation Algorithms for Predicting {RNA}
Secondary Structures with Arbitrary Pseudoknots",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "323--332",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.109",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We study three closely related problems motivated by
the prediction of RNA secondary structures with
arbitrary pseudoknots: the problem 2-Interval Pattern
proposed by Vialette [CHECK END OF SENTENCE], the
problem Maximum Base Pair Stackings proposed by Leong
et al. [CHECK END OF SENTENCE], and the problem Maximum
Stacking Base Pairs proposed by Lyngs. [CHECK END OF
SENTENCE]. For the 2-Interval Pattern, we present
polynomial-time approximation algorithms for the
problem over the preceding-and-crossing model and on
input with the unitary restriction. For Maximum Base
Pair Stackings and Maximum Stacking Base Pairs, we
present polynomial-time approximation algorithms for
the two problems on explicit input of candidate base
pairs. We also propose a new problem called
Length-Weighted Balanced 2-Interval Pattern, which is
natural in the context of RNA secondary structure
prediction.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "2-intervals.; RNA secondary structure prediction;
stacking pairs",
}
@Article{Shibuya:2010:FHD,
author = "Tetsuo Shibuya",
title = "Fast Hinge Detection Algorithms for Flexible Protein
Structures",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "333--341",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.62",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Analysis of conformational changes is one of the keys
to the understanding of protein functions and
interactions. For the analysis, we often compare two
protein structures, taking flexible regions like hinge
regions into consideration. The Root Mean Square
Deviation (RMSD) is the most popular measure for
comparing two protein structures, but it is only for
rigid structures without hinge regions. In this paper,
we propose a new measure called RMSD considering hinges
(RMSDh) and its variant {\rm RMSDh}$^{(k)}$ for
comparing two flexible proteins with hinge regions. We
also propose novel efficient algorithms for computing
them, which can detect the hinge positions at the same
time. The RMSDh is suitable for cases where there is
one small hinge region in each of the two target
structures. The new algorithm for computing the RMSDh
runs in linear time, which is the same as the time
complexity for computing the RMSD and is faster than
any of previous algorithms for hinge detection. The
{\rm RMSDh}$^{(k)}$ is designed for comparing
structures with more than one hinge region. The {\rm
RMSDh}$^{(k)}$ measure considers at most $k$ small
hinge region, i.e., the {\rm RMSDh}$^{(k)}$ value
should be small if the two structures are similar
except for at most $k$ hinge regions. To compute the
value, we propose an $ O(k n^2) $-time and $ O(n)
$-space algorithm based on a new dynamic programming
technique. With the same computational time and space,
we can enumerate the predicted hinge positions. We also
test our algorithms against actual flexible protein
structures, and show that the hinge positions can be
correctly detected by our algorithms.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "Algorithm; dynamic programming.; protein 3D structure
comparison; protein hinge detection",
}
@Article{Guillemot:2010:FPT,
author = "Sylvain Guillemot and Vincent Berry",
title = "Fixed-Parameter Tractability of the Maximum Agreement
Supertree Problem",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "342--353",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.93",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Given a set $L$ of labels and a collection of rooted
trees whose leaves are bijectively labeled by some
elements of $L$, the Maximum Agreement Supertree
(SMAST) problem is given as follows: find a tree $T$ on
a largest label set $ L' \subseqeq L $ that
homeomorphically contains every input tree restricted
to $ L' $. The problem has phylogenetic applications to
infer supertrees and perform tree congruence analyses.
In this paper, we focus on the parameterized complexity
of this NP-hard problem, considering different
combinations of parameters as well as particular cases.
We show that SMAST on $k$ rooted binary trees on a
label set of size $n$ can be solved in $ O((8 n)^k) $
time, which is an improvement with respect to the
previously known $ O(n^{3k^2}) $ time algorithm. In
this case, we also give an $ O((2 k)^p k n^2) $ time
algorithm, where $p$ is an upper bound on the number of
leaves of $L$ missing in a SMAST solution. This shows
that SMAST can be solved efficiently when the input
trees are mostly congruent. Then, for the particular
case where any triple of leaves is contained in at
least one input tree, we give $ O(4^p n^3) $ and $
O(3.12^p + n^4) $ time algorithms, obtaining the first
fixed-parameter tractable algorithms on a single
parameter for this problem. We also obtain
intractability results for several combinations of
parameters, thus indicating that it is unlikely that
fixed-parameter tractable algorithms can be found in
these particular cases.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "algorithms; maximum agreement supertree; parameterized
complexity; Phylogenetics; reductions; rooted
triples.",
}
@Article{Liu:2010:MPI,
author = "Xiaowen Liu and Jinyan Li and Lusheng Wang",
title = "Modeling Protein Interacting Groups by
Quasi-Bicliques: Complexity, Algorithm, and
Application",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "354--364",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.61",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein-protein interactions (PPIs) are one of the
most important mechanisms in cellular processes. To
model protein interaction sites, recent studies have
suggested to find interacting protein group pairs from
large PPI networks at the first step and then to search
conserved motifs within the protein groups to form
interacting motif pairs. To consider the noise effect
and the incompleteness of biological data, we propose
to use quasi-bicliques for finding interacting protein
group pairs. We investigate two new problems that arise
from finding interacting protein group pairs: the
maximum vertex quasi-biclique problem and the maximum
balanced quasi-biclique problem. We prove that both
problems are NP-hard. This is a surprising result as
the widely known maximum vertex biclique problem is
polynomial time solvable [1]. We then propose a
heuristic algorithm that uses the greedy method to find
the quasi-bicliques from PPI networks. Our experiment
results on real data show that this algorithm has a
better performance than a benchmark algorithm for
identifying highly matched BLOCKS and PRINTS motifs. We
also report results of two case studies on interacting
motif pairs that map well with two interacting domain
pairs in iPfam. Availability: The software and
supplementary information are available at
\path=http://www.cs.cityu.edu.hk/~lwang/software/ppi/index.html=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "interaction sites; Protein-protein interactions;
quasi-bicliques.",
}
@Article{Qi:2010:SGR,
author = "Xingqin Qi and Guojun Li and Shuguang Li and Ying Xu",
title = "Sorting Genomes by Reciprocal Translocations,
Insertions, and Deletions",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "365--374",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.53",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The problem of sorting by reciprocal translocations
(abbreviated as SBT) arises from the field of
comparative genomics, which is to find a shortest
sequence of reciprocal translocations that transforms
one genome $ \Pi $ into another genome $ \Gamma $, with
the restriction that $ \Pi $ and $ \Gamma $ contain the
same genes. SBT has been proved to be polynomial-time
solvable, and several polynomial algorithms have been
developed. In this paper, we show how to extend
Bergeron's SBT algorithm to include insertions and
deletions, allowing to compare genomes containing
different genes. In particular, if the gene set of $
\Pi $ is a subset (or superset, respectively) of the
gene set of $ \Gamma $, we present an approximation
algorithm for transforming $ \Pi $ into $ \Gamma $ by
reciprocal translocations and deletions (insertions,
respectively), providing a sorting sequence with length
at most OPT + 2, where OPT is the minimum number of
translocations and deletions (insertions, respectively)
needed to transform $ \Pi $ into $ \Gamma $; if $ \Pi $
and $ \Gamma $ have different genes but not containing
each other, we give a heuristic to transform $ \Pi $
into $ \Gamma $ by a shortest sequence of reciprocal
translocations, insertions, and deletions, with bounds
for the length of the sorting sequence it outputs. At a
conceptual level, there is some similarity between our
algorithm and the algorithm developed by El Mabrouk
which is used to sort two chromosomes with different
gene contents by reversals, insertions, and
deletions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "algorithm.; deletion; insertion; Translocation",
}
@Article{Unger:2010:LSG,
author = "Giora Unger and Benny Chor",
title = "Linear Separability of Gene Expression Data Sets",
journal = j-TCBB,
volume = "7",
number = "2",
pages = "375--381",
month = apr,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.90",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 7 16:01:51 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We study simple geometric properties of gene
expression data sets, where samples are taken from two
distinct classes (e.g., two types of cancer).
Specifically, the problem of linear separability for
pairs of genes is investigated. If a pair of genes
exhibits linear separation with respect to the two
classes, then the joint expression level of the two
genes is strongly correlated to the phenomena of the
sample being taken from one class or the other. This
may indicate an underlying molecular mechanism relating
the two genes and the phenomena(e.g., a specific
cancer). We developed and implemented novel efficient
algorithmic tools for finding all pairs of genes that
induce a linear separation of the two sample classes.
These tools are based on computational geometric
properties and were applied to 10 publicly available
cancer data sets. For each data set, we computed the
number of actual separating pairs and compared it to an
upper bound on the number expected by chance and to the
numbers resulting from shuffling the labels of the data
at random empirically. Seven out of these 10 data sets
are highly separable. Statistically, this phenomenon is
highly significant, very unlikely to occur at random.
It is therefore reasonable to expect that it manifests
a functional association between separating genes and
the underlying phenotypic classes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
keywords = "diagnosis; DNA microarrays; Gene expression analysis;
linear separation.",
}
@Article{Leitner:2010:OBI,
author = "Florian Leitner and Scott A. Mardis and Martin
Krallinger and Gianni Cesareni and Lynette A. Hirschman
and Alfonso Valencia",
title = "An Overview of {BioCreative II.5}",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "385--399",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.61",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kolchinsky:2010:CPP,
author = "Artemy Kolchinsky and Alaa Abi-Haidar and Jasleen Kaur
and Ahmed Abdeen Hamed and Luis M. Rocha",
title = "Classification of Protein-Protein Interaction
Full-Text Documents Using Text and Citation Network
Features",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "400--411",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.55",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dai:2010:MGN,
author = "Hong-Jie Dai and Po-Ting Lai and Richard Tzong-Han
Tsai",
title = "Multistage Gene Normalization and {SVM}-Based Ranking
for Protein Interactor Extraction in Full-Text
Articles",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "412--420",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.45",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lan:2010:EIF,
author = "Man Lan and Jian Su",
title = "Empirical Investigations into Full-Text Protein
Interaction Article Categorization Task {(ACT)} in the
{BioCreative II.5} Challenge",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "421--427",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.49",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2010:BSI,
author = "Yifei Chen and Feng Liu and Bernard Manderick",
title = "{BioLMiner} System: Interaction Normalization Task and
Interaction Pair Task in the {BioCreative II.5}
Challenge",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "428--441",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.47",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Saetre:2010:EPI,
author = "Rune S{\ae}tre and Kazuhiro Yoshida and Makoto Miwa
and Takuya Matsuzaki and Yoshinobu Kano and Jun'ichi
Tsujii",
title = "Extracting Protein Interactions from Text with the
Unified {AkaneRE} Event Extraction System",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "442--453",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.46",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cao:2010:IAM,
author = "Yong-gang Cao and Zuofeng Li and Feifan Liu and
Shashank Agarwal and Qing Zhang and Hong Yu",
title = "An {IR}-Aided Machine Learning Framework for the
{BioCreative II.5} Challenge",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "454--461",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.56",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Verspoor:2010:ESB,
author = "Karin Verspoor and Christophe Roeder and Helen L.
Johnson and Kevin Bretonnel Cohen and William A.
{Baumgartner, Jr.} and Lawrence E. Hunter",
title = "Exploring Species-Based Strategies for Gene
Normalization",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "462--471",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.48",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rinaldi:2010:OBI,
author = "Fabio Rinaldi and Gerold Schneider and Kaarel
Kaljurand and Simon Clematide and Th{\'e}r{\`e}se
Vachon and Martin Romacker",
title = "{OntoGene} in {BioCreative II.5}",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "472--480",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.50",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hakenberg:2010:EEP,
author = "J{\"o}rg Hakenberg and Robert Leaman and Nguyen Ha Vo
and Siddhartha Jonnalagadda and Ryan Sullivan and
Christopher Miller and Luis Tari and Chitta Baral and
Graciela Gonzalez",
title = "Efficient Extraction of Protein-Protein Interactions
from Full-Text Articles",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "481--494",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.51",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chowdhury:2010:COD,
author = "Rezaul Alan Chowdhury and Hai-Son Le and Vijaya
Ramachandran",
title = "Cache-Oblivious Dynamic Programming for
Bioinformatics",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "495--510",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.94",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tininini:2010:CHA,
author = "Leonardo Tininini and Paola Bertolazzi and Alessandra
Godi and Giuseppe Lancia",
title = "{CollHaps}: a Heuristic Approach to Haplotype
Inference by Parsimony",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "511--523",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.130",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jackups:2010:CAS,
author = "Ronald {Jackups, Jr.} and Jie Liang",
title = "Combinatorial Analysis for Sequence and Spatial Motif
Discovery in Short Sequence Fragments",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "524--536",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.101",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Han:2010:NPC,
author = "Xiaoxu Han",
title = "Nonnegative Principal Component Analysis for Cancer
Molecular Pattern Discovery",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "537--549",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.36",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zeng:2010:SSC,
author = "Jia Zeng and Xiao-Yu Zhao and Xiao-Qin Cao and Hong
Yan",
title = "{SCS}: Signal, Context, and Structure Features for
Genome-Wide Human Promoter Recognition",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "550--562",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.95",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zimek:2010:SHF,
author = "Arthur Zimek and Fabian Buchwald and Eibe Frank and
Stefan Kramer",
title = "A Study of Hierarchical and Flat Classification of
Proteins",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "563--571",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.104",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bonet:2010:CUD,
author = "Maria Luisa Bonet and Katherine {St. John}",
title = "On the Complexity of {uSPR} Distance",
journal = j-TCBB,
volume = "7",
number = "3",
pages = "572--576",
month = jul,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.132",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 20 13:49:55 MDT 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mandou:2010:GEI,
author = "Ion Mandou and Giri Narasimhan and Yi Pan and Yanqing
Zhang",
title = "{Guest Editors}' Introduction to the Special Section
on Bioinformatics Research and Applications",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "577--578",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.114",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Munoz:2010:RPG,
author = "Adriana Munoz and David Sankoff",
title = "Rearrangement Phylogeny of Genomes in Contig Form",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "579--587",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.66",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Venkatachalam:2010:UTC,
author = "Balaji Venkatachalam and Jim Apple and Katherine {St.
John} and Daniel Gusfield",
title = "Untangling Tanglegrams: Comparing Trees by Their
Drawings",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "588--597",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.57",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bonizzoni:2010:PPX,
author = "Paola Bonizzoni and Gianluca Della Vedova and Riccardo
Dondi and Yuri Pirola and Romeo Rizzi",
title = "Pure Parsimony Xor Haplotyping",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "598--610",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.52",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wu:2010:ECC,
author = "Yufeng Wu",
title = "Exact Computation of Coalescent Likelihood for
Panmictic and Subdivided Populations under the Infinite
Sites Model",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "611--618",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.2",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rajasekaran:2010:IAP,
author = "Sanguthevar Rajasekaran and Sahar {Al Seesi} and Reda
A. Ammar",
title = "Improved Algorithms for Parsing {ESLTAGs}: a
Grammatical Model Suitable for {RNA} Pseudoknots",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "619--627",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.54",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Blin:2010:QGP,
author = "Guillaume Blin and Florian Sikora and Stephane
Vialette",
title = "Querying Graphs in Protein-Protein Interactions
Networks Using Feedback Vertex Set",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "628--635",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.53",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cheng:2010:SLM,
author = "Qiang Cheng",
title = "A Sparse Learning Machine for High-Dimensional Data
with Application to Microarray Gene Analysis",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "636--646",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.8",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Langdon:2010:SSD,
author = "W. B. Langdon and G. J. G. Upton and R. da Silva
Camargo and A. P. Harrison",
title = "A Survey of Spatial Defects in {Homo Sapiens
Affymetrix GeneChips}",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "647--653",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.108",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2010:CRA,
author = "Gang Li and Tak-Ming Chan and Kwong-Sak Leung and
Kin-Hong Lee",
title = "A Cluster Refinement Algorithm for Motif Discovery",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "654--668",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.25",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhou:2010:FNN,
author = "Jianjun Zhou and Jorg Sander and Zhipeng Cai and
Lusheng Wang and Guohui Lin",
title = "Finding the Nearest Neighbors in Biological Databases
Using Less Distance Computations",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "669--680",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.99",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huang:2010:HRR,
author = "Liang-Tsung Huang and Lien-Fu Lai and M. Michael
Gromiha",
title = "Human-Readable Rule Generator for Integrating Amino
Acid Sequence Information and Stability of Mutant
Proteins",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "681--687",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.128",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Godin:2010:QDS,
author = "Christophe Godin and Pascal Ferraro",
title = "Quantifying the Degree of Self-Nestedness of Trees:
Application to the Structural Analysis of Plants",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "688--703",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.29",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Snir:2010:QMD,
author = "Sagi Snir and Satish Rao",
title = "Quartets {MaxCut}: a Divide and Conquer Quartets
Algorithm",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "704--718",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.133",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lu:2010:RNM,
author = "Xin Lu and Anthony Gamst and Ronghui Xu",
title = "{RDCurve}: a Nonparametric Method to Evaluate the
Stability of Ranking Procedures",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "719--726",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.138",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tsang:2010:SPA,
author = "Herbert H. Tsang and Kay C. Wiese",
title = "{SARNA-Predict}: Accuracy Improvement of {RNA}
Secondary Structure Prediction Using Permutation-Based
Simulated Annealing",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "727--740",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.97",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{You:2010:UGP,
author = "Liwen You and Vladimir Brusic and Marcus Gallagher and
Mikael Boden",
title = "Using {Gaussian} Process with Test Rejection to Detect
{T}-Cell Epitopes in Pathogen Genomes",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "741--751",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.131",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Apostolico:2010:VDE,
author = "Alberto Apostolico and Matteo Comin and Laxmi Parida",
title = "{VARUN}: Discovering Extensible Motifs under
Saturation Constraints",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "752--762",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.123",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Miklos:2010:MPI,
author = "Istvan Miklos and Bence Melykuti and Krister Swenson",
title = "The {Metropolized} Partial Importance Sampling {MCMC}
Mixes Slowly on Minimum Reversal Rearrangement Paths",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "763--767",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.26",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2010:TAI,
author = "Anonymous",
title = "2010 {TCBB} Annual Index",
journal = j-TCBB,
volume = "7",
number = "4",
pages = "763--767",
month = oct,
year = "2010",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.116",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:02 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sagot:2011:EEa,
author = "Marie-France Sagot",
title = "{EIC} Editorial",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "1--1",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.7",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Owen:2011:FAC,
author = "Megan Owen and J. Scott Provan",
title = "A Fast Algorithm for Computing Geodesic Distances in
Tree Space",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "2--13",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.3",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Comparing and computing distances between phylogenetic
trees are important biological problems, especially for
models where edge lengths play an important role. The
geodesic distance measure between two phylogenetic
trees with edge lengths is the length of the shortest
path between them in the continuous tree space
introduced by Billera, Holmes, and Vogtmann. This tree
space provides a powerful tool for studying and
comparing phylogenetic trees, both in exhibiting a
natural distance measure and in providing a
Euclidean-like structure for solving optimization
problems on trees. An important open problem is to find
a polynomial time algorithm for finding geodesics in
tree space.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Shah:2011:GFA,
author = "Mohak Shah and Jacques Corbeil",
title = "A General Framework for Analyzing Data from Two Short
Time-Series Microarray Experiments",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "14--26",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.51",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We propose a general theoretical framework for
analyzing differentially expressed genes and behavior
patterns from two homogeneous short time-course data.
The framework generalizes the recently proposed
Hilbert--Schmidt Independence Criterion (HSIC)-based
framework adapting it to the time-series scenario by
utilizing tensor analysis for data transformation. The
proposed framework is effective in yielding criteria
that can identify both the differentially expressed
genes and time-course patterns of interest between two
time-series experiments without requiring to explicitly
cluster the data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mauch:2011:EFE,
author = "Sean Mauch and Mark Stalzer",
title = "Efficient Formulations for Exact Stochastic Simulation
of Chemical Systems",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "27--35",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.47",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "One can generate trajectories to simulate a system of
chemical reactions using either Gillespie's direct
method or Gibson and Bruck's next reaction method.
Because one usually needs many trajectories to
understand the dynamics of a system, performance is
important. In this paper, we present new formulations
of these methods that improve the computational
complexity of the algorithms. We present optimized
implementations, available from
\path=http://cain.sourceforge.net/=, that offer better
performance than previous work. There is no single
method that is best for all problems. Simple
formulations often work best for systems with a small
number of reactions, while some sophisticated methods
offer the best performance for large problems and scale
well asymptotically.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gasbarra:2011:EHF,
author = "Dario Gasbarra and Sangita Kulathinal and Matti
Pirinen and Mikko J. Sillanpaa",
title = "Estimating Haplotype Frequencies by Combining Data
from Large {DNA} Pools with Database Information",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "36--44",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.71",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We assume that allele frequency data have been
extracted from several large DNA pools, each containing
genetic material of up to hundreds of sampled
individuals. Our goal is to estimate the haplotype
frequencies among the sampled individuals by combining
the pooled allele frequency data with prior knowledge
about the set of possible haplotypes. Such prior
information can be obtained, for example, from a
database such as HapMap. We present a Bayesian
haplotyping method for pooled DNA based on a continuous
approximation of the multinomial distribution. The
proposed method is applicable when the sizes of the DNA
pools and/or the number of considered loci exceed the
limits of several earlier methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bajaj:2011:FFP,
author = "Chandrajit L. Bajaj and Rezaul Chowdhury and Vinay
Siddahanavalli",
title = "{$ F^2 $Dock}: Fast {Fourier} Protein-Protein
Docking",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "45--58",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.57",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The functions of proteins are often realized through
their mutual interactions. Determining a relative
transformation for a pair of proteins and their
conformations which form a stable complex, reproducible
in nature, is known as docking. It is an important step
in drug design, structure determination, and
understanding function and structure relationships. In
this paper, we extend our nonuniform fast Fourier
transform-based docking algorithm to include an
adaptive search phase (both translational and
rotational) and thereby speed up its execution. We have
also implemented a multithreaded version of the
adaptive docking algorithm for even faster execution on
multicore machines. We call this protein-protein
docking code $ F^2 $Dock ($ F^2 $ = {\rm
\underline{F}ast\underline{F}ourier}).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Giard:2011:FSB,
author = "Joachim Giard and Patrice Rondao Alface and Jean-Luc
Gala and Benoit Macq",
title = "Fast Surface-Based Travel Depth Estimation Algorithm
for Macromolecule Surface Shape Description",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "59--68",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.53",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Travel Depth, introduced by Coleman and Sharp in 2006,
is a physical interpretation of molecular depth, a term
frequently used to describe the shape of a molecular
active site or binding site. Travel Depth can be seen
as the physical distance a solvent molecule would have
to travel from a point of the surface, i.e., the
Solvent-Excluded Surface (SES), to its convex hull.
Existing algorithms providing an estimation of the
Travel Depth are based on a regular sampling of the
molecule volume and the use of the Dijkstra's shortest
path algorithm. Since Travel Depth is only defined on
the molecular surface, this volume-based approach is
characterized by a large computational complexity due
to the processing of unnecessary samples lying inside
or outside the molecule.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pizzi:2011:FSM,
author = "Cinzia Pizzi and Pasi Rastas and Esko Ukkonen",
title = "Finding Significant Matches of Position Weight
Matrices in Linear Time",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "69--79",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.35",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Position weight matrices are an important method for
modeling signals or motifs in biological sequences,
both in DNA and protein contexts. In this paper, we
present fast algorithms for the problem of finding
significant matches of such matrices. Our algorithms
are of the online type, and they generalize classical
multipattern matching, filtering, and superalphabet
techniques of combinatorial string matching to the
problem of weight matrix matching. Several variants of
the algorithms are developed, including multiple matrix
extensions that perform the search for several matrices
in one scan through the sequence database. Experimental
performance evaluation is provided to compare the new
techniques against each other as well as against some
other online and index-based algorithms proposed in the
literature.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Andonie:2011:FAP,
author = "Razvan Andonie and Levente Fabry-Asztalos and
Christopher B. Abdul-Wahid and Sarah Abdul-Wahid and
Grant I. Barker and Lukas C. Magill",
title = "Fuzzy {ARTMAP} Prediction of Biological Activities for
Potential {HIV-1} Protease Inhibitors Using a Small
Molecular Data Set",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "80--93",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.50",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Obtaining satisfactory results with neural networks
depends on the availability of large data samples. The
use of small training sets generally reduces
performance. Most classical Quantitative
Structure-Activity Relationship (QSAR) studies for a
specific enzyme system have been performed on small
data sets. We focus on the neuro-fuzzy prediction of
biological activities of HIV-1 protease inhibitory
compounds when inferring from small training sets. We
propose two computational intelligence prediction
techniques which are suitable for small training sets,
at the expense of some computational overhead. Both
techniques are based on the FAMR model. The FAMR is a
Fuzzy ARTMAP (FAM) incremental learning system used for
classification and probability estimation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mitra:2011:GNS,
author = "Sushmita Mitra and Ranajit Das and Yoichi Hayashi",
title = "Genetic Networks and Soft Computing",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "94--107",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.39",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The analysis of gene regulatory networks provides
enormous information on various fundamental cellular
processes involving growth, development, hormone
secretion, and cellular communication. Their extraction
from available gene expression profiles is a
challenging problem. Such reverse engineering of
genetic networks offers insight into cellular activity
toward prediction of adverse effects of new drugs or
possible identification of new drug targets. Tasks such
as classification, clustering, and feature selection
enable efficient mining of knowledge about gene
interactions in the form of networks. It is known that
biological data is prone to different kinds of noise
and ambiguity. Soft computing tools, such as fuzzy
sets, evolutionary strategies, and neurocomputing, have
been found to be helpful in providing low-cost,
acceptable solutions in the presence of various types
of uncertainties.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2011:IMG,
author = "Wenxue Wang and Bijoy K. Ghosh and Himadri Pakrasi",
title = "Identification and Modeling of Genes with Diurnal
Oscillations from Microarray Time Series Data",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "108--121",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.37",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Behavior of living organisms is strongly modulated by
the day and night cycle giving rise to a cyclic pattern
of activities. Such a pattern helps the organisms to
coordinate their activities and maintain a balance
between what could be performed during the ``day'' and
what could be relegated to the ``night.'' This cyclic
pattern, called the ``Circadian Rhythm,'' is a
biological phenomenon observed in a large number of
organisms. In this paper, our goal is to analyze
transcriptome data from Cyanothece for the purpose of
discovering genes whose expressions are rhythmic. We
cluster these genes into groups that are close in terms
of their phases and show that genes from a specific
metabolic functional category are tightly clustered,
indicating perhaps a ``preferred time of the
day/night'' when the organism performs this function.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Luo:2011:ICE,
author = "Lin-Kai Luo and Deng-Feng Huang and Ling-Jun Ye and
Qi-Feng Zhou and Gui-Fang Shao and Hong Peng",
title = "Improving the Computational Efficiency of Recursive
Cluster Elimination for Gene Selection",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "122--129",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.44",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The gene expression data are usually provided with a
large number of genes and a relatively small number of
samples, which brings a lot of new challenges.
Selecting those informative genes becomes the main
issue in microarray data analysis. Recursive cluster
elimination based on support vector machine (SVM-RCE)
has shown the better classification accuracy on some
microarray data sets than recursive feature elimination
based on support vector machine (SVM-RFE). However,
SVM-RCE is extremely time-consuming. In this paper, we
propose an improved method of SVM-RCE called ISVM-RCE.
ISVM-RCE first trains a SVM model with all clusters,
then applies the infinite norm of weight coefficient
vector in each cluster to score the cluster, finally
eliminates the gene clusters with the lowest score.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tan:2011:IPK,
author = "Mehmet Tan and Mohammed Alshalalfa and Reda Alhajj and
Faruk Polat",
title = "Influence of Prior Knowledge in Constraint-Based
Learning of Gene Regulatory Networks",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "130--142",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.58",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Constraint-based structure learning algorithms
generally perform well on sparse graphs. Although
sparsity is not uncommon, there are some domains where
the underlying graph can have some dense regions; one
of these domains is gene regulatory networks, which is
the main motivation to undertake the study described in
this paper. We propose a new constraint-based algorithm
that can both increase the quality of output and
decrease the computational requirements for learning
the structure of gene regulatory networks. The
algorithm is based on and extends the PC algorithm. Two
different types of information are derived from the
prior knowledge; one is the probability of existence of
edges, and the other is the nodes that seem to be
dependent on a large number of nodes compared to other
nodes in the graph.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gong:2011:ITM,
author = "Liuling Gong and Nidhal Bouaynaya and Dan Schonfeld",
title = "Information-Theoretic Model of Evolution over Protein
Communication Channel",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "143--151",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.1",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, we propose a communication model of
evolution and investigate its information-theoretic
bounds. The process of evolution is modeled as the
retransmission of information over a protein
communication channel, where the transmitted message is
the organism's proteome encoded in the DNA. We compute
the capacity and the rate distortion functions of the
protein communication system for the three domains of
life: Archaea, Bacteria, and Eukaryotes. The tradeoff
between the transmission rate and the distortion in
noisy protein communication channels is analyzed. As
expected, comparison between the optimal transmission
rate and the channel capacity indicates that the
biological fidelity does not reach the Shannon optimal
distortion.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Barker:2011:LGR,
author = "Nathan A. Barker and Chris J. Myers and Hiroyuki
Kuwahara",
title = "Learning Genetic Regulatory Network Connectivity from
Time Series Data",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "152--165",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.48",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recent experimental advances facilitate the collection
of time series data that indicate which genes in a cell
are expressed. This information can be used to
understand the genetic regulatory network that
generates the data. Typically, Bayesian analysis
approaches are applied which neglect the time series
nature of the experimental data, have difficulty in
determining the direction of causality, and do not
perform well on networks with tight feedback. To
address these problems, this paper presents a method to
learn genetic network connectivity which exploits the
time series nature of experimental data to achieve
better causal predictions. This method first breaks up
the data into bins.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ropers:2011:MRU,
author = "Delphine Ropers and Valentina Baldazzi and Hidde de
Jong",
title = "Model Reduction Using Piecewise-Linear Approximations
Preserves Dynamic Properties of the Carbon Starvation
Response in \bioname{Escherichia coli}",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "166--181",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.49",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The adaptation of the bacterium Escherichia coli to
carbon starvation is controlled by a large network of
biochemical reactions involving genes, mRNAs, proteins,
and signalling molecules. The dynamics of these
networks is difficult to analyze, notably due to a lack
of quantitative information on parameter values. To
overcome these limitations, model reduction approaches
based on quasi-steady-state (QSS) and piecewise-linear
(PL) approximations have been proposed, resulting in
models that are easier to handle mathematically and
computationally. These approximations are not supposed
to affect the capability of the model to account for
essential dynamical properties of the system, but the
validity of this assumption has not been systematically
tested.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wu:2011:NMI,
author = "Yufeng Wu",
title = "New Methods for Inference of Local Tree Topologies
with Recombinant {SNP} Sequences in Populations",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "182--193",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.27",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Large amount of population-scale genetic variation
data are being collected in populations. One
potentially important biological problem is to infer
the population genealogical history from these genetic
variation data. Partly due to recombination,
genealogical history of a set of DNA sequences in a
population usually cannot be represented by a single
tree. Instead, genealogy is better represented by a
genealogical network, which is a compact representation
of a set of correlated local genealogical trees, each
for a short region of genome and possibly with
different topology. Inference of genealogical history
for a set of DNA sequences under recombination has many
potential applications, including association mapping
of complex diseases.In this paper, we present two new
methods for reconstructing local tree topologies with
the presence of recombination, which extend and improve
the previous work in.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Agrawal:2011:PSS,
author = "Ankit Agrawal and Xiaoqiu Huang",
title = "Pairwise Statistical Significance of Local Sequence
Alignment Using Sequence-Specific and Position-Specific
Substitution Matrices",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "194--205",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.69",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Pairwise sequence alignment is a central problem in
bioinformatics, which forms the basis of various other
applications. Two related sequences are expected to
have a high alignment score, but relatedness is usually
judged by statistical significance rather than by
alignment score. Recently, it was shown that pairwise
statistical significance gives promising results as an
alternative to database statistical significance for
getting individual significance estimates of pairwise
alignment scores. The improvement was mainly attributed
to making the statistical significance estimation
process more sequence-specific and
database-independent. In this paper, we use
sequence-specific and position-specific substitution
matrices to derive the estimates of pairwise
statistical significance, which is expected to use more
sequence-specific information in estimating pairwise
statistical significance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{vanBerlo:2011:PMF,
author = "Rogier J. P. van Berlo and Dick de Ridder and
Jean-Marc Daran and Pascale A. S. Daran-Lapujade and
Bas Teusink and Marcel J. T. Reinders",
title = "Predicting Metabolic Fluxes Using Gene Expression
Differences As Constraints",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "206--216",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.55",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A standard approach to estimate intracellular fluxes
on a genome-wide scale is flux-balance analysis (FBA),
which optimizes an objective function subject to
constraints on (relations between) fluxes. The
performance of FBA models heavily depends on the
relevance of the formulated objective function and the
completeness of the defined constraints. Previous
studies indicated that FBA predictions can be improved
by adding regulatory on/off constraints. These
constraints were imposed based on either absolute or
relative gene expression values. We provide a new
algorithm that directly uses regulatory up/down
constraints based on gene expression data in FBA
optimization (tFBA). Our assumption is that if the
activity of a gene drastically changes from one
condition to the other, the flux through the reaction
controlled by that gene will change accordingly.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lahti:2011:PAP,
author = "Leo Lahti and Laura L. Elo and Tero Aittokallio and
Samuel Kaski",
title = "Probabilistic Analysis of Probe Reliability in
Differential Gene Expression Studies with Short
Oligonucleotide Arrays",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "217--225",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.38",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Probe defects are a major source of noise in gene
expression studies. While existing approaches detect
noisy probes based on external information such as
genomic alignments, we introduce and validate a
targeted probabilistic method for analyzing probe
reliability directly from expression data and
independently of the noise source. This provides
insights into the various sources of probe-level noise
and gives tools to guide probe design.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kc:2011:TIP,
author = "Dukka B. Kc and Dennis R. Livesay",
title = "Topology Improves Phylogenetic Motif Functional Site
Predictions",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "226--233",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.60",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Prediction of protein functional sites from
sequence-derived data remains an open bioinformatics
problem. We have developed a phylogenetic motif (PM)
functional site prediction approach that identifies
functional sites from alignment fragments that parallel
the evolutionary patterns of the family. In our
approach, PMs are identified by comparing tree
topologies of each alignment fragment to that of the
complete phylogeny. Herein, we bypass the phylogenetic
reconstruction step and identify PMs directly from
distance matrix comparisons. In order to optimize the
new algorithm, we consider three different distance
matrices and 13 different matrix similarity scores. We
assess the performance of the various approaches on a
structurally nonredundant data set that includes three
types of functional site definitions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hoque:2011:TRG,
author = "Md Tamjidul Hoque and Madhu Chetty and Andrew Lewis
and Abdul Sattar",
title = "Twin Removal in Genetic Algorithms for Protein
Structure Prediction Using Low-Resolution Model",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "234--245",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.34",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper presents the impact of twins and the
measures for their removal from the population of
genetic algorithm (GA) when applied to effective
conformational searching. It is conclusively shown that
a twin removal strategy for a GA provides considerably
enhanced performance when investigating solutions to
complex ab initio protein structure prediction (PSP)
problems in low-resolution model. Without twin removal,
GA crossover and mutation operations can become
ineffectual as generations lose their ability to
produce significant differences, which can lead to the
solution stalling.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{daCosta:2011:WPC,
author = "Joaquim F. Pinto da Costa and Hugo Alonso and Luis
Roque",
title = "A Weighted Principal Component Analysis and Its
Application to Gene Expression Data",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "246--252",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.61",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this work, we introduce in the first part new
developments in Principal Component Analysis (PCA) and
in the second part a new method to select variables
(genes in our application). Our focus is on problems
where the values taken by each variable do not all have
the same importance and where the data may be
contaminated with noise and contain outliers, as is the
case with microarray data. The usual PCA is not
appropriate to deal with this kind of problems. In this
context, we propose the use of a new correlation
coefficient as an alternative to Pearson's. This leads
to a so-called weighted PCA (WPCA).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2011:DAN,
author = "Ping Li and James Lam",
title = "Disturbance Analysis of Nonlinear Differential
Equation Models of Genetic {SUM} Regulatory Networks",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "253--259",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.19",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Noise disturbances and time delays are frequently met
in cellular genetic regulatory systems. This paper is
concerned with the disturbance analysis of a class of
genetic regulatory networks described by nonlinear
differential equation models. The mechanisms of genetic
regulatory networks to amplify (attenuate) external
disturbance are explored, and a simple measure of the
amplification (attenuation) level is developed from a
nonlinear robust control point of view. It should be
noted that the conditions used to measure the
disturbance level are delay-independent or
delay-dependent, and are expressed within the framework
of linear matrix inequalities, which can be
characterized as convex optimization, and computed by
the interior-point algorithm easily.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Luo:2011:LTA,
author = "Cheng-Wei Luo and Ming-Chiang Chen and Yi-Ching Chen
and Roger W. L. Yang and Hsiao-Fei Liu and Kun-Mao
Chao",
title = "Linear-Time Algorithms for the Multiple Gene
Duplication Problems",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "260--265",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.52",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A fundamental problem arising in the evolutionary
molecular biology is to discover the locations of gene
duplications and multiple gene duplication episodes
based on the phylogenetic information. The solutions to
the MULTIPLE GENE DUPLICATION problems can provide
useful clues to place the gene duplication events onto
the locations of a species tree and to expose the
multiple gene duplication episodes. In this paper, we
study two variations of the MULTIPLE GENE DUPLICATION
problems: the EPISODE-CLUSTERING (EC) problem and the
MINIMUM EPISODES (ME) problem. For the EC problem, we
improve the results of Burleigh et al. with an optimal
linear-time algorithm.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mao:2011:RMS,
author = "Kezhi Z. Mao and Wenyin Tang",
title = "Recursive {Mahalanobis} Separability Measure for Gene
Subset Selection",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "266--272",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.43",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Mahalanobis class separability measure provides an
effective evaluation of the discriminative power of a
feature subset, and is widely used in feature
selection. However, this measure is computationally
intensive or even prohibitive when it is applied to
gene expression data. In this study, a recursive
approach to Mahalanobis measure evaluation is proposed,
with the goal of reducing computational overhead.
Instead of evaluating Mahalanobis measure directly in
high-dimensional space, the recursive approach
evaluates the measure through successive evaluations in
2D space. Because of its recursive nature, this
approach is extremely efficient when it is combined
with a forward search procedure. In addition, it is
noted that gene subsets selected by Mahalanobis measure
tend to overfit training data and generalize
unsatisfactorily on unseen test data, due to small
sample size in gene expression problems.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Krishnamurthy:2011:SMM,
author = "Vikram Krishnamurthy and Kai-Yiu Luk",
title = "Semi-{Markov} Models for {Brownian} Dynamics
Permeation in Biological Ion Channels",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "273--281",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.136",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Constructing accurate computational models that
explain how ions permeate through a biological ion
channel is an important problem in biophysics and drug
design. Brownian dynamics simulations are large-scale
interacting particle computer simulations for modeling
ion channel permeation but can be computationally
prohibitive. In this paper, we show the somewhat
surprising result that a small-dimensional semi-Markov
model can generate events (such as conduction events
and dwell times at binding sites in the protein) that
are statistically indistinguishable from Brownian
dynamics computer simulation. This approach enables the
use of extrapolation techniques to predict channel
conduction when performing the actual Brownian dynamics
simulation that is computationally intractable.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Krishnamurthy:2011:TRL,
author = "Vikram Krishnamurthy and Kai-Yiu Luk",
title = "2010 {TCBB} Reviewers List",
journal = j-TCBB,
volume = "8",
number = "1",
pages = "282--284",
month = jan,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.1",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Dec 20 18:39:04 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sagot:2011:EEb,
author = "Marie-France Sagot",
title = "{EIC} Editorial",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "289--291",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.15",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wu:2011:GES,
author = "Fang-Xiang Wu and Jun Huan",
title = "Guest Editorial: Special Focus on Bioinformatics and
Systems Biology",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "292--293",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.16",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2011:FSF,
author = "Yanpeng Li and Xiaohua Hu and Hongfei Lin and Zhiahi
Yang",
title = "A Framework for Semisupervised Feature Generation and
Its Applications in Biomedical Literature Mining",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "294--307",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.99",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Feature representation is essential to machine
learning and text mining. In this paper, we present a
feature coupling generalization (FCG) framework for
generating new features from unlabeled data. It selects
two special types of features, i.e.,
example-distinguishing features (EDFs) and
class-distinguishing features (CDFs) from original
feature set, and then generalizes EDFs into
higher-level features based on their coupling degrees
with CDFs in unlabeled data. The advantage is: EDFs
with extreme sparsity in labeled data can be enriched
by their co-occurrences with CDFs in unlabeled data so
that the performance of these low-frequency features
can be greatly boosted and new information from
unlabeled can be incorporated.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jeong:2011:PSS,
author = "Jong Cheol Jeong and Xiaotong Lin and Xue-wen Chen",
title = "On Position-Specific Scoring Matrix for Protein
Function Prediction",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "308--315",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.93",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "While genome sequencing projects have generated
tremendous amounts of protein sequence data for a vast
number of genomes, substantial portions of most genomes
are still unannotated. Despite the success of
experimental methods for identifying protein functions,
they are often lab intensive and time consuming. Thus,
it is only practical to use in silico methods for the
genome-wide functional annotations. In this paper, we
propose new features extracted from protein sequence
only and machine learning-based methods for
computational function prediction. These features are
derived from a position-specific scoring matrix, which
has shown great potential in other bininformatics
problems. We evaluate these features using four
different classifiers and yeast protein data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Oh:2011:ELA,
author = "Sangyoon Oh and Min Su Lee and Byoung-Tak Zhang",
title = "Ensemble Learning with Active Example Selection for
Imbalanced Biomedical Data Classification",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "316--325",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.96",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In biomedical data, the imbalanced data problem occurs
frequently and causes poor prediction performance for
minority classes. It is because the trained classifiers
are mostly derived from the majority class. In this
paper, we describe an ensemble learning method combined
with active example selection to resolve the imbalanced
data problem. Our method consists of three key
components: (1) an active example selection algorithm
to choose informative examples for training the
classifier, (2) an ensemble learning method to combine
variations of classifiers derived by active example
selection, and (3) an incremental learning scheme to
speed up the iterative training procedure for active
example selection.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ibrahim:2011:UQP,
author = "Zina Ibrahim and Alioune Ngom and Ahmed Y. Tawfik",
title = "Using Qualitative Probability in Reverse-Engineering
Gene Regulatory Networks",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "326--334",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.98",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper demonstrates the use of qualitative
probabilistic networks (QPNs) to aid Dynamic Bayesian
Networks (DBNs) in the process of learning the
structure of gene regulatory networks from microarray
gene expression data. We present a study which shows
that QPNs define monotonic relations that are capable
of identifying regulatory interactions in a manner that
is less susceptible to the many sources of uncertainty
that surround gene expression data. Moreover, we
construct a model that maps the regulatory interactions
of genetic networks to QPN constructs and show its
capability in providing a set of candidate regulators
for target genes, which is subsequently used to
establish a prior structure that the DBN learning
algorithm can use and which (1) distinguishes spurious
correlations from true regulations, (2) enables the
discovery of sets of coregulators of target genes, and
(3) results in a more efficient construction of gene
regulatory networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sabnis:2011:CTD,
author = "Amit Sabnis and Robert W. Harrison",
title = "A Continuous-Time, Discrete-State Method for
Simulating the Dynamics of Biochemical Systems",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "335--341",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.97",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Computational systems biology is largely driven by
mathematical modeling and simulation of biochemical
networks, via continuous deterministic methods or
discrete event stochastic methods. Although the
deterministic methods are efficient in predicting the
macroscopic behavior of a biochemical system, they are
severely limited by their inability to represent the
stochastic effects of random molecular fluctuations at
lower concentration. In this work, we have presented a
novel method for simulating biochemical networks based
on a deterministic solution with a modification that
permits the incorporation of stochastic effects. To
demonstrate the feasibility of our approach, we have
tested our method on three previously reported
biochemical networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Raiford:2011:GOA,
author = "Douglas W. Raiford and Dan E. Krane and Travis E. Doom
and Michael L. Raymer",
title = "A Genetic Optimization Approach for Isolating
Translational Efficiency Bias",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "342--352",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.24",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The study of codon usage bias is an important research
area that contributes to our understanding of molecular
evolution, phylogenetic relationships, respiratory
lifestyle, and other characteristics. Translational
efficiency bias is perhaps the most well-studied codon
usage bias, as it is frequently utilized to predict
relative protein expression levels. We present a novel
approach to isolating translational efficiency bias in
microbial genomes. There are several existent methods
for isolating translational efficiency bias. Previous
approaches are susceptible to the confounding
influences of other potentially dominant biases.
Additionally, existing approaches to identifying
translational efficiency bias generally require both
genomic sequence information and prior knowledge of a
set of highly expressed genes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ram:2011:MBB,
author = "Ramesh Ram and Madhu Chetty",
title = "A {Markov-Blanket}-Based Model for Gene Regulatory
Network Inference",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "353--367",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.70",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "An efficient two-step Markov blanket method for
modeling and inferring complex regulatory networks from
large-scale microarray data sets is presented. The
inferred gene regulatory network (GRN) is based on the
time series gene expression data capturing the
underlying gene interactions. For constructing a highly
accurate GRN, the proposed method performs: (1)
discovery of a gene's Markov Blanket (MB), (2)
formulation of a flexible measure to determine the
network's quality, (3) efficient searching with the aid
of a guided genetic algorithm, and (4) pruning to
obtain a minimal set of correct interactions.
Investigations are carried out using both synthetic as
well as yeast cell cycle gene expression data sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{He:2011:PSC,
author = "Zengyou He and Can Yang and Weichuan Yu",
title = "A Partial Set Covering Model for Protein Mixture
Identification Using Mass Spectrometry Data",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "368--380",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.54",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein identification is a key and essential step in
mass spectrometry (MS) based proteome research. To
date, there are many protein identification strategies
that employ either MS data or MS/MS data for database
searching. While MS-based methods provide wider
coverage than MS/MS-based methods, their identification
accuracy is lower since MS data have less information
than MS/MS data. Thus, it is desired to design more
sophisticated algorithms that achieve higher
identification accuracy using MS data. Peptide Mass
Fingerprinting (PMF) has been widely used to identify
single purified proteins from MS data for many years.
In this paper, we extend this technology to protein
mixture identification.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wu:2011:ACC,
author = "Yonghui Wu and Timothy J. Close and Stefano Lonardi",
title = "Accurate Construction of Consensus Genetic Maps via
Integer Linear Programming",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "381--394",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.35",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We study the problem of merging genetic maps, when the
individual genetic maps are given as directed acyclic
graphs. The computational problem is to build a
consensus map, which is a directed graph that includes
and is consistent with all (or, the vast majority of)
the markers in the input maps. However, when markers in
the individual maps have ordering conflicts, the
resulting consensus map will contain cycles. Here, we
formulate the problem of resolving cycles in the
context of a parsimonious paradigm that takes into
account two types of errors that may be present in the
input maps, namely, local reshuffles and global
displacements.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Aydin:2011:BMA,
author = "Zafer Aydin and Yucel Altunbasak and Hakan Erdogan",
title = "{Bayesian} Models and Algorithms for Protein $ \beta
$-Sheet Prediction",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "395--409",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2008.140",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Prediction of the 3D structure greatly benefits from
the information related to secondary structure, solvent
accessibility, and nonlocal contacts that stabilize a
protein's structure. We address the problem of $ \beta
$-sheet prediction defined as the prediction of $ \beta
$--strand pairings, interaction types (parallel or
antiparallel), and $ \beta $-residue interactions (or
contact maps). We introduce a Bayesian approach for
proteins with six or less $ \beta $-strands in which we
model the conformational features in a probabilistic
framework by combining the amino acid pairing
potentials with a priori knowledge of $ \beta $-strand
organizations. To select the optimum $ \beta $-sheet
architecture, we significantly reduce the search space
by heuristics that enforce the amino acid pairs with
strong interaction potentials.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cardona:2011:CGT,
author = "Gabriel Cardona and Merce Llabres and Francesc
Rossello and Gabriel Valiente",
title = "Comparison of Galled Trees",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "410--427",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.60",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Galled trees, directed acyclic graphs that model
evolutionary histories with isolated hybridization
events, have become very popular due to both their
biological significance and the existence of
polynomial-time algorithms for their reconstruction. In
this paper, we establish to which extent several
distance measures for the comparison of evolutionary
networks are metrics for galled trees, and hence, when
they can be safely used to evaluate galled tree
reconstruction methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Leung:2011:DMD,
author = "KwongSak Leung and KinHong Lee and JinFeng Wang and
Eddie YT Ng and Henry LY Chan and Stephen KW Tsui and
Tony SK Mok and Pete Chi-Hang Tse and Joseph JY Sung",
title = "Data Mining on {DNA} Sequences of {Hepatitis B}
Virus",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "428--440",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.6",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Extraction of meaningful information from large
experimental data sets is a key element in
bioinformatics research. One of the challenges is to
identify genomic markers in Hepatitis B Virus (HBV)
that are associated with HCC (liver cancer) development
by comparing the complete genomic sequences of HBV
among patients with HCC and those without HCC. In this
study, a data mining framework, which includes
molecular evolution analysis, clustering, feature
selection, classifier learning, and classification, is
introduced. Our research group has collected HBV DNA
sequences, either genotype B or C, from over 200
patients specifically for this project. In the
molecular evolution analysis and clustering, three
subgroups have been identified in genotype C and a
clustering method has been developed to separate the
subgroups.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lin:2011:DMF,
author = "Tien-ho Lin and Robert F. Murphy and Ziv Bar-Joseph",
title = "Discriminative Motif Finding for Predicting Protein
Subcellular Localization",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "441--451",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.82",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Many methods have been described to predict the
subcellular location of proteins from sequence
information. However, most of these methods either rely
on global sequence properties or use a set of known
protein targeting motifs to predict protein
localization. Here, we develop and test a novel method
that identifies potential targeting motifs using a
discriminative approach based on hidden Markov models
(discriminative HMMs). These models search for motifs
that are present in a compartment but absent in other,
nearby, compartments by utilizing an hierarchical
structure that mimics the protein sorting mechanism. We
show that both discriminative motif finding and the
hierarchical structure improve localization prediction
on a benchmark data set of yeast proteins.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Saraswathi:2011:IPE,
author = "Saras Saraswathi and Suresh Sundaram and Narasimhan
Sundararajan and Michael Zimmermann and Marit
Nilsen-Hamilton",
title = "{ICGA-PSO-ELM} Approach for Accurate Multiclass Cancer
Classification Resulting in Reduced Gene Sets in Which
Genes Encoding Secreted Proteins Are Highly
Represented",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "452--463",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.13",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A combination of Integer-Coded Genetic Algorithm
(ICGA) and Particle Swarm Optimization (PSO), coupled
with the neural-network-based Extreme Learning Machine
(ELM), is used for gene selection and cancer
classification. ICGA is used with PSO\_ELM to select an
optimal set of genes, which is then used to build a
classifier to develop an algorithm (ICGA\_PSO\_ELM)
that can handle sparse data and sample imbalance. We
evaluate the performance of ICGA\_PSO\_ELM and compare
our results with existing methods in the literature. An
investigation into the functions of the selected genes,
using a systems biology approach, revealed that many of
the identified genes are involved in cell signaling and
proliferation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Irigoien:2011:MTC,
author = "Itziar Irigoien and Sergi Vives and Concepcion
Arenas",
title = "Microarray Time Course Experiments: Finding Profiles",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "464--475",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.79",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Time course studies with microarray techniques and
experimental replicates are very useful in biomedical
research. We present, in replicate experiments, an
alternative approach to select and cluster genes
according to a new measure for association between
genes. First, the procedure normalizes and standardizes
the expression profile of each gene, and then,
identifies scaling parameters that will further
minimize the distance between replicates of the same
gene. Then, the procedure filters out genes with a flat
profile, detects differences between replicates, and
separates genes without significant differences from
the rest. For this last group of genes, we define a
mean profile for each gene and use it to compute the
distance between two genes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dong:2011:NNK,
author = "Qiwen Dong and Shuigeng Zhou",
title = "Novel Nonlinear Knowledge-Based Mean Force Potentials
Based on Machine Learning",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "476--486",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.86",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The prediction of 3D structures of proteins from amino
acid sequences is one of the most challenging problems
in molecular biology. An essential task for solving
this problem with coarse-grained models is to deduce
effective interaction potentials. The development and
evaluation of new energy functions is critical to
accurately modeling the properties of biological
macromolecules. Knowledge-based mean force potentials
are derived from statistical analysis of proteins of
known structures. Current knowledge-based potentials
are almost in the form of weighted linear sum of
interaction pairs. In this study, a class of novel
nonlinear knowledge-based mean force potentials is
presented. The potential parameters are obtained by
nonlinear classifiers, instead of relative frequencies
of interaction pairs against a reference state or
linear classifiers.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Loriot:2011:CSD,
author = "Sebastien Loriot and Sushant Sachdeva and Karine
Bastard and Chantal Prevost and Frederic Cazals",
title = "On the Characterization and Selection of Diverse
Conformational Ensembles with Applications to Flexible
Docking",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "487--498",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.59",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "To address challenging flexible docking problems, a
number of docking algorithms pregenerate large
collections of candidate conformers. To remove the
redundancy from such ensembles, a central problem in
this context is to report a selection of conformers
maximizing some geometric diversity criterion. We make
three contributions to this problem. First, we resort
to geometric optimization so as to report selections
maximizing the molecular volume or molecular surface
area (MSA) of the selection. Greedy strategies are
developed, together with approximation bounds. Second,
to assess the efficacy of our algorithms, we
investigate two conformer ensembles corresponding to a
flexible loop of four protein complexes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Giegerich:2011:SAS,
author = "Robert Giegerich and Christian Hoener zu
Siederdissen",
title = "Semantics and Ambiguity of Stochastic {RNA} Family
Models",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "499--516",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.12",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Stochastic models, such as hidden Markov models or
stochastic context-free grammars (SCFGs) can fail to
return the correct, maximum likelihood solution in the
case of semantic ambiguity. This problem arises when
the algorithm implementing the model inspects the same
solution in different guises. It is a difficult problem
in the sense that proving semantic nonambiguity has
been shown to be algorithmically undecidable, while
compensating for it (by coalescing scores of equivalent
solutions) has been shown to be NP-hard. For stochastic
context-free grammars modeling RNA secondary structure,
it has been shown that the distortion of results can be
quite severe. Much less is known about the case when
stochastic context-free grammars model the matching of
a query sequence to an implicit consensus structure for
an RNA family.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tofigh:2011:SID,
author = "Ali Tofigh and Michael Hallett and Jens Lagergren",
title = "Simultaneous Identification of Duplications and
Lateral Gene Transfers",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "517--535",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.14",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The incongruency between a gene tree and a
corresponding species tree can be attributed to
evolutionary events such as gene duplication and gene
loss. This paper describes a combinatorial model where
so-called DTL-scenarios are used to explain the
differences between a gene tree and a corresponding
species tree taking into account gene duplications,
gene losses, and lateral gene transfers (also known as
horizontal gene transfers). The reasonable biological
constraint that a lateral gene transfer may only occur
between contemporary species leads to the notion of
acyclic DTL-scenarios. Parsimony methods are introduced
by defining appropriate optimization problems. We show
that finding most parsimonious acyclic DTL-scenarios is
NP-hard.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lieberman:2011:VEA,
author = "Michael D. Lieberman and Sima Taheri and whatever Guo
and Fatemeh Mirrashed and Inbal Yahav and Aleks Aris
and Ben Shneiderman",
title = "Visual Exploration across Biomedical Databases",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "536--550",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.1",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Though biomedical research often draws on knowledge
from a wide variety of fields, few visualization
methods for biomedical data incorporate meaningful
cross-database exploration. A new approach is offered
for visualizing and exploring a query-based subset of
multiple heterogeneous biomedical databases. Databases
are modeled as an entity-relation graph containing
nodes (database records) and links (relationships
between records). Users specify a keyword search string
to retrieve an initial set of nodes, and then explore
intra- and interdatabase links. Results are visualized
with user-defined semantic substrates to take advantage
of the rich set of attributes usually present in
biomedical data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hickey:2011:AAN,
author = "Glenn Hickey and Mathieu Blanchette and Paz Carmi and
Anil Maheshwari and Norbert Zeh",
title = "An Approximation Algorithm for the {Noah's Ark
Problem} with Random Feature Loss",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "551--556",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.37",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The phylogenetic diversity (PD) of a set of species is
a measure of their evolutionary distinctness based on a
phylogenetic tree. PD is increasingly being adopted as
an index of biodiversity in ecological conservation
projects. The Noah's Ark Problem (NAP) is an NP-Hard
optimization problem that abstracts a fundamental
conservation challenge in asking to maximize the
expected PD of a set of taxa given a fixed budget,
where each taxon is associated with a cost of
conservation and a probability of extinction. Only
simplified instances of the problem, where one or more
parameters are fixed as constants, have as of yet been
addressed in the literature.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cortes:2011:EMM,
author = "Juan Cortes and Sophie Barbe and Monique Erard and
Thierry Simeon",
title = "Encoding Molecular Motions in Voxel Maps",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "557--563",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.23",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper builds on the combination of robotic path
planning algorithms and molecular modeling methods for
computing large-amplitude molecular motions, and
introduces voxel maps as a computational tool to encode
and to represent such motions. We investigate several
applications and show results that illustrate the
interest of such representation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rocha:2011:GCL,
author = "J. Rocha",
title = "Graph Comparison by Log-Odds Score Matrices with
Application to Protein Topology Analysis",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "564--569",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.59",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A TOPS diagram is a simplified description of the
topology of a protein using a graph where nodes are $
\alpha $-helices and $ \beta $-strands, and edges
correspond to chirality relations and parallel or
antiparallel bonds between strands. We present a
matching algorithm between two TOPS diagrams where the
likelihood of a match is measured according to
previously known matches between complete 3D
structures. This totally new 3D training is recorded on
transition matrices that count the likelihood that a
given TOPS feature, or combination thereof, is replaced
by another feature on homologs. The new algorithm
outperforms existing ones on a benchmark database. Some
biologically significant examples are discussed as
well.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fujita:2011:ICR,
author = "Andre Fujita and Joao Ricardo Sato and Marcos Almeida
Demasi and Rui Yamaguchi and Teppei Shimamura and
Carlos Eduardo Ferreira and Mari Cleide Sogayar and
Satoru Miyano",
title = "Inferring Contagion in Regulatory Networks",
journal = j-TCBB,
volume = "8",
number = "2",
pages = "570--576",
month = mar,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.40",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jan 26 14:16:19 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Several gene regulatory network models containing
concepts of directionality at the edges have been
proposed. However, only a few reports have an
interpretable definition of directionality. Here,
differently from the standard causality concept defined
by Pearl, we introduce the concept of contagion in
order to infer directionality at the edges, i.e.,
asymmetries in gene expression dependences of
regulatory networks. Moreover, we present a bootstrap
algorithm in order to test the contagion concept. This
technique was applied in simulated data and, also, in
an actual large sample of biological data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Benso:2011:CMG,
author = "Alfredo Benso and Stefano {Di Carlo} and Gianfranco
Politano",
title = "A {cDNA} Microarray Gene Expression Data Classifier
for Clinical Diagnostics Based on Graph Theory",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "577--591",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.90",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yoruk:2011:CSM,
author = "Erdem Yoruk and Michael F. Ochs and Donald Geman and
Laurent Younes",
title = "A Comprehensive Statistical Model for Cell Signaling",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "592--606",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.87",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2011:FHC,
author = "Jianxin Wang and Min Li and Jianer Chen and Yi Pan",
title = "A Fast Hierarchical Clustering Algorithm for
Functional Modules Discovery in Protein Interaction
Networks",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "607--620",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.75",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Feng:2011:MFB,
author = "Jianxing Feng and Rui Jiang and Tao Jiang",
title = "A Max-Flow-Based Approach to the Identification of
Protein Complexes Using Protein Interaction and
Microarray Data",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "621--634",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.78",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huber:2011:PAR,
author = "Katharina T. Huber and Leo van Iersel and Steven Kelk
and Rados{\l}aw Suchecki",
title = "A Practical Algorithm for Reconstructing Level-1
Phylogenetic Networks",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "635--649",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.17",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Murphy:2011:TAP,
author = "James T. Murphy and Ray Walshe and Marc Devocelle",
title = "A Theoretical Analysis of the {Prodrug} Delivery
System for Treating Antibiotic-Resistant Bacteria",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "650--658",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.58",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ghorai:2011:CCG,
author = "Santanu Ghorai and Anirban Mukherjee and Sanghamitra
Sengupta and Pranab K. Dutta",
title = "Cancer Classification from Gene Expression Data by
{NPPC} Ensemble",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "659--671",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.36",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gossler:2011:CBM,
author = "Gregor Gossler",
title = "Component-Based Modeling and Reachability Analysis of
Genetic Networks",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "672--682",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.81",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tamada:2011:EGW,
author = "Yoshinori Tamada and Seiya Imoto and Hiromitsu Araki
and Masao Nagasaki and Cristin Print and D. Stephen
Charnock-Jones and Satoru Miyano",
title = "Estimating Genome-Wide Gene Networks Using
Nonparametric {Bayesian} Network Models on Massively
Parallel Computers",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "683--697",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.68",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hudek:2011:FSL,
author = "Alexander K. Hudek and Daniel G. Brown",
title = "{FEAST}: Sensitive Local Alignment with Multiple Rates
of Evolution",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "698--709",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.76",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Allman:2011:ITT,
author = "Elizabeth S. Allman and Sonja Petrovi{\'c} and John A.
Rhodes and Seth Sullivant",
title = "Identifiability of Two-Tree Mixtures for Group-Based
Models",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "710--722",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.79",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2011:INR,
author = "Tianwei Yu and Hesen Peng and Wei Sun",
title = "Incorporating Nonlinear Relationships in Microarray
Missing Value Imputation",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "723--731",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.73",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Song:2011:MSS,
author = "Bin Song and {\.I} Esra B{\"u}y{\"u}ktahtakin and
Sanjay Ranka and Tamer Kahveci",
title = "Manipulating the Steady State of Metabolic Pathways",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "732--747",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.41",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2011:MLP,
author = "Qian Xu and Sinno Jialin Pan and Hannah Hong Xue and
Qiang Yang",
title = "Multitask Learning for Protein Subcellular Location
Prediction",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "748--759",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.22",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Armananzas:2011:PSM,
author = "Ruben Armananzas and Yvan Saeys and Inaki Inza and
Miguel Garcia-Torres and Concha Bielza and Yves van de
Peer and Pedro Larranaga",
title = "Peakbin Selection in Mass Spectrometry Data Using a
Consensus Approach with Estimation of Distribution
Algorithms",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "760--774",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.18",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mitrofanova:2011:PPF,
author = "Antonina Mitrofanova and Vladimir Pavlovic and Bud
Mishra",
title = "Prediction of Protein Functions with Gene Ontology and
Interspecies Protein Homology Data",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "775--784",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.15",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Willson:2011:RNC,
author = "Stephen J. Willson",
title = "Regular Networks Can be Uniquely Constructed from
Their Trees",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "785--796",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.69",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Strutz:2011:SRL,
author = "Tilo Strutz",
title = "{$3$D} Shape Reconstruction of Loop Objects in {X}-Ray
Protein Crystallography",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "797--807",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.67",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dost:2011:TFM,
author = "Banu Dost and Chunlei Wu and Andrew Su and Vineet
Bafna",
title = "{TCLUST}: a Fast Method for Clustering Genome-Scale
Expression Data",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "808--818",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.34",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Venkateswaran:2011:TTF,
author = "Jayendra Gnanaskandan Venkateswaran and Bin Song and
Tamer Kahveci and Christopher Jermaine",
title = "{TRIAL}: a Tool for Finding Distant Structural
Similarities",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "819--831",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.28",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Valentini:2011:TPR,
author = "Giorgio Valentini",
title = "True Path Rule Hierarchical Ensembles for Genome-Wide
Gene Function Prediction",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "832--847",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.38",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bansal:2011:NFP,
author = "Mukul S. Bansal and Ron Shamir",
title = "A Note on the Fixed Parameter Tractability of the
Gene-Duplication Problem",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "848--850",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.74",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sehgal:2011:IRD,
author = "Aditya Kumar Sehgal and Sanmay Das and Keith Noto and
Milton H. {Saier, Jr.} and Charles Elkan",
title = "Identifying Relevant Data for a Biological Database:
Handcrafted Rules versus Machine Learning",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "851--857",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.83",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nguyen:2011:TBU,
author = "Minh N. Nguyen and Jacek M. Zurada and Jagath C.
Rajapakse",
title = "Toward Better Understanding of Protein Secondary
Structure: Extracting Prediction Rules",
journal = j-TCBB,
volume = "8",
number = "3",
pages = "858--864",
month = may # "\slash " # jun,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.16",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed May 25 15:41:56 2011",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Borodovsky:2011:GEI,
author = "Mark Borodovsky and Teresa M. Przytycka and
Sanguthevar Rajasekaran and Alexander Zelikovsky",
title = "{Guest Editors}' Introduction to the Special Section
on Bioinformatics Research and Applications",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "865--866",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.92",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Shibberu:2011:SAP,
author = "Yosi Shibberu and Allen Holder",
title = "A Spectral Approach to Protein Structure Alignment",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "867--875",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.24",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A new intrinsic geometry based on a spectral analysis
is used to motivate methods for aligning protein folds.
The geometry is induced by the fact that a distance
matrix can be scaled so that its eigenvalues are
positive. We provide a mathematically rigorous
development of the intrinsic geometry underlying our
spectral approach and use it to motivate two alignment
algorithms. The first uses eigenvalues alone and
dynamic programming to quickly compute a fold
alignment. Family identification results are reported
for the Skolnick40 and Proteus300 data sets. The second
algorithm extends our spectral method by iterating
between our intrinsic geometry and the 3D geometry of a
fold to make high-quality alignments.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ferraro:2011:ACQ,
author = "Nicola Ferraro and Luigi Palopoli and Simona Panni and
Simona E. Rombo",
title = "Asymmetric Comparison and Querying of Biological
Networks",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "876--889",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.29",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Comparing and querying the protein-protein interaction
(PPI) networks of different organisms is important to
infer knowledge about conservation across species.
Known methods that perform these tasks operate
symmetrically, i.e., they do not assign a distinct role
to the input PPI networks. However, in most cases, the
input networks are indeed distinguishable on the basis
of how the corresponding organism is biologically well
characterized. In this paper a new idea is developed,
that is, to exploit differences in the characterization
of organisms at hand in order to devise methods for
comparing their PPI networks. We use the PPI network
(called Master) of the best characterized organism as a
fingerprint to guide the alignment process to the
second input network (called Slave), so that generated
results preferably retain the structural
characteristics of the Master network.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wiedenhoeft:2011:PMI,
author = "John Wiedenhoeft and Roland Krause and Oliver
Eulenstein",
title = "The Plexus Model for the Inference of Ancestral
Multidomain Proteins",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "890--901",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.22",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Interactions of protein domains control essential
cellular processes. Thus, inferring the evolutionary
histories of multidomain proteins in the context of
their families can provide rewarding insights into
protein function. However, methods to infer these
histories are challenged by the complexity of
macroevolutionary events. Here, we address this
challenge by describing an algorithm that computes a
novel network-like structure, called plexus, which
represents the evolution of domains and their
combinations. Finally, we demonstrate the performance
of this algorithm with empirical data sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pattengale:2011:UHP,
author = "Nicholas Pattengale and Andre Aberer and Krister
Swenson and Alexandros Stamatakis and Bernard Moret",
title = "Uncovering Hidden Phylogenetic Consensus in Large Data
Sets",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "902--911",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.28",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Many of the steps in phylogenetic reconstruction can
be confounded by ``rogue'' taxa---taxa that cannot be
placed with assurance anywhere within the tree, indeed,
whose location within the tree varies with almost any
choice of algorithm or parameters. Phylogenetic
consensus methods, in particular, are known to suffer
from this problem. In this paper, we provide a novel
framework to define and identify rogue taxa. In this
framework, we formulate a bicriterion optimization
problem, the relative information criterion, that
models the net increase in useful information present
in the consensus tree when certain taxa are removed
from the input data. We also provide an effective
greedy heuristic to identify a subset of rogue taxa and
use this heuristic in a series of experiments, with
both pathological examples from the literature and a
collection of large biological data sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gysel:2011:EIC,
author = "Rob Gysel and Daniel Gusfield",
title = "Extensions and Improvements to the Chordal Graph
Approach to the Multistate Perfect Phylogeny Problem",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "912--917",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.27",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The multistate perfect phylogeny problem is a classic
problem in computational biology. When no perfect
phylogeny exists, it is of interest to find a set of
characters to remove in order to obtain a perfect
phylogeny in the remaining data. This is known as the
character removal problem. We show how to use chordal
graphs and triangulations to solve the character
removal problem for an arbitrary number of states,
which was previously unsolved. We outline a
preprocessing technique that speeds up the computation
of the minimal separators of a graph.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tsai:2011:CTA,
author = "Ming-Chi Tsai and Guy E. Blelloch and R. Ravi and
Russell Schwartz",
title = "A Consensus Tree Approach for Reconstructing Human
Evolutionary History and Detecting Population
Substructure",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "918--928",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.23",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The random accumulation of variations in the human
genome over time implicitly encodes a history of how
human populations have arisen, dispersed, and
intermixed since we emerged as a species.
Reconstructing that history is a challenging
computational and statistical problem but has important
applications both to basic research and to the
discovery of genotype-phenotype correlations. We
present a novel approach to inferring human
evolutionary history from genetic variation data. We
use the idea of consensus trees, a technique generally
used to reconcile species trees from divergent gene
trees, adapting it to the problem of finding robust
relationships within a set of intraspecies phylogenies
derived from local regions of the genome.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bandyopadhyay:2011:BIM,
author = "Sanghamitra Bandyopadhyay and Malay Bhattacharyya",
title = "A Biologically Inspired Measure for Coexpression
Analysis",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "929--942",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.106",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Two genes are said to be coexpressed if their
expression levels have a similar spatial or temporal
pattern. Ever since the profiling of gene microarrays
has been in progress, computational modeling of
coexpression has acquired a major focus. As a result,
several similarity/distance measures have evolved over
time to quantify coexpression similarity/dissimilarity
between gene pairs. Of these, correlation coefficient
has been established to be a suitable quantifier of
pairwise coexpression. In general, correlation
coefficient is good for symbolizing linear dependence,
but not for nonlinear dependence. In spite of this
drawback, it outperforms many other existing measures
in modeling the dependency in biological data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tenazinha:2011:SMM,
author = "Nuno Tenazinha and Susana Vinga",
title = "A Survey on Methods for Modeling and Analyzing
Integrated Biological Networks",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "943--958",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.117",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Understanding how cellular systems build up integrated
responses to their dynamically changing environment is
one of the open questions in Systems Biology. Despite
their intertwinement, signaling networks, gene
regulation and metabolism have been frequently modeled
independently in the context of well-defined
subsystems. For this purpose, several mathematical
formalisms have been developed according to the
features of each particular network under study.
Nonetheless, a deeper understanding of cellular
behavior requires the integration of these various
systems into a model capable of capturing how they
operate as an ensemble. With the recent advances in the
``omics'' technologies, more data is becoming available
and, thus, recent efforts have been driven toward this
integrated modeling approach.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huang:2011:IHA,
author = "Chao-Wen Huang and Wun-Shiun Lee and Sun-Yuan Hsieh",
title = "An Improved Heuristic Algorithm for Finding Motif
Signals in {DNA} Sequences",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "959--975",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.92",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The planted (l,d)-motif search problem is a
mathematical abstraction of the DNA functional site
discovery task. In this paper, we propose a heuristic
algorithm that can find planted (l,d)-signals in a
given set of DNA sequences. Evaluations on simulated
data sets demonstrate that the proposed algorithm
outperforms current widely used motif finding
algorithms. We also report the results of experiments
on real biological data sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bocker:2011:DGS,
author = "Sebastian Bocker and Birte Kehr and Florian Rasche",
title = "Determination of Glycan Structure from Tandem Mass
Spectra",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "976--986",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.129",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Glycans are molecules made from simple sugars that
form complex tree structures. Glycans constitute one of
the most important protein modifications and
identification of glycans remains a pressing problem in
biology. Unfortunately, the structure of glycans is
hard to predict from the genome sequence of an
organism. In this paper, we consider the problem of
deriving the topology of a glycan solely from tandem
mass spectrometry (MS) data. We study, how to generate
glycan tree candidates that sufficiently match the
sample mass spectrum, avoiding the combinatorial
explosion of glycan structures. Unfortunately, the
resulting problem is known to be computationally hard.
We present an efficient exact algorithm for this
problem based on fixed-parameter algorithmics that can
process a spectrum in a matter of seconds.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wong:2011:EAE,
author = "Samuel S. Y. Wong and Weimin Luo and Keith C. C.
Chan",
title = "{EvoMD}: An Algorithm for Evolutionary Molecular
Design",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "987--1003",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.100",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Traditionally, Computer-Aided Molecular Design (CAMD)
uses heuristic search and mathematical programming to
tackle the molecular design problem. But these
techniques do not handle large and nonlinear search
space very well. To overcome these drawbacks,
graph-based evolutionary algorithms (EAs) have been
proposed to evolve molecular design by mimicking
chemical reactions on the exchange of chemical bonds
and components between molecules. For these EAs to
perform their tasks, known molecular components, which
can serve as building blocks for the molecules to be
designed, and known chemical rules, which govern
chemical combination between different components, have
to be introduced before the evolutionary process can
take place.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Merelli:2011:IBS,
author = "Ivan Merelli and Paolo Cozzi and Daniele D'Agostino
and Andrea Clematis and Luciano Milanesi",
title = "Image-Based Surface Matching Algorithm Oriented to
Structural Biology",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "1004--1016",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.21",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Emerging technologies for structure matching based on
surface descriptions have demonstrated their
effectiveness in many research fields. In particular,
they can be successfully applied to in silico studies
of structural biology. Protein activities, in fact, are
related to the external characteristics of these
macromolecules and the ability to match surfaces can be
important to infer information about their possible
functions and interactions. In this work, we present a
surface-matching algorithm, based on encoding the outer
morphology of proteins in images of local description,
which allows us to establish point-to-point
correlations among macromolecular surfaces using
image-processing functions. Discarding methods relying
on biological analysis of atomic structures and
expensive computational approaches based on energetic
studies, this algorithm can successfully be used for
macromolecular recognition by employing local surface
features.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{DiLena:2011:TOS,
author = "Pietro {Di Lena} and Piero Fariselli and Luciano
Margara and Marco Vassura and Rita Casadio",
title = "Is There an Optimal Substitution Matrix for Contact
Prediction with Correlated Mutations?",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "1017--1028",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.91",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Correlated mutations in proteins are believed to occur
in order to preserve the protein functional folding
through evolution. Their values can be deduced from
sequence and/or structural alignments and are
indicative of residue contacts in the protein
three-dimensional structure. A correlation among pairs
of residues is routinely evaluated with the Pearson
correlation coefficient and the MCLACHLAN similarity
matrix. In literature, there is no justification for
the adoption of the MCLACHLAN instead of other
substitution matrices. In this paper, we approach the
problem of computing the optimal similarity matrix for
contact prediction with correlated mutations, i.e., the
similarity matrix that maximizes the accuracy of
contact prediction with correlated mutations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huber:2011:MMT,
author = "Katharina T. Huber and Andreas Spillner and Rados law
Suchecki and Vincent Moulton",
title = "Metrics on Multilabeled Trees: Interrelationships and
Diameter Bounds",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "1029--1040",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.122",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Multilabeled trees or MUL-trees, for short, are trees
whose leaves are labeled by elements of some nonempty
finite set X such that more than one leaf may be
labeled by the same element of X. This class of trees
includes phylogenetic trees and tree shapes. MUL-trees
arise naturally in, for example, biogeography and gene
evolution studies and also in the area of phylogenetic
network reconstruction. In this paper, we introduce
novel metrics which may be used to compare MUL-trees,
most of which generalize well-known metrics on
phylogenetic trees and tree shapes. These metrics can
be used, for example, to better understand the space of
MUL-trees or to help visualize collections of
MUL-trees.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhao:2011:MKI,
author = "Xin Zhao and Leo Wang-Kit Cheung",
title = "Multiclass Kernel-Imbedded {Gaussian} Processes for
Microarray Data Analysis",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "1041--1053",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.85",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Identifying significant differentially expressed genes
of a disease can help understand the disease at the
genomic level. A hierarchical statistical model named
multiclass kernel-imbedded Gaussian process (mKIGP) is
developed under a Bayesian framework for a multiclass
classification problem using microarray gene expression
data. Specifically, based on a multinomial probit
regression setting, an empirically adaptive algorithm
with a cascading structure is designed to find
appropriate featuring kernels, to discover potentially
significant genes, and to make optimal tumor/cancer
class predictions. A Gibbs sampler is adopted as the
core of the algorithm to perform Bayesian inferences. A
prescreening procedure is implemented to alleviate the
computational complexity.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2011:PTN,
author = "Peng Zhang and Houqiang Li and Honghui Wang and Wong
Stephen and Xiaobo Zhou",
title = "Peak Tree: a New Tool for Multiscale Hierarchical
Representation and Peak Detection of Mass Spectrometry
Data",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "1054--1066",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.56",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Peak detection is one of the most important steps in
mass spectrometry (MS) analysis. However, the detection
result is greatly affected by severe spectrum
variations. Unfortunately, most current peak detection
methods are neither flexible enough to revise false
detection results nor robust enough to resist spectrum
variations. To improve flexibility, we introduce peak
tree to represent the peak information in MS spectra.
Each tree node is a peak judgment on a range of scales,
and each tree decomposition, as a set of nodes, is a
candidate peak detection result. To improve robustness,
we combine peak detection and common peak alignment
into a closed-loop framework, which finds the optimal
decomposition via both peak intensity and common peak
information.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{El-Manzalawy:2011:PMI,
author = "Yasser El-Manzalawy and Drena Dobbs and Vasant
Honavar",
title = "Predicting {MHC-II} Binding Affinity Using Multiple
Instance Regression",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "1067--1079",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.94",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Reliably predicting the ability of antigen peptides to
bind to major histocompatibility complex class II
(MHC-II) molecules is an essential step in developing
new vaccines. Uncovering the amino acid sequence
correlates of the binding affinity of MHC-II binding
peptides is important for understanding pathogenesis
and immune response. The task of predicting MHC-II
binding peptides is complicated by the significant
variability in their length. Most existing
computational methods for predicting MHC-II binding
peptides focus on identifying a nine amino acids core
region in each binding peptide. We formulate the
problems of qualitatively and quantitatively predicting
flexible length MHC-II peptides as multiple instance
learning and multiple instance regression problems,
respectively. Based on this formulation, we introduce
MHCMIR, a novel method for predicting MHC-II binding
affinity using multiple instance regression. We present
results of experiments using several benchmark data
sets that show that MHCMIR is competitive with the
state-of-the-art methods for predicting MHC-II binding
peptides. An online web server that implements the
MHCMIR method for MHC-II binding affinity prediction is
freely accessible at
\path=http://ailab.cs.iastate.edu/mhcmir/=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yang:2011:RFS,
author = "Feng Yang and K. Z. Mao",
title = "Robust Feature Selection for Microarray Data Based on
Multicriterion Fusion",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "1080--1092",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.103",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Feature selection often aims to select a compact
feature subset to build a pattern classifier with
reduced complexity, so as to achieve improved
classification performance. From the perspective of
pattern analysis, producing stable or robust solution
is also a desired property of a feature selection
algorithm. However, the issue of robustness is often
overlooked in feature selection. In this study, we
analyze the robustness issue existing in feature
selection for high-dimensional and small-sized
gene-expression data, and propose to improve robustness
of feature selection algorithm by using multiple
feature selection evaluation criteria. Based on this
idea, a multicriterion fusion-based recursive feature
elimination (MCF-RFE) algorithm is developed with the
goal of improving both classification performance and
stability of feature selection results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tino:2011:SCG,
author = "Peter Ti{\v{n}}o and Hongya Zhao and Hong Yan",
title = "Searching for Coexpressed Genes in Three-Color {cDNA}
Microarray Data Using a Probabilistic Model-Based
{Hough Transform}",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "1093--1107",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.120",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The effects of a drug on the genomic scale can be
assessed in a three-color cDNA microarray with the
three color intensities represented through the
so-called hexaMplot. In our recent study, we have shown
that the Hough Transform (HT) applied to the hexaMplot
can be used to detect groups of coexpressed genes in
the normal-disease-drug samples. However, the standard
HT is not well suited for the purpose because (1) the
assayed genes need first to be hard-partitioned into
equally and differentially expressed genes, with HT
ignoring possible information in the former group; (2)
the hexaMplot coordinates are negatively correlated and
there is no direct way of expressing this in the
standard HT and (3) it is not clear how to quantify the
association of coexpressed genes with the line along
which they cluster.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2011:IMP,
author = "Li-San Wang and Jim Leebens-Mack and P. Kerr Wall and
Kevin Beckmann and Claude W. dePamphilis and Tandy
Warnow",
title = "The Impact of Multiple Protein Sequence Alignment on
Phylogenetic Estimation",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "1108--1119",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2009.68",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Multiple sequence alignment is typically the first
step in estimating phylogenetic trees, with the
assumption being that as alignments improve, so will
phylogenetic reconstructions. Over the last decade or
so, new multiple sequence alignment methods have been
developed to improve comparative analyses of protein
structure, but these new methods have not been
typically used in phylogenetic analyses. In this paper,
we report on a simulation study that we performed to
evaluate the consequences of using these new multiple
sequence alignment methods in terms of the resultant
phylogenetic reconstruction. We find that while
alignment accuracy is positively correlated with
phylogenetic accuracy, the amount of improvement in
phylogenetic estimation that results from an improved
alignment can range from quite small to substantial.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sadjad:2011:TRS,
author = "Bashir Sadjad and Zsolt Zsoldos",
title = "Toward a Robust Search Method for the Protein--Drug
Docking Problem",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "1120--1133",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.70",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Predicting the binding mode(s) of a drug molecule to a
target receptor is pivotal in structure-based rational
drug design. In contrast to most approaches to solve
this problem, the idea in this paper is to analyze the
search problem from a computational perspective. By
building on top of an existing docking tool, new
methods are proposed and relevant computational results
are proven. These methods and results are applicable
for other place-and-join frameworks as well. A fast
approximation scheme for the docking of rigid fragments
is described that guarantees certain geometric
approximation factors. It is also demonstrated that
this can be translated into an energy approximation for
simple scoring functions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2011:ARD,
author = "Yang Chen and Jinglu Hu",
title = "Accurate Reconstruction for {DNA} Sequencing by
Hybridization Based on a Constructive Heuristic",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "1134--1140",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.89",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Sequencing by hybridization is a promising
cost-effective technology for high-throughput DNA
sequencing via microarray chips. However, due to the
effects of spectrum errors rooted in experimental
conditions, an accurate and fast reconstruction of
original sequences has become a challenging problem. In
the last decade, a variety of analyses and designs have
been tried to overcome this problem, where different
strategies have different trade-offs in speed and
accuracy. Motivated by the idea that the errors could
be identified by analyzing the interrelation of
spectrum elements, this paper presents a constructive
heuristic algorithm, featuring an accurate
reconstruction guided by a set of well-defined criteria
and rules.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Guillemot:2011:CSM,
author = "Sylvain Guillemot and Jesper Jansson and Wing-Kin
Sung",
title = "Computing a Smallest Multilabeled Phylogenetic Tree
from Rooted Triplets",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "1141--1147",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.77",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Peters:2011:TSC,
author = "Tim Peters and David W. Bulger and To-ha Loi and Jean
Yee Hwa Yang and David Ma",
title = "Two-Step Cross-Entropy Feature Selection for
Microarrays --- Power Through Complementarity",
journal = j-TCBB,
volume = "8",
number = "4",
pages = "1148--1151",
month = jul,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.30",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 27 10:53:41 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Current feature selection methods for supervised
classification of tissue samples from microarray data
generally fail to exploit complementary discriminatory
power that can be found in sets of features [CHECK END
OF SENTENCE]. Using a feature selection method with the
computational architecture of the cross-entropy method
[CHECK END OF SENTENCE], including an additional
preliminary step ensuring a lower bound on the number
of times any feature is considered, we show when
testing on a human lymph node data set that there are a
significant number of genes that perform well when
their complementary power is assessed, but ``pass under
the radar'' of popular feature selection methods that
only assess genes individually on a given
classification tool.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhu:2011:GMA,
author = "Dongxiao Zhu and Lipi Acharya and Hui Zhang",
title = "A Generalized Multivariate Approach to Pattern
Discovery from Replicated and Incomplete Genome-Wide
Measurements",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1153--1169",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.102",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chang:2011:NKD,
author = "Rui Chang and Robert Shoemaker and Wei Wang",
title = "A Novel Knowledge-Driven Systems Biology Approach for
Phenotype Prediction upon Genetic Intervention",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1170--1182",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.18",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Irurozki:2011:PPH,
author = "Ekhine Irurozki and Borja Calvo and Jose A. Lozano",
title = "A Preprocessing Procedure for Haplotype Inference by
Pure Parsimony",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1183--1195",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.125",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Battagliero:2011:EAA,
author = "Simone Battagliero and Giuseppe Puglia and Saverio
Vicario and Francesco Rubino and Gaetano Scioscia and
Pietro Leo",
title = "An Efficient Algorithm for Approximating Geodesic
Distances in Tree Space",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1196--1207",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.121",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{John:2011:CCP,
author = "David J. John and Jacquelyn S. Fetrow and James L.
Norris",
title = "Continuous Cotemporal Probabilistic Modeling of
Systems Biology Networks from Sparse Data",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1208--1222",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.95",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Guziolowski:2011:DLR,
author = "Carito Guziolowski and Sylvain Blachon and Tatiana
Baumuratova and Gautier Stoll and Ovidiu Radulescu and
Anne Siegel",
title = "Designing Logical Rules to Model the Response of
Biomolecular Networks with Complex Interactions: An
Application to Cancer Modeling",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1223--1234",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.71",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sahu:2011:ELH,
author = "Sitanshu Sekhar Sahu and Ganapati Panda",
title = "Efficient Localization of Hot Spots in Proteins Using
a Novel {$S$}-Transform Based Filtering Approach",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1235--1246",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.109",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Flores:2011:FFM,
author = "Samuel Coulbourn Flores and Michael Sherman and
Christopher M. Bruns and Peter Eastman and Russ B.
Altman",
title = "Fast Flexible Modeling of {RNA} Structure Using
Internal Coordinates",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1247--1257",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.104",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2011:IAF,
author = "Biing-Feng Wang and Chien-Hsin Lin",
title = "Improved Algorithms for Finding Gene Teams and
Constructing Gene Team Trees",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1258--1272",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.127",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zheng:2011:MBS,
author = "Chun-Hou Zheng and Lei Zhang and To-Yee Ng and Chi
Keung Shiu and De-Shuang Huang",
title = "Metasample-Based Sparse Representation for Tumor
Classification",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1273--1282",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.20",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Peng:2011:MSA,
author = "Qian Peng and Andrew D. Smith",
title = "Multiple Sequence Assembly from Reads Alignable to a
Common Reference Genome",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1283--1295",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.107",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Betzler:2011:PAF,
author = "Nadja Betzler and Rene van Bevern and Michael R.
Fellows and Christian Komusiewicz and Rolf
Niedermeier",
title = "Parameterized Algorithmics for Finding Connected
Motifs in Biological Networks",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1296--1308",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.19",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kim:2011:PMS,
author = "Jong Kyoung Kim and Seungjin Choi",
title = "Probabilistic Models for Semisupervised Discriminative
Motif Discovery in {DNA} Sequences",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1309--1317",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.84",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Feijao:2011:SBL,
author = "Pedro Feijao and Joao Meidanis",
title = "{SCJ}: a Breakpoint-Like Distance that Simplifies
Several Rearrangement Problems",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1318--1329",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.34",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mernberger:2011:SSG,
author = "Marco Mernberger and Gerhard Klebe and Eyke
Hullermeier",
title = "{SEGA}: Semiglobal Graph Alignment for Structure-Based
Protein Comparison",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1330--1343",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.35",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Boyen:2011:SGM,
author = "Peter Boyen and Dries {Van Dyck} and Frank Neven and
Roeland C. H. J. van Ham and Aalt D. J. van Dijk",
title = "{SLIDER}: a Generic Metaheuristic for the Discovery of
Correlated Motifs in Protein-Protein Interaction
Networks",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1344--1357",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.17",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Buhrman:2011:SMR,
author = "Harry Buhrman and Peter T. S. van der Gulik and Steven
M. Kelk and Wouter M. Koolen and Leen Stougie",
title = "Some Mathematical Refinements Concerning Error
Minimization in the Genetic Code",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1358--1372",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.40",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wong:2011:UKA,
author = "William W. L. Wong and Forbes J. Burkowski",
title = "Using Kernel Alignment to Select Features of Molecular
Descriptors in a {QSAR} Study",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1373--1384",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.31",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Muselli:2011:MMV,
author = "Marco Muselli and Alberto Bertoni and Marco Frasca and
Alessandro Beghini and Francesca Ruffino and Giorgio
Valentini",
title = "A Mathematical Model for the Validation of Gene
Selection Methods",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1385--1392",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.83",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dubrova:2011:SBA,
author = "Elena Dubrova and Maxim Teslenko",
title = "A {SAT}-Based Algorithm for Finding Attractors in
Synchronous {Boolean} Networks",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1393--1399",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.20",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2011:FEA,
author = "Zhi-Zhong Chen and Lusheng Wang",
title = "Fast Exact Algorithms for the Closest String and
Substring Problems with Application to the Planted {$
(L, d) $}-Motif Model",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1400--1410",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.21",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Grusea:2011:DNC,
author = "Simona Grusea",
title = "On the Distribution of the Number of Cycles in the
Breakpoint Graph of a Random Signed Permutation",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1411--1416",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.123",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Befekadu:2011:PMR,
author = "Getachew K. Befekadu and Mahlet G. Tadesse and
Tsung-Heng Tsai and Habtom W. Ressom",
title = "Probabilistic Mixture Regression Models for Alignment
of {LC-MS} Data",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1417--1424",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.88",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Magni:2011:SPI,
author = "Paolo Magni and Angela Simeone and Sandra Healy and
Antonella Isacchi and Roberta Bosotti",
title = "Summarizing Probe Intensities of {Affymetrix GeneChip
3'} Expression Arrays Taking into Account Day-to-Day
Variability",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1425--1430",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.82",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Aharoni:2011:QPD,
author = "Ehud Aharoni and Hani Neuvirth and Saharon Rosset",
title = "The Quality Preserving Database: a Computational
Framework for Encouraging Collaboration, Enhancing
Power and Controlling False Discovery",
journal = j-TCBB,
volume = "8",
number = "5",
pages = "1431--1437",
month = sep,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.105",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Aug 17 09:10:05 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Axenopoulos:2011:SDF,
author = "Apostolos Axenopoulos and Petros Daras and Georgios
Papadopoulos and Elias Houstis",
title = "A Shape Descriptor for Fast Complementarity Matching
in Molecular Docking",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1441--1457",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.72",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhao:2011:ASM,
author = "Wenqi Zhao and Guoliang Xu and Chandrajit L. Bajaj",
title = "An Algebraic Spline Model of Molecular Surfaces for
Energetic Computations",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1458--1467",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.81",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nebel:2011:AFE,
author = "Markus E. Nebel and Scheid Anika",
title = "Analysis of the Free Energy in a Stochastic {RNA}
Secondary Structure Model",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1468--1482",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.126",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhao:2011:ASB,
author = "Liang Zhao and Limsoon Wong and Jinyan Li",
title = "Antibody-Specified {B}-Cell Epitope Prediction in Line
with the Principle of Context-Awareness",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1483--1494",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.49",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cobanoglu:2011:CGU,
author = "Murat Can Cobanoglu and Yucel Saygin and Ugur
Sezerman",
title = "Classification of {GPCRs} Using Family Specific
Motifs",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1495--1508",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.101",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2011:CED,
author = "Qi Li and Chandra Kambhamettu",
title = "Contour Extraction of \bioname{Drosophila} Embryos",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1509--1521",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.37",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Oh:2011:FKD,
author = "Jung Hun Oh and Jean Gao",
title = "Fast Kernel Discriminant Analysis for Classification
of Liver Cancer Mass Spectra",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1522--1534",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.42",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2011:FAP,
author = "Qingfeng Chen and Yi-Ping Phoebe Chen",
title = "Function Annotation for Pseudoknot Using Structure
Similarity",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1535--1544",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.50",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chalkidis:2011:HPH,
author = "Georgios Chalkidis and Masao Nagasaki and Satoru
Miyano",
title = "High Performance Hybrid Functional {Petri} Net
Simulations of Biological Pathway Models on {CUDA}",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1545--1556",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.118",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2011:HHT,
author = "Helong Li and Sam Kwong and Lihua Yang and Daren Huang
and Dongping Xiao",
title = "{Hilbert--Huang Transform} for Analysis of Heart Rate
Variability in Cardiac Health",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1557--1567",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.43",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sheng:2011:IAG,
author = "Jinhua Sheng and Hong-Wen Deng and Vince Calhoun and
Yu-Ping Wang",
title = "Integrated Analysis of Gene Expression and Copy Number
Data on Gene Shaving Using Independent Component
Analysis",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1568--1579",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.71",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2011:MIS,
author = "Carla C. M. Chen and Holger Schwender and Jonthan
Keith and Robin Nunkesser and Kerrie Mengersen and
Paula Macrossan",
title = "Methods for Identifying {SNP} Interactions: a Review
on Variations of Logic Regression, Random Forest and
{Bayesian} Logistic Regression",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1580--1591",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.46",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zheng:2011:MPD,
author = "Chun-Hou Zheng and Lei Zhang and Vincent To-Yee Ng and
Chi Keung Shiu and D.-S. Huang",
title = "Molecular Pattern Discovery Based on Penalized Matrix
Decomposition",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1592--1603",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.79",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{LeFaucheur:2011:NCS,
author = "Xavier {Le Faucheur} and Eli Hershkovits and Rina
Tannenbaum and Allen Tannenbaum",
title = "Nonparametric Clustering for Studying {RNA}
Conformations",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1604--1619",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.128",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dotu:2011:LPS,
author = "Ivan Dotu and Manuel Cebrian and Pascal {Van
Hentenryck} and Peter Clote",
title = "On Lattice Protein Structure Prediction Revisited",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1620--1632",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.41",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2011:RUP,
author = "Hong-Dong Li and Yi-Zeng Liang and Qing-Song Xu and
Dong-Sheng Cao and Bin-Bin Tan and Bai-Chuan Deng and
Chen-Chen Lin",
title = "Recipe for Uncovering Predictive Genes Using Support
Vector Machines Based on Model Population Analysis",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1633--1641",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.36",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hafemeister:2011:SOP,
author = "Christoph Hafemeister and Roland Krause and Alexander
Schliep",
title = "Selecting Oligonucleotide Probes for Whole-Genome
Tiling Arrays with a Cross-Hybridization Potential",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1642--1652",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.39",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fober:2011:SAL,
author = "Thomas Fober and Gerghei Glinca and Gerhard Klebe and
Eyke Hullermeier",
title = "Superposition and Alignment of Labeled Point Clouds",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1653--1666",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.42",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tuller:2011:CEI,
author = "Tamir Tuller and Elchanan Mossel",
title = "Co-evolution Is Incompatible with the {Markov}
Assumption in Phylogenetics",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1667--1670",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.124",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sun:2011:CFS,
author = "Bing-Yu Sun and Zhi-Hua Zhu and Jiuyong Li and Bin
Linghu",
title = "Combined Feature Selection and Cancer Prognosis Using
Support Vector Machine Regression",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1671--1677",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.119",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2011:CBA,
author = "Weiguo Liu and Bertil Schmidt and Wolfgang
Muller-Wittig",
title = "{CUDA-BLASTP}: Accelerating {BLASTP} on {CUDA}-Enabled
Graphics Hardware",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1678--1684",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.33",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2011:GTS,
author = "Louxin Zhang",
title = "From Gene Trees to Species Trees {II}: Species Tree
Inference by Minimizing Deep Coalescence Events",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1685--1691",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.83",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fellows:2011:HIC,
author = "Michael R. Fellows and Tzvika Hartman and Danny
Hermelin and Gad M. Landau and Frances Rosamond and
Liat Rozenberg",
title = "Haplotype Inference Constrained by Plausible Haplotype
Data",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1692--1699",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.72",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ambroise:2011:IRP,
author = "Jerome Ambroise and Joachim Giard and Jean-Luc Gala
and Benoit Macq",
title = "Identification of Relevant Properties for Epitopes
Detection Using a Regression Model",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1700--1707",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.77",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2011:ICA,
author = "Qingguo Wang and Yi Shang and Dong Xu",
title = "Improving a Consensus Approach for Protein Structure
Selection by Removing Redundancy",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1708--1715",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.75",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Poleksic:2011:OWU,
author = "Aleksandar Poleksic",
title = "Optimizing a Widely Used Protein Structure Alignment
Measure in Expected Polynomial Time",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1716--1720",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.122",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rodrigues:2011:PCE,
author = "Thiago de Souza Rodrigues and Fernanda Caldas Cardoso
and Santuza Maria Ribeiro Teixeira and Sergio Costa
Oliveira and Antonio Padua Braga",
title = "Protein Classification with Extended-Sequence Coding
by Sliding Window",
journal = j-TCBB,
volume = "8",
number = "6",
pages = "1721--1726",
month = nov,
year = "2011",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.78",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sun Nov 6 06:45:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{DeBlasio:2012:MEM,
author = "Daniel DeBlasio and Jocelyne Bruand and Shaojie
Zhang",
title = "A Memory Efficient Method for Structure-Based {RNA}
Multiple Alignment",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "1--11",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.86",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pirola:2012:EAH,
author = "Yuri Pirola and Paola Bonizzoni and Tao Jiang",
title = "An Efficient Algorithm for Haplotype Inference on
Pedigrees with Recombinations and Mutations",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "12--25",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.51",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Doyon:2012:EME,
author = "Jean-Philippe Doyon and Sylvie Hamel and Cedric
Chauve",
title = "An Efficient Method for Exploring the Space of Gene
Tree\slash Species Tree Reconciliations in a
Probabilistic Framework",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "26--39",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.64",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2012:EMM,
author = "Chong Wang and Peter Beyerlein and Heike Pospisil and
Antje Krause and Chris Nugent and Werner Dubitzky",
title = "An Efficient Method for Modeling Kinetic Behavior of
Channel Proteins in Cardiomyocytes",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "40--51",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.84",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Vasic:2012:ITA,
author = "Bane Vasic and Vida Ravanmehr and Anantha Raman
Krishnan",
title = "An Information Theoretic Approach to Constructing
Robust {Boolean} Gene Regulatory Networks",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "52--65",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.61",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Piraveenan:2012:AMD,
author = "Mahendra Piraveenan and Mikhail Prokopenko and Albert
Zomaya",
title = "Assortative Mixing in Directed Biological Networks",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "66--78",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.80",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chan:2012:CVM,
author = "Raymond H. Chan and Tony H. Chan and Hau Man Yeung and
Roger Wei Wang",
title = "Composition Vector Method Based on Maximum Entropy
Principle for Sequence Comparison",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "79--87",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.45",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Quevedo:2012:DLP,
author = "Jose R. Quevedo and Antonio Bahamonde and Miguel
Perez-Enciso and Oscar Luaces",
title = "Disease Liability Prediction from Large Scale
Genotyping Data Using Classifiers with a Reject
Option",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "88--97",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.44",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2012:DGE,
author = "Ying-Xin Li and Shuiwang Ji and Sudhir Kumar and
Jieping Ye and Zhi-Hua Zhou",
title = "\bioname{Drosophila} Gene Expression Pattern Annotation
through Multi-Instance Multi-Label Learning",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "98--112",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.73",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Paoletti:2012:INC,
author = "David R. Paoletti and Dan E. Krane and Michael L.
Raymer and Travis E. Doom",
title = "Inferring the Number of Contributors to Mixed {DNA}
Profiles",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "113--122",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.76",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Qian:2012:IGR,
author = "Xiaoning Qian and Edward R. Dougherty",
title = "Intervention in Gene Regulatory Networks via
Phenotypically Constrained Control Policies Based on
Long-Run Behavior",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "123--136",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.107",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kuruppu:2012:IDC,
author = "Shanika Kuruppu and Bryan Beresford-Smith and Thomas
Conway and Justin Zobel",
title = "Iterative Dictionary Construction for Compression of
Large {DNA} Data Sets",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "137--149",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.82",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bogdanowicz:2012:MSD,
author = "Damian Bogdanowicz and Krzysztof Giaro",
title = "Matching Split Distance for Unrooted Binary
Phylogenetic Trees",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "150--160",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.48",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wong:2012:MEA,
author = "Thomas K. F. Wong and Y. S. Chiu and T. W. Lam and S.
M. Yiu",
title = "Memory Efficient Algorithms for Structural Alignment
of {RNAs} with Pseudoknots",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "161--168",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.66",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mahoney:2012:MOB,
author = "Arthur W. Mahoney and Gregory J. Podgorski and
Nicholas S. Flann",
title = "Multiobjective Optimization Based-Approach for
Discovering Novel Cancer Therapies",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "169--184",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2010.39",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sun:2012:PEU,
author = "Jianyong Sun and Jonathan M. Garibaldi and Charlie
Hodgman",
title = "Parameter Estimation Using Metaheuristics in Systems
Biology: a Comprehensive Review",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "185--202",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.63",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Passerini:2012:PMB,
author = "Andrea Passerini and Marco Lippi and Paolo Frasconi",
title = "Predicting Metal-Binding Sites from Protein Sequence",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "203--213",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.94",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bokhari:2012:RNE,
author = "Shahid H. Bokhari and Laura W. Pomeroy and Daniel A.
Janies",
title = "Reassortment Networks and the Evolution of Pandemic
{H1N1} Swine-Origin Influenza",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "214--227",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.95",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ma:2012:RNH,
author = "Lingyu Ma and Marco Reisert and Hans Burkhardt",
title = "{RENNSH}: a Novel $ \alpha $-Helix Identification
Approach for Intermediate Resolution Electron Density
Maps",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "228--239",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.52",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2012:RSH,
author = "Shuai Cheng Li and Dongbo Bu and Ming Li",
title = "Residues with Similar Hexagon Neighborhoods Share
Similar Side-Chain Conformations",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "240--248",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.74",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sun:2012:SSM,
author = "Hong Sun and Ahmet Sacan and Hakan Ferhatosmanoglu and
Yusu Wang",
title = "{Smolign}: a Spatial Motifs-Based Protein Multiple
Structural Alignment Method",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "249--261",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.67",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2012:SGS,
author = "Lei Yu and Yue Han and Michael E. Berens",
title = "Stable Gene Selection from Microarray Data via Sample
Weighting",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "262--272",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.47",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bonet:2012:CFM,
author = "Maria Luisa Bonet and Simone Linz and Katherine {St.
John}",
title = "The Complexity of Finding Multiple Solutions to
Betweenness and Quartet Compatibility",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "273--285",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.108",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Daniels:2012:TPS,
author = "Noah Daniels and Anoop Kumar and Lenore Cowen and Matt
Menke",
title = "Touring Protein Space with {Matt}",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "286--293",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.70",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xiang:2012:TDT,
author = "Yang Xiang and Philip R. O. Payne and Kun Huang",
title = "Transactional Database Transformation and Its
Application in Prioritizing Human Disease Genes",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "294--304",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.58",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ambert:2012:IGS,
author = "Kyle H. Ambert and Aaron M. Cohen",
title = "$k$-Information Gain Scaled Nearest Neighbors: a Novel
Approach to Classifying Protein-Protein
Interaction-Related Documents",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "305--310",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.32",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2012:MLM,
author = "Yun Xu and Da Teng and Yiming Lei",
title = "{MinePhos}: a Literature Mining System for Protein
Phoshphorylation Information Extraction",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "311--315",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.85",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2012:RL,
author = "Yun Xu and Da Teng and Yiming Lei",
title = "2011 Reviewers List",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "316--318",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.2",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2012:AI,
author = "Yun Xu and Da Teng and Yiming Lei",
title = "2011 Annual Index",
journal = j-TCBB,
volume = "9",
number = "1",
pages = "??--??",
month = jan,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.1",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Dec 15 08:25:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Acharya:2012:GCA,
author = "Lipi Acharya and Thair Judeh and Zhansheng Duan and
Michael Rabbat and Dongxiao Zhu",
title = "{GSGS}: a Computational Approach to Reconstruct
Signaling Pathway Structures from Gene Sets",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "438--450",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.143",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Reconstruction of signaling pathway structures is
essential to decipher complex regulatory relationships
in living cells. The existing computational approaches
often rely on unrealistic biological assumptions and do
not explicitly consider signal transduction mechanisms.
Signal transduction events refer to linear cascades of
reactions from the cell surface to the nucleus and
characterize a signaling pathway. In this paper, we
propose a novel approach, Gene Set Gibbs Sampling
(GSGS), to reverse engineer signaling pathway
structures from gene sets related to the pathways. We
hypothesize that signaling pathways are structurally an
ensemble of overlapping linear signal transduction
events which we encode as Information Flows (IFs).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Andreotti:2012:ALR,
author = "Sandro Andreotti and Gunnar W. Klau and Knut Reinert",
title = "{Antilope} --- a {Lagrangian} Relaxation Approach to
the de novo Peptide Sequencing Problem",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "385--394",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.59",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Peptide sequencing from mass spectrometry data is a
key step in proteome research. Especially de novo
sequencing, the identification of a peptide from its
spectrum alone, is still a challenge even for
state-of-the-art algorithmic approaches. In this paper,
we present antilope, a new fast and flexible approach
based on mathematical programming. It builds on the
spectrum graph model and works with a variety of
scoring schemes. antilope combines Lagrangian
relaxation for solving an integer linear programming
formulation with an adaptation of Yen's $k$ shortest
paths algorithm. It shows a significant improvement in
running time compared to mixed integer optimization and
performs at the same speed like other state-of-the-art
tools.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Angadi:2012:SSS,
author = "Ulavappa B. Angadi and M. Venkatesulu",
title = "Structural {SCOP} Superfamily Level Classification
Using Unsupervised Machine Learning",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "601--608",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.114",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "One of the major research directions in bioinformatics
is that of assigning superfamily classification to a
given set of proteins. The classification reflects the
structural, evolutionary, and functional relatedness.
These relationships are embodied in a hierarchical
classification, such as the Structural Classification
of Protein (SCOP), which is mostly manually curated.
Such a classification is essential for the structural
and functional analyses of proteins. Yet a large number
of proteins remain unclassified. In this study, we have
proposed an unsupervised machine learning approach to
classify and assign a given set of proteins to SCOP
superfamilies. In the method, we have constructed a
database and similarity matrix using P-values obtained
from an all-against-all BLAST run and trained the
network with the ART2 unsupervised learning algorithm
using the rows of the similarity matrix as input
vectors, enabling the trained network to classify the
proteins from 0.82 to 0.97 f-measure accuracy.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{DiCamillo:2012:SSB,
author = "Barbara {Di Camillo} and Marco Falda and Gianna
Toffolo and Claudio Cobelli",
title = "{SimBioNeT}: a Simulator of Biological Network
Topology",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "592--600",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.116",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Studying biological networks at topological level is a
major issue in computational biology studies and
simulation is often used in this context, either to
assess reverse engineering algorithms or to investigate
how topological properties depend on network
parameters. In both contexts, it is desirable for a
topology simulator to reproduce the current knowledge
on biological networks, to be able to generate a number
of networks with the same properties and to be flexible
with respect to the possibility to mimic networks of
different organisms. We propose a biological network
topology simulator, SimBioNeT, in which module
structures of different type and size are replicated at
different level of network organization and
interconnected, so to obtain the desired degree
distribution, e.g., scale free, and a clustering
coefficient constant with the number of nodes in the
network, a typical characteristic of biological
networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2012:ARN,
author = "Zhi-Zhong Chen and Lusheng Wang",
title = "Algorithms for Reticulate Networks of Multiple
Phylogenetic Trees",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "372--384",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.137",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A reticulate network $N$ of multiple phylogenetic
trees may have nodes with two or more parents (called
reticulation nodes). There are two ways to define the
reticulation number of $N$. One way is to define it as
the number of reticulation nodes in $N$ in this case, a
reticulate network with the smallest reticulation
number is called an optimal type-I reticulate network
of the trees. The better way is to define it as the
total number of parents of reticulation nodes in $N$
minus the number of reticulation nodes in $N$; in this
case, a reticulate network with the smallest
reticulation number is called an optimal type-II
reticulate network of the trees.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cheng:2012:MDT,
author = "Yi-Ming Cheng and Srinivasa Murthy Gopal and Sean M.
Law and Michael Feig",
title = "Molecular Dynamics Trajectory Compression with a
Coarse-Grained Model",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "476--486",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.141",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Molecular dynamics trajectories are very data
intensive thereby limiting sharing and archival of such
data. One possible solution is compression of
trajectory data. Here, trajectory compression based on
conversion to the coarse-grained model PRIMO is
proposed. The compressed data are about one third of
the original data and fast decompression is possible
with an analytical reconstruction procedure from PRIMO
to all-atom representations. This protocol largely
preserves structural features and to a more limited
extent also energetic features of the original
trajectory.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{ElBakry:2012:IDE,
author = "Ola ElBakry and M. Omair Ahmad and M. N. S. Swamy",
title = "Identification of Differentially Expressed Genes for
Time-Course Microarray Data Based on Modified {RM}
{ANOVA}",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "451--466",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.65",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The regulation of gene expression is a dynamic
process, hence it is of vital interest to identify and
characterize changes in gene expression over time. We
present here a general statistical method for detecting
changes in microarray expression over time within a
single biological group and is based on repeated
measures (RM) ANOVA. In this method, unlike the
classical F-statistic, statistical significance is
determined taking into account the time dependency of
the microarray data. A correction factor for this RM
F-statistic is introduced leading to a higher
sensitivity as well as high specificity. We investigate
the two approaches that exist in the literature for
calculating the p-values using resampling techniques of
gene-wise p-values and pooled p-values.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Feng:2012:LSL,
author = "Zeny Z. Feng and Xiaojian Yang and Sanjeena Subedi and
Paul D. McNicholas",
title = "The {LASSO} and Sparse Least Squares Regression
Methods for {SNP} Selection in Predicting Quantitative
Traits",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "629--636",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.139",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recent work concerning quantitative traits of interest
has focused on selecting a small subset of single
nucleotide polymorphisms (SNPs) from among the SNPs
responsible for the phenotypic variation of the trait.
When considered as covariates, the large number of
variables (SNPs) and their association with those in
close proximity pose challenges for variable selection.
The features of sparsity and shrinkage of regression
coefficients of the least absolute shrinkage and
selection operator (LASSO) method appear attractive for
SNP selection. Sparse partial least squares (SPLS) is
also appealing as it combines the features of sparsity
in subset selection and dimension reduction to handle
correlations among SNPs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hopfensitz:2012:MBG,
author = "Martin Hopfensitz and Christoph Mussel and Christian
Wawra and Markus Maucher and Michael Kuhl and Heiko
Neumann and Hans A. Kestler",
title = "Multiscale Binarization of Gene Expression Data for
Reconstructing {Boolean} Networks",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "487--498",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.62",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Network inference algorithms can assist life
scientists in unraveling gene-regulatory systems on a
molecular level. In recent years, great attention has
been drawn to the reconstruction of Boolean networks
from time series. These need to be binarized, as such
networks model genes as binary variables (either
``expressed'' or ``not expressed''). Common
binarization methods often cluster measurements or
separate them according to statistical or information
theoretic characteristics and may require many data
points to determine a robust threshold. Yet, time
series measurements frequently comprise only a small
number of samples. To overcome this limitation, we
propose a binarization that incorporates measurements
at multiple resolutions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huang:2012:PEL,
author = "Qinghua Huang and Dacheng Tao and Xuelong Li and Alan
Liew",
title = "Parallelized Evolutionary Learning for Detection of
Biclusters in Gene Expression Data",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "560--570",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.53",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The analysis of gene expression data obtained from
microarray experiments is important for discovering the
biological process of genes. Biclustering algorithms
have been proven to be able to group the genes with
similar expression patterns under a number of
experimental conditions. In this paper, we propose a
new biclustering algorithm based on evolutionary
learning. By converting the biclustering problem into a
common clustering problem, the algorithm can be applied
in a search space constructed by the conditions. To
further reduce the size of the search space, we
randomly separate the full conditions into a number of
condition subsets (subspaces), each of which has a
smaller number of conditions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Karafyllidis:2012:QGC,
author = "Ioannis G. Karafyllidis",
title = "Quantum Gate Circuit Model of Signal Integration in
Bacterial Quorum Sensing",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "571--579",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.104",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Bacteria evolved cell to cell communication processes
to gain information about their environment and
regulate gene expression. Quorum sensing is such a
process in which signaling molecules, called
autoinducers, are produced, secreted and detected. In
several cases bacteria use more than one autoinducers
and integrate the information conveyed by them. It has
not yet been explained adequately why bacteria evolved
such signal integration circuits and what can learn
about their environments using more than one
autoinducers since all signaling pathways merge in one.
Here quantum information theory, which includes
classical information theory as a special case, is used
to construct a quantum gate circuit that reproduces
recent experimental results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kelk:2012:EC,
author = "Steven Kelk and Celine Scornavacca and Leo van
Iersel",
title = "On the Elusiveness of Clusters",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "517--534",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.128",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Rooted phylogenetic networks are often used to
represent conflicting phylogenetic signals. Given a set
of clusters, a network is said to represent these
clusters in the softwired sense if, for each cluster in
the input set, at least one tree embedded in the
network contains that cluster. Motivated by parsimony
we might wish to construct such a network using as few
reticulations as possible, or minimizing the level of
the network, i.e., the maximum number of reticulations
used in any ``tangled'' region of the network. Although
these are NP-hard problems, here we prove that, for
every fixed $ k \ge 0 $, it is polynomial-time solvable
to construct a phylogenetic network with level equal to
$k$ representing a cluster set, or to determine that no
such network exists.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kentzoglanakis:2012:SIF,
author = "Kyriakos Kentzoglanakis and Matthew Poole",
title = "A Swarm Intelligence Framework for Reconstructing Gene
Networks: Searching for Biologically Plausible
Architectures",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "358--371",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.87",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, we investigate the problem of reverse
engineering the topology of gene regulatory networks
from temporal gene expression data. We adopt a
computational intelligence approach comprising swarm
intelligence techniques, namely particle swarm
optimization (PSO) and ant colony optimization (ACO).
In addition, the recurrent neural network (RNN)
formalism is employed for modeling the dynamical
behavior of gene regulatory systems. More specifically,
ACO is used for searching the discrete space of network
architectures and PSO for searching the corresponding
continuous space of RNN model parameters. We propose a
novel solution construction process in the context of
ACO for generating biologically plausible candidate
architectures.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kulekci:2012:EMR,
author = "M. Oguzhan Kulekci and Jeffrey Scott Vitter and Bojian
Xu",
title = "Efficient Maximal Repeat Finding Using the
{Burrows-Wheeler} Transform and Wavelet Tree",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "421--429",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.127",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Finding repetitive structures in genomes and proteins
is important to understand their biological functions.
Many data compressors for modern genomic sequences rely
heavily on finding repeats in the sequences.
Small-scale and local repetitive structures are better
understood than large and complex interspersed ones.
The notion of maximal repeats captures all the repeats
in the data in a space-efficient way. Prior work on
maximal repeat finding used either a suffix tree or a
suffix array along with other auxiliary data
structures. Their space usage is 19-50 times the text
size with the best engineering efforts, prohibiting
their usability on massive data such as the whole human
genome.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ma:2012:MRD,
author = "Wenji Ma and Yong Yang and Zhi-Zhong Chen and Lusheng
Wang",
title = "Mutation Region Detection for Closely Related
Individuals without a Known Pedigree",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "499--510",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.134",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Linkage analysis serves as a way of finding locations
of genes that cause genetic diseases. Linkage studies
have facilitated the identification of several hundreds
of human genes that can harbor mutations which by
themselves lead to a disease phenotype. The fundamental
problem in linkage analysis is to identify regions
whose allele is shared by all or almost all affected
members but by none or few unaffected members. Almost
all the existing methods for linkage analysis are for
families with clearly given pedigrees. Little work has
been done for the case where the sampled individuals
are closely related, but their pedigree is not known.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mimaroglu:2012:DDC,
author = "Selim Mimaroglu and Emin Aksehirli",
title = "{DICLENS}: Divisive Clustering Ensemble with Automatic
Cluster Number",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "408--420",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.129",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Clustering has a long and rich history in a variety of
scientific fields. Finding natural groupings of a data
set is a hard task as attested by hundreds of
clustering algorithms in the literature. Each
clustering technique makes some assumptions about the
underlying data set. If the assumptions hold, good
clusterings can be expected. It is hard, in some cases
impossible, to satisfy all the assumptions. Therefore,
it is beneficial to apply different clustering methods
on the same data set, or the same method with varying
input parameters or both. We propose a novel method,
DICLENS, which combines a set of clusterings into a
final clustering having better overall quality.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nanni:2012:IBV,
author = "Loris Nanni and Alessandra Lumini and Dinesh Gupta and
Aarti Garg",
title = "Identifying Bacterial Virulent Proteins by Fusing a
Set of Classifiers Based on Variants of {Chou}'s Pseudo
Amino Acid Composition and on Evolutionary
Information",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "467--475",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.117",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The availability of a reliable prediction method for
prediction of bacterial virulent proteins has several
important applications in research efforts targeted
aimed at finding novel drug targets, vaccine
candidates, and understanding virulence mechanisms in
pathogens. In this work, we have studied several
feature extraction approaches for representing proteins
and propose a novel bacterial virulent protein
prediction method, based on an ensemble of classifiers
where the features are extracted directly from the
amino acid sequence and from the evolutionary
information of a given protein. We have evaluated and
compared several ensembles obtained by combining six
feature extraction methods and several classification
approaches based on two general purpose classifiers
(i.e., Support Vector Machine and a variant of input
decimated ensemble) and their random subspace
version.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Phipps:2012:OPN,
author = "Paul Phipps and Sergey Bereg",
title = "Optimizing Phylogenetic Networks for Circular Split
Systems",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "535--547",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.109",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We address the problem of realizing a given distance
matrix by a planar phylogenetic network with a minimum
number of faces. With the help of the popular software
SplitsTree4, we start by approximating the distance
matrix with a distance metric that is a linear
combination of circular splits. The main results of
this paper are the necessary and sufficient conditions
for the existence of a network with a single face. We
show how such a network can be constructed, and we
present a heuristic for constructing a network with few
faces using the first algorithm as the base case.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Poleksic:2012:CPS,
author = "Aleksandar Poleksic",
title = "On Complexity of Protein Structure Alignment Problem
under Distance Constraint",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "511--516",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.133",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We study the well-known Largest Common Point-set (LCP)
under Bottleneck Distance Problem. Given two proteins
$a$ and $b$ (as sequences of points in
three-dimensional space) and a distance cutoff $ \sigma
$, the goal is to find a spatial superposition and an
alignment that maximizes the number of pairs of points
from $a$ and $b$ that can be fit under the distance $
\sigma $ from each other. The best to date algorithms
for approximate and exact solution to this problem run
in time $ O(n^8) $ and $ O(n^{32}) $, respectively,
where $n$ represents protein length. This work improves
runtime of the approximation algorithm and the expected
runtime of the algorithm for absolute optimum for both
order-dependent and order-independent alignments.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Spillner:2012:CDR,
author = "Andreas Spillner and Binh Nguyen and Vincent Moulton",
title = "Constructing and Drawing Regular Planar Split
Networks",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "395--407",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.115",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Split networks are commonly used to visualize
collections of bipartitions, also called splits, of a
finite set. Such collections arise, for example, in
evolutionary studies. Split networks can be viewed as a
generalization of phylogenetic trees and may be
generated using the SplitsTree package. Recently, the
NeighborNet method for generating split networks has
become rather popular, in part because it is guaranteed
to always generate a circular split system, which can
always be displayed by a planar split network. Even so,
labels must be placed on the ``outside'' of the
network, which might be problematic in some
applications. To help circumvent this problem, it can
be helpful to consider so-called flat split systems,
which can be displayed by planar split networks where
labels are allowed on the inside of the network too.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Steinbiss:2012:NED,
author = "Sascha Steinbiss and Stefan Kurtz",
title = "A New Efficient Data Structure for Storage and
Retrieval of Multiple Biosequences",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "345--357",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.146",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Today's genome analysis applications require sequence
representations allowing for fast access to their
contents while also being memory-efficient enough to
facilitate analyses of large-scale data. While a wide
variety of sequence representations exist, lack of a
generic implementation of efficient sequence storage
has led to a plethora of poorly reusable or programming
language-specific implementations. We present a novel,
space-efficient data structure (GtEncseq) for storing
multiple biological sequences of variable alphabet
size, with customizable character transformations,
wildcard support, and an assortment of internal
representations optimized for different distributions
of wildcards and sequence lengths. For the human genome
(3.1 gigabases, including 237 million wildcard
characters) our representation requires only $ 2 + 8
\cdot 10^{-6} $ bits per character.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Su:2012:INP,
author = "Chien-Hao Su and Tse-Yi Wang and Ming-Tsung Hsu and
Francis Cheng-Hsuan Weng and Cheng-Yan Kao and Daryi
Wang and Huai-Kuang Tsai",
title = "The Impact of Normalization and Phylogenetic
Information on Estimating the Distance for
Metagenomes",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "619--628",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.111",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Metagenomics enables the study of unculturable
microorganisms in different environments directly.
Discriminating between the compositional differences of
metagenomes is an important and challenging problem.
Several distance functions have been proposed to
estimate the differences based on functional profiles
or taxonomic distributions; however, the strengths and
limitations of such functions are still unclear.
Initially, we analyzed three well-known distance
functions and found very little difference between them
in the clustering of samples. This motivated us to
incorporate suitable normalizations and phylogenetic
information into the functions so that we could cluster
samples from both real and synthetic data sets. The
results indicate significant improvement in sample
clustering over that derived by rank-based
normalization with phylogenetic information, regardless
of whether the samples are from real or synthetic
microbiomes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2012:EGS,
author = "Daifeng Wang and Mia K. Markey and Claus O. Wilke and
Ari Arapostathis",
title = "Eigen-Genomic System Dynamic-Pattern Analysis
({ESDA}): Modeling {mRNA} Degradation and
Self-Regulation",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "430--437",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.150",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "High-throughput methods systematically measure the
internal state of the entire cell, but powerful
computational tools are needed to infer dynamics from
their raw data. Therefore, we have developed a new
computational method, Eigen-genomic System
Dynamic-pattern Analysis (ESDA), which uses systems
theory to infer dynamic parameters from a time series
of gene expression measurements. As many genes are
measured at a modest number of time points, estimation
of the system matrix is underdetermined and traditional
approaches for estimating dynamic parameters are
ineffective; thus, ESDA uses the principle of
dimensionality reduction to overcome the data
imbalance. Since degradation rates are naturally
confounded by self-regulation, our model estimates an
effective degradation rate that is the difference
between self-regulation and degradation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2012:NEA,
author = "Biing-Feng Wang and Chung-Chin Kuo and Shang-Ju Liu
and Chien-Hsin Lin",
title = "A New Efficient Algorithm for the Gene-Team Problem on
General Sequences",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "330--344",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.96",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Identifying conserved gene clusters is an important
step toward understanding the evolution of genomes and
predicting the functions of genes. A famous model to
capture the essential biological features of a
conserved gene cluster is called the gene-team model.
The problem of finding the gene teams of two general
sequences is the focus of this paper. For this problem,
He and Goldwasser had an efficient algorithm that
requires $ O(m n) $ time using $ O(m + n) $ working
space, where $m$ and $n$ are, respectively, the numbers
of genes in the two given sequences. In this paper, a
new efficient algorithm is presented. Assume $ m \le n
$.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2012:OSA,
author = "Biing-Feng Wang",
title = "Output-Sensitive Algorithms for Finding the Nested
Common Intervals of Two General Sequences",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "548--559",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.112",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The focus of this paper is the problem of finding all
nested common intervals of two general sequences.
Depending on the treatment one wants to apply to
duplicate genes, Blin et al. introduced three models to
define nested common intervals of two sequences: the
uniqueness, the free-inclusion, and the bijection
models. We consider all the three models. For the
uniqueness and the bijection models, we give $ O(n +
N_{\rm out}) $-time algorithms, where $ N_{\rm out} $
denotes the size of the output. For the free-inclusion
model, we give an $ O(n^{1 + \varepsilon } + N_{{\rm
out}}) $-time algorithm, where $ \varepsilon > 0 $ is
an arbitrarily small constant.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2012:RCM,
author = "Shu-Lin Wang and Yi-Hai Zhu and Wei Jia and De-Shuang
Huang",
title = "Robust Classification Method of Tumor Subtype by Using
Correlation Filters",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "580--591",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.135",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Tumor classification based on Gene Expression Profiles
(GEPs), which is of great benefit to the accurate
diagnosis and personalized treatment for different
types of tumor, has drawn a great attention in recent
years. This paper proposes a novel tumor classification
method based on correlation filters to identify the
overall pattern of tumor subtype hidden in
differentially expressed genes. Concretely, two
correlation filters, i.e., Minimum Average Correlation
Energy (MACE) and Optimal Tradeoff Synthetic
Discriminant Function (OTSDF), are introduced to
determine whether a test sample matches the templates
synthesized for each subclass. The experiments on six
publicly available data sets indicate that the proposed
method is robust to noise, and can more effectively
avoid the effects of dimensionality curse.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yoon:2012:SLP,
author = "Yongwook Yoon and Gary Geunbae Lee",
title = "Subcellular Localization Prediction through Boosting
Association Rules",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "609--618",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.131",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Computational methods for predicting protein
subcellular localization have used various types of
features, including N-terminal sorting signals, amino
acid compositions, and text annotations from protein
databases. Our approach does not use biological
knowledge such as the sorting signals or homologues,
but use just protein sequence information. The method
divides a protein sequence into short k-mer sequence
fragments which can be mapped to word features in
document classification. A large number of class
association rules are mined from the protein sequence
examples that range from the N-terminus to the
C-terminus. Then, a boosting algorithm is applied to
those rules to build up a final classifier.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zeng:2012:HES,
author = "Nianyin Zeng and Zidong Wang and Yurong Li and Min Du
and Xiaohui Liu",
title = "A Hybrid {EKF} and Switching {PSO} Algorithm for Joint
State and Parameter Estimation of Lateral Flow
Immunoassay Models",
journal = j-TCBB,
volume = "9",
number = "2",
pages = "321--329",
month = mar,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.140",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 26 16:30:44 2012",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, a hybrid extended Kalman filter (EKF)
and switching particle swarm optimization (SPSO)
algorithm is proposed for jointly estimating both the
parameters and states of the lateral flow immunoassay
model through available short time-series measurement.
Our proposed method generalizes the well-known EKF
algorithm by imposing physical constraints on the
system states. Note that the state constraints are
encountered very often in practice that give rise to
considerable difficulties in system analysis and
design. The main purpose of this paper is to handle the
dynamic modeling problem with state constraints by
combining the extended Kalman filtering and constrained
optimization algorithms via the maximization
probability method.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mazza:2012:HPC,
author = "Tommaso Mazza",
title = "High Performance Computational Systems Biology",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "641--642",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.42",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Burkitt:2012:CCB,
author = "Mark Burkitt and Dawn Walker and Daniella M. Romano
and Alireza Fazeli",
title = "Constructing Complex {$3$D} Biological Environments
from Medical Imaging Using High Performance Computing",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "643--654",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.69",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Extracting information about the structure of
biological tissue from static image data is a complex
task requiring computationally intensive operations.
Here, we present how multicore CPUs and GPUs have been
utilized to extract information about the shape, size,
and path followed by the mammalian oviduct, called the
fallopian tube in humans, from histology images, to
create a unique but realistic 3D virtual organ.
Histology images were processed to identify the
individual cross sections and determine the 3D path
that the tube follows through the tissue. This
information was then related back to the histology
images, linking the 2D cross sections with their
corresponding 3D position along the oviduct. A series
of linear 2D spline cross sections, which were
computationally generated for the length of the
oviduct, were bound to the 3D path of the tube using a
novel particle system technique that provides smooth
resolution of self-intersections. This results in a
unique 3D model of the oviduct, which is grounded in
reality. The GPU is used for the processor intensive
operations of image processing and particle physics
based simulations, significantly reducing the time
required to generate a complete model.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dematte:2012:SGP,
author = "Lorenzo Dematte",
title = "{Smoldyn} on Graphics Processing Units: Massively
Parallel {Brownian} Dynamics Simulations",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "655--667",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.106",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Space is a very important aspect in the simulation of
biochemical systems; recently, the need for simulation
algorithms able to cope with space is becoming more and
more compelling. Complex and detailed models of
biochemical systems need to deal with the movement of
single molecules and particles, taking into
consideration localized fluctuations, transportation
phenomena, and diffusion. A common drawback of spatial
models lies in their complexity: models can become very
large, and their simulation could be time consuming,
especially if we want to capture the systems behavior
in a reliable way using stochastic methods in
conjunction with a high spatial resolution. In order to
deliver the promise done by systems biology to be able
to understand a system as whole, we need to scale up
the size of models we are able to simulate, moving from
sequential to parallel simulation algorithms. In this
paper, we analyze Smoldyn, a widely diffused algorithm
for stochastic simulation of chemical reactions with
spatial resolution and single molecule detail, and we
propose an alternative, innovative implementation that
exploits the parallelism of Graphics Processing Units
(GPUs). The implementation executes the most
computational demanding steps (computation of
diffusion, unimolecular, and bimolecular reaction, as
well as the most common cases of molecule-surface
interaction) on the GPU, computing them in parallel on
each molecule of the system. The implementation offers
good speed-ups and real time, high quality graphics
output.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Belcastro:2012:REA,
author = "Vincenzo Belcastro and Francesco Gregoretti and Velia
Siciliano and Michele Santoro and Giovanni D'Angelo and
Gennaro Oliva and Diego di Bernardo",
title = "Reverse Engineering and Analysis of Genome-Wide Gene
Regulatory Networks from Gene Expression Profiles Using
High-Performance Computing",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "668--678",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.60",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Regulation of gene expression is a carefully regulated
phenomenon in the cell. ``Reverse-engineering''
algorithms try to reconstruct the regulatory
interactions among genes from genome-scale measurements
of gene expression profiles (microarrays). Mammalian
cells express tens of thousands of genes; hence,
hundreds of gene expression profiles are necessary in
order to have acceptable statistical evidence of
interactions between genes. As the number of profiles
to be analyzed increases, so do computational costs and
memory requirements. In this work, we designed and
developed a parallel computing algorithm to
reverse-engineer genome-scale gene regulatory networks
from thousands of gene expression profiles. The
algorithm is based on computing pairwise Mutual
Information between each gene-pair. We successfully
tested it to reverse engineer the Mus Musculus (mouse)
gene regulatory network in liver from gene expression
profiles collected from a public repository. A parallel
hierarchical clustering algorithm was implemented to
discover ``communities'' within the gene network.
Network communities are enriched for genes involved in
the same biological functions. The inferred network was
used to identify two mitochondrial proteins.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bustamam:2012:FPM,
author = "Alhadi Bustamam and Kevin Burrage and Nicholas A.
Hamilton",
title = "Fast Parallel {Markov} Clustering in Bioinformatics
Using Massively Parallel Computing on {GPU} with {CUDA}
and {ELLPACK-R} Sparse Format",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "679--692",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.68",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Markov clustering (MCL) is becoming a key algorithm
within bioinformatics for determining clusters in
networks. However, with increasing vast amount of data
on biological networks, performance and scalability
issues are becoming a critical limiting factor in
applications. Meanwhile, GPU computing, which uses CUDA
tool for implementing a massively parallel computing
environment in the GPU card, is becoming a very
powerful, efficient, and low-cost option to achieve
substantial performance gains over CPU approaches. The
use of on-chip memory on the GPU is efficiently
lowering the latency time, thus, circumventing a major
issue in other parallel computing environments, such as
MPI. We introduce a very fast Markov clustering
algorithm using CUDA (CUDA-MCL) to perform parallel
sparse matrix-matrix computations and parallel sparse
Markov matrix normalizations, which are at the heart of
MCL. We utilized ELLPACK-R sparse format to allow the
effective and fine-grain massively parallel processing
to cope with the sparse nature of interaction networks
data sets in bioinformatics applications. As the
results show, CUDA-MCL is significantly faster than the
original MCL running on CPU. Thus, large-scale parallel
computation on off-the-shelf desktop-machines, that
were previously only possible on supercomputing
architectures, can significantly change the way
bioinformaticians and biologists deal with their
data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Barnat:2012:PSP,
author = "Jiri Barnat and Lubos Brim and Adam Krejci and Adam
Streck and David Safranek and Martin Vejnar and Tomas
Vejpustek",
title = "On Parameter Synthesis by Parallel Model Checking",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "693--705",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.110",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "An important problem in current computational systems
biology is to analyze models of biological systems
dynamics under parameter uncertainty. This paper
presents a novel algorithm for parameter synthesis
based on parallel model checking. The algorithm is
conceptually universal with respect to the modeling
approach employed. We introduce the algorithm, show its
scalability, and examine its applicability on several
biological models.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Stegmayer:2012:BIV,
author = "Georgina Stegmayer and Diego H. Milone and Laura
Kamenetzky and Mariana G. Lopez and Fernando Carrari",
title = "A Biologically Inspired Validity Measure for
Comparison of Clustering Methods over Metabolic Data
Sets",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "706--716",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.10",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In the biological domain, clustering is based on the
assumption that genes or metabolites involved in a
common biological process are coexpressed/coaccumulated
under the control of the same regulatory network. Thus,
a detailed inspection of the grouped patterns to verify
their memberships to well-known metabolic pathways
could be very useful for the evaluation of clusters
from a biological perspective. The aim of this work is
to propose a novel approach for the comparison of
clustering methods over metabolic data sets, including
prior biological knowledge about the relation among
elements that constitute the clusters. A way of
measuring the biological significance of clustering
solutions is proposed. This is addressed from the
perspective of the usefulness of the clusters to
identify those patterns that change in coordination and
belong to common pathways of metabolic regulation. The
measure summarizes in a compact way the objective
analysis of clustering methods, which respects
coherence and clusters distribution. It also evaluates
the biological internal connections of such clusters
considering common pathways. The proposed measure was
tested in two biological databases using three
clustering methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pizzuti:2012:CAM,
author = "Clara Pizzuti and Simona E. Rombo",
title = "A Coclustering Approach for Mining Large
Protein-Protein Interaction Networks",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "717--730",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.158",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Several approaches have been presented in the
literature to cluster Protein-Protein Interaction (PPI)
networks. They can be grouped in two main categories:
those allowing a protein to participate in different
clusters and those generating only nonoverlapping
clusters. In both cases, a challenging task is to find
a suitable compromise between the biological relevance
of the results and a comprehensive coverage of the
analyzed networks. Indeed, methods returning high
accurate results are often able to cover only small
parts of the input PPI network, especially when
low-characterized networks are considered. We present a
coclustering-based technique able to generate both
overlapping and nonoverlapping clusters. The density of
the clusters to search for can also be set by the user.
We tested our method on the two networks of yeast and
human, and compared it to other five well-known
techniques on the same interaction data sets. The
results showed that, for all the examples considered,
our approach always reaches a good compromise between
accuracy and network coverage. Furthermore, the
behavior of our algorithm is not influenced by the
structure of the input network, different from all the
techniques considered in the comparison, which returned
very good results on the yeast network, while on the
human network their outcomes are rather poor.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kountouris:2012:CSF,
author = "Petros Kountouris and Michalis Agathocleous and
Vasilis J. Promponas and Georgia Christodoulou and
Simos Hadjicostas and Vassilis Vassiliades and Chris
Christodoulou",
title = "A Comparative Study on Filtering Protein Secondary
Structure Prediction",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "731--739",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.22",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Filtering of Protein Secondary Structure Prediction
(PSSP) aims to provide physicochemically realistic
results, while it usually improves the predictive
performance. We performed a comparative study on this
challenging problem, utilizing both machine learning
techniques and empirical rules and we found that
combinations of the two lead to the highest
improvement.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2012:FIF,
author = "Xiao-Fei Zhang and Dao-Qing Dai",
title = "A Framework for Incorporating Functional
Interrelationships into Protein Function Prediction
Algorithms",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "740--753",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.148",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The functional annotation of proteins is one of the
most important tasks in the post-genomic era. Although
many computational approaches have been developed in
recent years to predict protein function, most of these
traditional algorithms do not take interrelationships
among functional terms into account, such as different
GO terms usually coannotate with some common proteins.
In this study, we propose a new functional similarity
measure in the form of Jaccard coefficient to quantify
these interrelationships and also develop a framework
for incorporating GO term similarity into protein
function prediction process. The experimental results
of cross-validation on \bioname{S. cerevisiae} and {\em
Homo sapiens} data sets demonstrate that our method is
able to improve the performance of protein function
prediction. In addition, we find that small size terms
associated with a few of proteins obtain more benefit
than the large size ones when considering functional
interrelationships. We also compare our similarity
measure with other two widely used measures, and
results indicate that when incorporated into function
prediction algorithms, our proposed measure is more
effective. Experiment results also illustrate that our
algorithms outperform two previous competing
algorithms, which also take functional
interrelationships into account, in prediction
accuracy. Finally, we show that our method is robust to
annotations in the database which are not complete at
present. These results give new insights about the
importance of functional interrelationships in protein
function prediction.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sharma:2012:TRF,
author = "Alok Sharma and Seiya Imoto and Satoru Miyano",
title = "A Top-r Feature Selection Algorithm for Microarray
Gene Expression Data",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "754--764",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.151",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Most of the conventional feature selection algorithms
have a drawback whereby a weakly ranked gene that could
perform well in terms of classification accuracy with
an appropriate subset of genes will be left out of the
selection. Considering this shortcoming, we propose a
feature selection algorithm in gene expression data
analysis of sample classifications. The proposed
algorithm first divides genes into subsets, the sizes
of which are relatively small (roughly of size $h$ ),
then selects informative smaller subsets of genes (of
size $ r < h $ ) from a subset and merges the chosen
genes with another gene subset (of size $r$ ) to update
the gene subset. We repeat this process until all
subsets are merged into one informative subset. We
illustrate the effectiveness of the proposed algorithm
by analyzing three distinct gene expression data sets.
Our method shows promising classification accuracy for
all the test data sets. We also show the relevance of
the selected genes in terms of their biological
functions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2012:CPS,
author = "Shuai Cheng Li and Dongbo Bu and Ming Li",
title = "Clustering 100,000 Protein Structure Decoys in
Minutes",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "765--773",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.142",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Ab initio protein structure prediction methods first
generate large sets of structural conformations as
candidates (called decoys), and then select the most
representative decoys through clustering techniques.
Classical clustering methods are inefficient due to the
pairwise distance calculation, and thus become
infeasible when the number of decoys is large. In
addition, the existing clustering approaches suffer
from the arbitrariness in determining a distance
threshold for proteins within a cluster: a small
distance threshold leads to many small clusters, while
a large distance threshold results in the merging of
several independent clusters into one cluster. In this
paper, we propose an efficient clustering method
through fast estimating cluster centroids and efficient
pruning rotation spaces. The number of clusters is
automatically detected by information distance
criteria. A package named ONION, which can be
downloaded freely, is implemented accordingly.
Experimental results on benchmark data sets suggest
that ONION is 14 times faster than existing tools, and
ONION obtains better selections for 31 targets, and
worse selection for 19 targets compared to SPICKER's
selections. On an average PC, ONION can cluster 100,000
decoys in around 12 minutes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sun:2012:DFF,
author = "Yanni Sun and Jeremy Buhler and Cheng Yuan",
title = "Designing Filters for Fast-Known {NcRNA}
Identification",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "774--787",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.149",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Detecting members of known noncoding RNA (ncRNA)
families in genomic DNA is an important part of
sequence annotation. However, the most widely used tool
for modeling ncRNA families, the covariance model (CM),
incurs a high-computational cost when used for
genome-wide search. This cost can be reduced by using a
filter to exclude sequences that are unlikely to
contain the ncRNA of interest, applying the CM only
where it is likely to match strongly. Despite recent
advances, designing an efficient filter that can detect
ncRNA instances lacking strong conservation while
excluding most irrelevant sequences remains
challenging. In this work, we design three types of
filters based on multiple secondary structure profiles
(SSPs). An SSP augments a regular profile (i.e., a
position weight matrix) with secondary structure
information but can still be efficiently scanned
against long sequences. Multi-SSP-based filters combine
evidence from multiple SSP matches and can achieve high
sensitivity and specificity. Our SSP-based filters are
extensively tested in BRAliBase III data set, Rfam 9.0,
and a published soil metagenomic data set. In addition,
we compare the SSP-based filters with several other
ncRNA search tools including Infernal (with profile
HMMs as filters), ERPIN, and tRNAscan-SE. Our
experiments demonstrate that carefully designed SSP
filters can achieve significant speedup over unfiltered
CM search while maintaining high sensitivity for
various ncRNA families. The designed filters and
filter-scanning programs are available at our website:
www.cse.msu.edu/~yannisun/ssp/",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Krejnik:2012:EEA,
author = "Milos Krejnik and Jiri Klema",
title = "Empirical Evidence of the Applicability of Functional
Clustering through Gene Expression Classification",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "788--798",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.23",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The availability of a great range of prior biological
knowledge about the roles and functions of genes and
gene-gene interactions allows us to simplify the
analysis of gene expression data to make it more
robust, compact, and interpretable. Here, we
objectively analyze the applicability of functional
clustering for the identification of groups of
functionally related genes. The analysis is performed
in terms of gene expression classification and uses
predictive accuracy as an unbiased performance measure.
Features of biological samples that originally
corresponded to genes are replaced by features that
correspond to the centroids of the gene clusters and
are then used for classifier learning. Using 10
benchmark data sets, we demonstrate that functional
clustering significantly outperforms random clustering
without biological relevance. We also show that
functional clustering performs comparably to gene
expression clustering, which groups genes according to
the similarity of their expression profiles. Finally,
the suitability of functional clustering as a feature
extraction technique is evaluated and discussed.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Armano:2012:EII,
author = "Giuliano Armano and Filippo Ledda",
title = "Exploiting Intrastructure Information for Secondary
Structure Prediction with Multifaceted Pipelines",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "799--808",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.159",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Predicting the secondary structure of proteins is
still a typical step in several bioinformatic tasks, in
particular, for tertiary structure prediction.
Notwithstanding the impressive results obtained so far,
mostly due to the advent of sequence encoding schemes
based on multiple alignment, in our view the problem
should be studied from a novel perspective, in which
understanding how available information sources are
dealt with plays a central role. After revisiting a
well-known secondary structure predictor viewed from
this perspective (with the goal of identifying which
sources of information have been considered and which
have not), we propose a generic software architecture
designed to account for all relevant information
sources. To demonstrate the validity of the approach, a
predictor compliant with the proposed generic
architecture has been implemented and compared with
several state-of-the-art secondary structure
predictors. Experiments have been carried out on
standard data sets, and the corresponding results
confirm the validity of the approach. The predictor is
available at \path=http://iasc.diee.unica.it/ssp2/=
through the corresponding web application or as
downloadable stand-alone portable unpack-and-run
bundle.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Serang:2012:FMS,
author = "Oliver Serang and William Stratford Noble",
title = "Faster Mass Spectrometry-Based Protein Inference:
Junction Trees Are More Efficient than Sampling and
Marginalization by Enumeration",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "809--817",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.26",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The problem of identifying the proteins in a complex
mixture using tandem mass spectrometry can be framed as
an inference problem on a graph that connects peptides
to proteins. Several existing protein identification
methods make use of statistical inference methods for
graphical models, including expectation maximization,
Markov chain Monte Carlo, and full marginalization
coupled with approximation heuristics. We show that,
for this problem, the majority of the cost of inference
usually comes from a few highly connected subgraphs.
Furthermore, we evaluate three different statistical
inference methods using a common graphical model, and
we demonstrate that junction tree inference
substantially improves rates of convergence compared to
existing methods. The python code used for this paper
is available at
\path=http://noble.gs.washington.edu/proj/fido=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huang:2012:GCU,
author = "Hong Huang and Hailiang Feng",
title = "Gene Classification Using Parameter-Free
Semi-Supervised Manifold Learning",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "818--827",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.152",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A new manifold learning method, called parameter-free
semi-supervised local Fisher discriminant analysis
(pSELF), is proposed to map the gene expression data
into a low-dimensional space for tumor classification.
Motivated by the fact that semi-supervised and
parameter-free are two desirable and promising
characteristics for dimension reduction, a new
difference-based optimization objective function with
unlabeled samples has been designed. The proposed
method preserves the global structure of unlabeled
samples in addition to separating labeled samples in
different classes from each other. The semi-supervised
method has an analytic form of the globally optimal
solution, which can be computed efficiently by eigen
decomposition. Experimental results on synthetic data
and SRBCT, DLBCL, and Brain Tumor gene expression data
sets demonstrate the effectiveness of the proposed
method.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jarvis:2012:MIP,
author = "Peter Jarvis and Jeremy Sumner",
title = "{Markov} Invariants for Phylogenetic Rate Matrices
Derived from Embedded Submodels",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "828--836",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.24",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We consider novel phylogenetic models with rate
matrices that arise via the embedding of a progenitor
model on a small number of character states, into a
target model on a larger number of character states.
Adapting representation-theoretic results from recent
investigations of Markov invariants for the general
rate matrix model, we give a prescription for
identifying and counting Markov invariants for such
``symmetric embedded'' models, and we provide
enumerations of these for the first few cases with a
small number of character states. The simplest example
is a target model on three states, constructed from a
general 2 state model; the ``$ 2 \hookrightarrow 3 $''
embedding. We show that for 2 taxa, there exist two
invariants of quadratic degree that can be used to
directly infer pairwise distances from observed
sequences under this model. A simple simulation study
verifies their theoretical expected values, and
suggests that, given the appropriateness of the model
class, they have superior statistical properties than
the standard (log) Det invariant (which is of cubic
degree for this case).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yang:2012:MPD,
author = "Cheng-Hong Yang and Yu-Huei Cheng and Cheng-Huei Yang
and Li-Yeh Chuang",
title = "Mutagenic Primer Design for Mismatch {PCR-RFLP} {SNP}
Genotyping Using a Genetic Algorithm",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "837--845",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.25",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Polymerase chain reaction-restriction fragment length
polymorphism (PCR-RFLP) is useful in small-scale basic
research studies of complex genetic diseases that are
associated with single nucleotide polymorphism (SNP).
Designing a feasible primer pair is an important work
before performing PCR-RFLP for SNP genotyping. However,
in many cases, restriction enzymes to discriminate the
target SNP resulting in the primer design is not
applicable. A mutagenic primer is introduced to solve
this problem. GA-based Mismatch PCR-RFLP Primers Design
(GAMPD) provides a method that uses a genetic algorithm
to search for optimal mutagenic primers and available
restriction enzymes from REBASE. In order to improve
the efficiency of the proposed method, a mutagenic
matrix is employed to judge whether a hypothetical
mutagenic primer can discriminate the target SNP by
digestion with available restriction enzymes. The
available restriction enzymes for the target SNP are
mined by the updated core of SNP-RFLPing. GAMPD has
been used to simulate the SNPs in the human SLC6A4 gene
under different parameter settings and compared with
SNP Cutter for mismatch PCR-RFLP primer design. The in
silico simulation of the proposed GAMPD program showed
that it designs mismatch PCR-RFLP primers. The GAMPD
program is implemented in JAVA and is freely available
at \path=http://bio.kuas.edu.tw/gampd/=",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Norkus:2012:AAL,
author = "Mindaugas Norkus and Damien Fay and Mary J. Murphy and
Frank Barry and Gearoid OLaighin and Liam Kilmartin",
title = "On the Application of Active Learning and {Gaussian}
Processes in Postcryopreservation Cell Membrane
Integrity Experiments",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "846--856",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.155",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Biological cell cryopreservation permits storage of
specimens for future use. Stem cell cryostorage in
particular is fast becoming a broadly spread practice
due to their potential for use in regenerative
medicine. For the optimal cryopreservation process,
ultralow temperatures are needed. However, elevated
temperatures are often unavoidable in a typical sample
handling cycle which in turn negatively affects the
postcryopreservation integrity of cells. In this paper,
we present an application of active learning using an
underlying Gaussian Process (GP) model in an
experimental study on postcryopreservation membrane
integrity response to a range of elevated temperature
conditions. We tailored this technique for the current
investigation and developed an algorithm which enabled
identification of the sampling locations for the
experiments in order to obtain the highest information
return about the process from a limited size sample
set. We applied this algorithm in the experimental
study investigating the effects of severe temperature
elevation (ranging from $ - 40 $ to $ 20^{\circ } ${\rm
C}) over a short term event (48 hours) on the
postcryopreservation membrane integrity of Mesenchymal
Stem Cells (MSCs) derived from human bone marrow. The
algorithm showed excellent performance by selecting the
locations which maximized the reduction of variance of
the process response estimate. An approximating GP
model developed from this experimental data shows that
the elevated temperatures during cryopreservation have
an imminent detrimental effect on cell integrity.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2012:PCD,
author = "Xiao-Fei Zhang and Dao-Qing Dai and Xiao-Xin Li",
title = "Protein Complexes Discovery Based on Protein-Protein
Interaction Data via a Regularized Sparse Generative
Network Model",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "857--870",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.20",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Detecting protein complexes from protein interaction
networks is one major task in the postgenome era.
Previous developed computational algorithms identifying
complexes mainly focus on graph partition or dense
region finding. Most of these traditional algorithms
cannot discover overlapping complexes which really
exist in the protein-protein interaction (PPI)
networks. Even if some density-based methods have been
developed to identify overlapping complexes, they are
not able to discover complexes that include peripheral
proteins. In this study, motivated by recent successful
application of generative network model to describe the
generation process of PPI networks and to detect
communities from social networks, we develop a
regularized sparse generative network model (RSGNM), by
adding another process that generates propensities
using exponential distribution and incorporating
Laplacian regularizer into an existing generative
network model, for protein complexes identification. By
assuming that the propensities are generated using
exponential distribution, the estimators of
propensities will be sparse, which not only has good
biological interpretation but also helps to control the
overlapping rate among detected complexes. And the
Laplacian regularizer will lead to the estimators of
propensities more smooth on interaction networks.
Experimental results on three yeast PPI networks show
that RSGNM outperforms six previous competing
algorithms in terms of the quality of detected
complexes. In addition, RSGNM is able to detect
overlapping complexes and complexes including
peripheral proteins simultaneously. These results give
new insights about the importance of generative network
models in protein complexes identification.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2012:QDS,
author = "Yong Zhang and Peng Li and Garng Huang",
title = "Quantifying Dynamic Stability of Genetic Memory
Circuits",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "871--884",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.132",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Bistability/Multistability has been found in many
biological systems including genetic memory circuits.
Proper characterization of system stability helps to
understand biological functions and has potential
applications in fields such as synthetic biology.
Existing methods of analyzing bistability are either
qualitative or in a static way. Assuming the circuit is
in a steady state, the latter can only reveal the
susceptibility of the stability to injected DC noises.
However, this can be inappropriate and inadequate as
dynamics are crucial for many biological networks. In
this paper, we quantitatively characterize the dynamic
stability of a genetic conditional memory circuit by
developing new dynamic noise margin (DNM) concepts and
associated algorithms based on system theory. Taking
into account the duration of the noisy perturbation,
the DNMs are more general cases of their static
counterparts. Using our techniques, we analyze the
noise immunity of the memory circuit and derive
insights on dynamic hold and write operations.
Considering cell-to-cell variations, our parametric
analysis reveals that the dynamic stability of the
memory circuit has significantly varying sensitivities
to underlying biochemical reactions attributable to
differences in structure, time scales, and nonlinear
interactions between reactions. With proper extensions,
our techniques are broadly applicable to other
multistable biological systems.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Czeizler:2012:QAS,
author = "Eugen Czeizler and Andrzej Mizera and Elena Czeizler
and Ralph-Johan Back and John E. Eriksson and Ion
Petre",
title = "Quantitative Analysis of the Self-Assembly Strategies
of Intermediate Filaments from Tetrameric {Vimentin}",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "885--898",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.154",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In vitro assembly of intermediate filaments from
tetrameric vimentin consists of a very rapid phase of
tetramers laterally associating into unit-length
filaments and a slow phase of filament elongation. We
focus in this paper on a systematic quantitative
investigation of two molecular models for filament
assembly, recently proposed in (Kirmse et al. J. Biol.
Chem. 282, 52 (2007), 18563-18572), through
mathematical modeling, model fitting, and model
validation. We analyze the quantitative contribution of
each filament elongation strategy: with tetramers, with
unit-length filaments, with longer filaments, or
combinations thereof. In each case, we discuss the
numerical fitting of the model with respect to one set
of data, and its separate validation with respect to a
second, different set of data. We introduce a
high-resolution model for vimentin filament
self-assembly, able to capture the detailed dynamics of
filaments of arbitrary length. This provides much more
predictive power for the model, in comparison to
previous models where only the mean length of all
filaments in the solution could be analyzed. We show
how kinetic observations on low-resolution models can
be extrapolated to the high-resolution model and used
for lowering its complexity.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mooney:2012:GGU,
author = "Michael Mooney and Beth Wilmot and The Bipolar Genome
Study and Shannon McWeeney",
title = "The {GA} and the {GWAS}: Using Genetic Algorithms to
Search for Multilocus Associations",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "899--910",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.145",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Enormous data collection efforts and improvements in
technology have made large genome-wide association
studies a promising approach for better understanding
the genetics of common diseases. Still, the knowledge
gained from these studies may be extended even further
by testing the hypothesis that genetic susceptibility
is due to the combined effect of multiple variants or
interactions between variants. Here, we explore and
evaluate the use of a genetic algorithm to discover
groups of SNPs (of size 2, 3, or 4) that are jointly
associated with bipolar disorder. The algorithm is
guided by the structure of a gene interaction network,
and is able to find groups of SNPs that are strongly
associated with the disease, while performing far fewer
statistical tests than other methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mazza:2012:RTP,
author = "Tommaso Mazza and Paolo Ballarini and Rosita Guido and
Davide Prandi",
title = "The Relevance of Topology in Parallel Simulation of
Biological Networks",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "911--923",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.27",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Important achievements in traditional biology have
deepened the knowledge about living systems leading to
an extensive identification of parts-list of the cell
as well as of the interactions among biochemical
species responsible for cell's regulation. Such an
expanding knowledge also introduces new issues. For
example, the increasing comprehension of the
interdependencies between pathways (pathways
cross-talk) has resulted, on one hand, in the growth of
informational complexity, on the other, in a strong
lack of information coherence. The overall grand
challenge remains unchanged: to be able to assemble the
knowledge of every ``piece'' of a system in order to
figure out the behavior of the whole (integrative
approach). In light of these considerations, high
performance computing plays a fundamental role in the
context of in-silico biology. Stochastic simulation is
a renowned analysis tool, which, although widely used,
is subject to stringent computational requirements, in
particular when dealing with heterogeneous and high
dimensional systems. Here, we introduce and discuss a
methodology aimed at alleviating the burden of
simulating complex biological networks. Such a method,
which springs from graph theory, is based on the
principle of fragmenting the computational space of a
simulation trace and delegating the computation of
fragments to a number of parallel processes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sengupta:2012:WMC,
author = "Debarka Sengupta and Ujjwal Maulik and Sanghamitra
Bandyopadhyay",
title = "Weighted {Markov} Chain Based Aggregation of
Biomolecule Orderings",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "924--933",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.28",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The scope and effectiveness of Rank Aggregation (RA)
have already been established in contemporary
bioinformatics research. Rank aggregation helps in
meta-analysis of putative results collected from
different analytic or experimental sources. For
example, we often receive considerably differing ranked
lists of genes or microRNAs from various target
prediction algorithms or microarray studies. Sometimes
combining them all, in some sense, yields more
effective ordering of the set of objects. Also,
assigning a certain level of confidence to each source
of ranking is a natural demand of aggregation.
Assignment of weights to the sources of orderings can
be performed by experts. Several rank aggregation
approaches like those based on Markov Chains (MCs),
evolutionary algorithms, etc., exist in the literature.
Markov chains, in general, are faster than the
evolutionary approaches. Unlike the evolutionary
computing approaches Markov chains have not been used
for weighted aggregation scenarios. This is because of
the absence of a formal framework of Weighted Markov
Chain (WMC). In this paper, we propose the use of a
modified version of MC4 (one of the Markov chains
proposed by Dwork et al., 2001), followed by the
weighted analog of local Kemenization for performing
rank aggregation, where the sources of rankings can be
prioritized by an expert. Effectiveness of the weighted
Markov chain approach over the very recently proposed
Genetic Algorithm (GA) and Cross-Entropy Monte Carlo
(MC) algorithm-based techniques, has been established
for gene orderings from microarray analysis and
orderings of predicted microRNA targets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zoppis:2012:MIO,
author = "Italo Zoppis and Erica Gianazza and Massimiliano
Borsani and Clizia Chinello and Veronica Mainini and
Carmen Galbusera and Carlo Ferrarese and Gloria
Galimberti and Sandro Sorbi and Barbara Borroni and
Fulvio Magni and Marco Antoniotti and Giancarlo Mauri",
title = "Mutual Information Optimization for Mass Spectra Data
Alignment",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "934--939",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.80",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "``Signal'' alignments play critical roles in many
clinical setting. This is the case of mass spectrometry
(MS) data, an important component of many types of
proteomic analysis. A central problem occurs when one
needs to integrate (MS) data produced by different
sources, e.g., different equipment and/or laboratories.
In these cases, some form of ``data integration'' or
``data fusion'' may be necessary in order to discard
some source-specific aspects and improve the ability to
perform a classification task such as inferring the
``disease classes'' of patients. The need for new
high-performance data alignments methods is therefore
particularly important in these contexts. In this
paper, we propose an approach based both on an
information theory perspective, generally used in a
feature construction problem, and the application of a
mathematical programming task (i.e., the weighted
bipartite matching problem). We present the results of
a competitive analysis of our method against other
approaches. The analysis was conducted on data from
plasma/ethylenediaminetetraacetic acid of ``control''
and Alzheimer patients collected from three different
hospitals. The results point to a significant
performance advantage of our method with respect to the
competing ones tested.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fagundes-Lima:2012:CSS,
author = "Denise Fagundes-Lima and Gerald Weber",
title = "Comment on {``SCS: Signal, Context, and Structure
Features for Genome-Wide Human Promoter
Recognition''}",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "940--941",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.130",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We comment on the flexibility profiles calculated by
Zeng et al., and show that these profiles do not
represent the local flexibility of the DNA molecule. If
one takes into account the physics of elasticity, the
averaged flexibility profile show an additional peak
which is missed in the original calculation. We show
that it is not possible to calculate the flexibility of
a 6-mer using tetranucleotide elastic constants, the
shortest sequence is a 7-mer. For 6-mers, dinucleotide
or trinucleotide parameters are needed. We present
calculations for dinucleotide flexibility parameters
and show that the same additional peak is present for
both 7--mers and 6-mers.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2012:IAT,
author = "Anonymous",
title = "{IEEE\slash ACM Transactions on Computational Biology
and Bioinformatics} Seeks New {Editor in Chief} for
2013--2014 Terms",
journal = j-TCBB,
volume = "9",
number = "3",
pages = "942--942",
month = may,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.43",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Apr 19 17:58:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2012:GEB,
author = "Luonan Chen and Michael K. Ng",
title = "Guest Editorial: Bioinformatics and Computational
Systems Biology",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "945--946",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.76",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yuan:2012:SRN,
author = "Yinyin Yuan and Christina Curtis and Carlos Caldas and
Florian Markowetz",
title = "A Sparse Regulatory Network of Copy-Number Driven Gene
Expression Reveals Putative Breast Cancer Oncogenes",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "947--954",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.105",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Copy number aberrations are recognized to be important
in cancer as they may localize to regions harboring
oncogenes or tumor suppressors. Such genomic
alterations mediate phenotypic changes through their
impact on expression. Both cis- and trans-acting
alterations are important since they may help to
elucidate putative cancer genes. However, amidst
numerous passenger genes, trans-effects are less well
studied due to the computational difficulty in
detecting weak and sparse signals in the data, and yet
may influence multiple genes on a global scale. We
propose an integrative approach to learn a sparse
interaction network of DNA copy-number regions with
their downstream transcriptional targets in breast
cancer. With respect to goodness of fit on both
simulated and real data, the performance of sparse
network inference is no worse than other
state-of-the-art models but with the advantage of
simultaneous feature selection and efficiency. The
DNA-RNA interaction network helps to distinguish
copy-number driven expression alterations from those
that are copy-number independent. Further, our approach
yields a quantitative copy-number dependency score,
which distinguishes cis- versus trans-effects. When
applied to a breast cancer data set, numerous
expression profiles were impacted by cis-acting
copy-number alterations, including several known
oncogenes such as GRB7, ERBB2, and LSM1. Several
trans-acting alterations were also identified,
impacting genes such as ADAM2 and BAGE, which warrant
further investigation. Availability: An R package named
lol is available from
\path=www.markowetzlab.org/software/lol.html=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2012:IBS,
author = "Li-Zhi Liu and Fang-Xiang Wu and W. J. Zhang",
title = "Inference of Biological {S}-System Using the Separable
Estimation Method and the Genetic Algorithm",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "955--965",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.126",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Reconstruction of a biological system from its
experimental time series data is a challenging task in
systems biology. The S-system which consists of a group
of nonlinear ordinary differential equations (ODEs) is
an effective model to characterize molecular biological
systems and analyze the system dynamics. However,
inference of S-systems without the knowledge of system
structure is not a trivial task due to its nonlinearity
and complexity. In this paper, a pruning separable
parameter estimation algorithm (PSPEA) is proposed for
inferring S-systems. This novel algorithm combines the
separable parameter estimation method (SPEM) and a
pruning strategy, which includes adding an $ \ell_1 $
regularization term to the objective function and
pruning the solution with a threshold value. Then, this
algorithm is combined with the continuous genetic
algorithm (CGA) to form a hybrid algorithm that owns
the properties of these two combined algorithms. The
performance of the pruning strategy in the proposed
algorithm is evaluated from two aspects: the parameter
estimation error and structure identification accuracy.
The results show that the proposed algorithm with the
pruning strategy has much lower estimation error and
much higher identification accuracy than the existing
method.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kawano:2012:IGP,
author = "Shuichi Kawano and Teppei Shimamura and Atsushi Niida
and Seiya Imoto and Rui Yamaguchi and Masao Nagasaki
and Ryo Yoshida and Cristin Print and Satoru Miyano",
title = "Identifying Gene Pathways Associated with Cancer
Characteristics via Sparse Statistical Methods",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "966--972",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.48",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We propose a statistical method for uncovering gene
pathways that characterize cancer heterogeneity. To
incorporate knowledge of the pathways into the model,
we define a set of activities of pathways from
microarray gene expression data based on the Sparse
Probabilistic Principal Component Analysis (SPPCA). A
pathway activity logistic regression model is then
formulated for cancer phenotype. To select pathway
activities related to binary cancer phenotypes, we use
the elastic net for the parameter estimation and derive
a model selection criterion for selecting tuning
parameters included in the model estimation. Our
proposed method can also reverse-engineer gene networks
based on the identified multiple pathways that enables
us to discover novel gene-gene associations relating
with the cancer phenotypes. We illustrate the whole
process of the proposed method through the analysis of
breast cancer gene expression data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kim:2012:SGE,
author = "Haseong Kim and Erol Gelenbe",
title = "Stochastic Gene Expression Modeling with {Hill}
Function for Switch-Like Gene Responses",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "973--979",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.153",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gene expression models play a key role to understand
the mechanisms of gene regulation whose aspects are
grade and switch-like responses. Though many stochastic
approaches attempt to explain the gene expression
mechanisms, the Gillespie algorithm which is commonly
used to simulate the stochastic models requires
additional gene cascade to explain the switch-like
behaviors of gene responses. In this study, we propose
a stochastic gene expression model describing the
switch-like behaviors of a gene by employing Hill
functions to the conventional Gillespie algorithm. We
assume eight processes of gene expression and their
biologically appropriate reaction rates are estimated
based on published literatures. We observed that the
state of the system of the toggled switch model is
rarely changed since the Hill function prevents the
activation of involved proteins when their
concentrations stay below a criterion. In ScbA-ScbR
system, which can control the antibiotic metabolite
production of microorganisms, our modified Gillespie
algorithm successfully describes the switch-like
behaviors of gene responses and oscillatory expressions
which are consistent with the published experimental
study.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2012:EFT,
author = "Xin Chen and Xiaohua Hu and Tze Yee Lim and Xiajiong
Shen and E. K. Park and Gail L. Rosen",
title = "Exploiting the Functional and Taxonomic Structure of
Genomic Data by Probabilistic Topic Modeling",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "980--991",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.113",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, we present a method that enable both
homology-based approach and composition-based approach
to further study the functional core (i.e., microbial
core and gene core, correspondingly). In the proposed
method, the identification of major functionality
groups is achieved by generative topic modeling, which
is able to extract useful information from unlabeled
data. We first show that generative topic model can be
used to model the taxon abundance information obtained
by homology-based approach and study the microbial
core. The model considers each sample as a
``document,'' which has a mixture of functional groups,
while each functional group (also known as a ``latent
topic'') is a weight mixture of species. Therefore,
estimating the generative topic model for taxon
abundance data will uncover the distribution over
latent functions (latent topic) in each sample. Second,
we show that, generative topic model can also be used
to study the genome-level composition of ``N-mer''
features (DNA subreads obtained by composition-based
approaches). The model consider each genome as a
mixture of latten genetic patterns (latent topics),
while each functional pattern is a weighted mixture of
the ``N-mer'' features, thus the existence of core
genomes can be indicated by a set of common N-mer
features. After studying the mutual information between
latent topics and gene regions, we provide an
explanation of the functional roles of uncovered latten
genetic patterns. The experimental results demonstrate
the effectiveness of proposed method.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gonzalez:2012:PLB,
author = "Alvaro J. Gonzalez and Li Liao and Cathy H. Wu",
title = "Predicting Ligand Binding Residues and Functional
Sites Using Multipositional Correlations with Graph
Theoretic Clustering and Kernel {CCA}",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "992--1001",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.136",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present a new computational method for predicting
ligand binding residues and functional sites in protein
sequences. These residues and sites tend to be not only
conserved, but also exhibit strong correlation due to
the selection pressure during evolution in order to
maintain the required structure and/or function. To
explore the effect of correlations among multiple
positions in the sequences, the method uses graph
theoretic clustering and kernel-based canonical
correlation analysis (kCCA) to identify binding and
functional sites in protein sequences as the residues
that exhibit strong correlation between the residues'
evolutionary characterization at the sites and the
structure-based functional classification of the
proteins in the context of a functional family. The
results of testing the method on two well-curated data
sets show that the prediction accuracy as measured by
Receiver Operating Characteristic (ROC) scores improves
significantly when multipositional correlations are
accounted for.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2012:GEI,
author = "Jianer Chen and Alexander Zelikovsky",
title = "{Guest Editors}' Introduction to the Special Section
on Bioinformatics Research and Applications",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1002--1003",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.77",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chaudhary:2012:FLS,
author = "Ruchi Chaudhary and J. Gordon Burleigh and David
Fernandez-Baca",
title = "Fast Local Search for Unrooted {Robinson--Foulds}
Supertrees",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1004--1013",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.47",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A Robinson--Foulds (RF) supertree for a collection of
input trees is a tree containing all the species in the
input trees that is at minimum total RF distance to the
input trees. Thus, an RF supertree is consistent with
the maximum number of splits in the input trees.
Constructing RF supertrees for rooted and unrooted data
is NP-hard. Nevertheless, effective local search
heuristics have been developed for the restricted case
where the input trees and the supertree are rooted. We
describe new heuristics, based on the Edge Contract and
Refine (ECR) operation, that remove this restriction,
thereby expanding the utility of RF supertrees. Our
experimental results on simulated and empirical data
sets show that our unrooted local search algorithms
yield better supertrees than those obtained from MRP
and rooted RF heuristics in terms of total RF distance
to the input trees and, for simulated data, in terms of
RF distance to the true tree.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lin:2012:MPT,
author = "Yu Lin and Vaibhav Rajan and Bernard M. E. Moret",
title = "A Metric for Phylogenetic Trees Based on Matching",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1014--1022",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.157",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Comparing two or more phylogenetic trees is a
fundamental task in computational biology. The simplest
outcome of such a comparison is a pairwise measure of
similarity, dissimilarity, or distance. A large number
of such measures have been proposed, but so far all
suffer from problems varying from computational cost to
lack of robustness; many can be shown to behave
unexpectedly under certain plausible inputs. For
instance, the widely used Robinson--Foulds distance is
poorly distributed and thus affords little
discrimination, while also lacking robustness in the
face of very small changes-reattaching a single leaf
elsewhere in a tree of any size can instantly maximize
the distance. In this paper, we introduce a new
pairwise distance measure, based on matching, for
phylogenetic trees. We prove that our measure induces a
metric on the space of trees, show how to compute it in
low polynomial time, verify through statistical testing
that it is robust, and finally note that it does not
exhibit unexpected behavior under the same inputs that
cause problems with other measures. We also illustrate
its usefulness in clustering trees, demonstrating
significant improvements in the quality of hierarchical
clustering as compared to the same collections of trees
clustered using the Robinson--Foulds distance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Swenson:2012:KMA,
author = "Krister M. Swenson and Eric Chen and Nicholas D.
Pattengale and David Sankoff",
title = "The Kernel of Maximum Agreement Subtrees",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1023--1031",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.11",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A Maximum Agreement SubTree (MAST) is a largest
subtree common to a set of trees and serves as a
summary of common substructure in the trees. A single
MAST can be misleading, however, since there can be an
exponential number of MASTs, and two MASTs for the same
tree set do not even necessarily share any leaves. In
this paper, we introduce the notion of the Kernel
Agreement SubTree (KAST), which is the summary of the
common substructure in all MASTs, and show that it can
be calculated in polynomial time (for trees with
bounded degree). Suppose the input trees represent
competing hypotheses for a particular phylogeny. We
explore the utility of the KAST as a method to discern
the common structure of confidence, and as a measure of
how confident we are in a given tree set. We also show
the trend of the KAST, as compared to other consensus
methods, on the set of all trees visited during a
Bayesian analysis of flatworm genomes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2012:RRN,
author = "Xiuwei Zhang and Bernard M. E. Moret",
title = "Refining Regulatory Networks through Phylogenetic
Transfer of Information",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1032--1045",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.62",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The experimental determination of transcriptional
regulatory networks in the laboratory remains difficult
and time-consuming, while computational methods to
infer these networks provide only modest accuracy. The
latter can be attributed partly to the limitations of a
single-organism approach. Computational biology has
long used comparative and evolutionary approaches to
extend the reach and accuracy of its analyses. In this
paper, we describe ProPhyC, a probabilistic
phylogenetic model and associated inference algorithms,
designed to improve the inference of regulatory
networks for a family of organisms by using known
evolutionary relationships among these organisms.
ProPhyC can be used with various network evolutionary
models and any existing inference method. Extensive
experimental results on both biological and synthetic
data confirm that our model (through its associated
refinement algorithms) yields substantial improvement
in the quality of inferred networks over all current
methods. We also compare ProPhyC with a transfer
learning approach we design. This approach also uses
phylogenetic relationships while inferring regulatory
networks for a family of organisms. Using similar input
information but designed in a very different framework,
this transfer learning approach does not perform better
than ProPhyC, which indicates that ProPhyC makes good
use of the evolutionary information.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hodgkinson:2012:ADM,
author = "Luqman Hodgkinson and Richard M. Karp",
title = "Algorithms to Detect Multiprotein Modularity Conserved
during Evolution",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1046--1058",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.125",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Detecting essential multiprotein modules that change
infrequently during evolution is a challenging
algorithmic task that is important for understanding
the structure, function, and evolution of the
biological cell. In this paper, we define a measure of
modularity for interactomes and present a linear-time
algorithm, Produles, for detecting multiprotein
modularity conserved during evolution that improves on
the running time of previous algorithms for related
problems and offers desirable theoretical guarantees.
We present a biologically motivated graph theoretic set
of evaluation measures complementary to previous
evaluation measures, demonstrate that Produles exhibits
good performance by all measures, and describe certain
recurrent anomalies in the performance of previous
algorithms that are not detected by previous measures.
Consideration of the newly defined measures and
algorithm performance on these measures leads to useful
insights on the nature of interactomics data and the
goals of previous and current algorithms. Through
randomization experiments, we demonstrate that
conserved modularity is a defining characteristic of
interactomes. Computational experiments on current
experimentally derived interactomes for \bioname{Homo
sapiens} and \bioname{Drosophila melanogaster}, combining
results across algorithms, show that nearly 10 percent
of current interactome proteins participate in
multiprotein modules with good evidence in the protein
interaction data of being conserved between human and
Drosophila.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jiang:2012:PPF,
author = "Jonathan Q. Jiang and Lisa J. McQuay",
title = "Predicting Protein Function by Multi-Label Correlated
Semi-Supervised Learning",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1059--1069",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.156",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Assigning biological functions to uncharacterized
proteins is a fundamental problem in the postgenomic
era. The increasing availability of large amounts of
data on protein-protein interactions (PPIs) has led to
the emergence of a considerable number of computational
methods for determining protein function in the context
of a network. These algorithms, however, treat each
functional class in isolation and thereby often suffer
from the difficulty of the scarcity of labeled data. In
reality, different functional classes are naturally
dependent on one another. We propose a new algorithm,
Multi-label Correlated Semi-supervised Learning (MCSL),
to incorporate the intrinsic correlations among
functional classes into protein function prediction by
leveraging the relationships provided by the PPI
network and the functional class network. The guiding
intuition is that the classification function should be
sufficiently smooth on subgraphs where the respective
topologies of these two networks are a good match. We
encode this intuition as regularized learning with
intraclass and interclass consistency, which can be
understood as an extension of the graph-based learning
with local and global consistency (LGC) method. Cross
validation on the yeast proteome illustrates that MCSL
consistently outperforms several state-of-the-art
methods. Most notably, it effectively overcomes the
problem associated with scarcity of label data. The
supplementary files are freely available at
\path=http://sites.google.com/site/csaijiang/MCSL=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2012:IEP,
author = "Jianxin Wang and Min Li and Huan Wang and Yi Pan",
title = "Identification of Essential Proteins Based on Edge
Clustering Coefficient",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1070--1080",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.147",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Identification of essential proteins is key to
understanding the minimal requirements for cellular
life and important for drug design. The rapid increase
of available protein-protein interaction (PPI) data has
made it possible to detect protein essentiality on
network level. A series of centrality measures have
been proposed to discover essential proteins based on
network topology. However, most of them tended to focus
only on the location of singleprotein, but ignored the
relevance between interactions and protein
essentiality. In this paper, a new centrality measure
for identifying essential proteins based on edge
clustering coefficient, named as NC, is proposed.
Different from previous centrality measures, NC
considers both the centrality of a node and the
relationship between it and its neighbors. For each
interaction in the network, we calculate its edge
clustering coefficient. A node's essentiality is
determined by the sum of the edge clustering
coefficients of interactions connecting it and its
neighbors. The new centrality measure NC takes into
account the modular nature of protein essentiality. NC
is applied to three different types of yeast
protein-protein interaction networks, which are
obtained from the DIP database, the MIPS database and
the BioGRID database, respectively. The experimental
results on the three different networks show that the
number of essential proteins discovered by NC
universally exceeds that discovered by the six other
centrality measures: DC, BC, CC, SC, EC, and IC.
Moreover, the essential proteins discovered by NC show
significant cluster effect.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jang:2012:CMP,
author = "Woo-Hyuk Jang and Suk-Hoon Jung and Dong-Soo Han",
title = "A Computational Model for Predicting Protein
Interactions Based on Multidomain Collaboration",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1081--1090",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.55",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recently, several domain-based computational models
for predicting protein-protein interactions (PPIs) have
been proposed. The conventional methods usually infer
domain or domain combination (DC) interactions from
already known interacting sets of proteins, and then
predict PPIs using the information. However, the
majority of these models often have limitations in
providing detailed information on which domain pair
(single domain interaction) or DC pair (multidomain
interaction) will actually interact for the predicted
protein interaction. Therefore, a more comprehensive
and concrete computational model for the prediction of
PPIs is needed. We developed a computational model to
predict PPIs using the information of intraprotein
domain cohesion and interprotein DC coupling
interaction. A method of identifying the primary
interacting DC pair was also incorporated into the
model in order to infer actual participants in a
predicted interaction. Our method made an apparent
improvement in the PPI prediction accuracy, and the
primary interacting DC pair identification was valid
specifically in predicting multidomain protein
interactions. In this paper, we demonstrate that (1)
the intraprotein domain cohesion is meaningful in
improving the accuracy of domain-based PPI prediction,
(2) a prediction model incorporating the intradomain
cohesion enables us to identify the primary interacting
DC pair, and (3) a hybrid approach using the
intra/interdomain interaction information can lead to a
more accurate prediction.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Choi:2012:HAS,
author = "Ickwon Choi and Michael W. Kattan and Brian J. Wells
and Changhong Yu",
title = "A Hybrid Approach to Survival Model Building Using
Integration of Clinical and Molecular Information in
Censored Data",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1091--1105",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.31",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In medical society, the prognostic models, which use
clinicopathologic features and predict prognosis after
a certain treatment, have been externally validated and
used in practice. In recent years, most research has
focused on high dimensional genomic data and small
sample sizes. Since clinically similar but molecularly
heterogeneous tumors may produce different clinical
outcomes, the combination of clinical and genomic
information, which may be complementary, is crucial to
improve the quality of prognostic predictions. However,
there is a lack of an integrating scheme for
clinic-genomic models due to the $ {\rm P} \gg {\rm N}
$ problem, in particular, for a parsimonious model. We
propose a methodology to build a reduced yet accurate
integrative model using a hybrid approach based on the
Cox regression model, which uses several dimension
reduction techniques, $ {\rm L}_2 $ penalized maximum
likelihood estimation (PMLE), and resampling methods to
tackle the problem. The predictive accuracy of the
modeling approach is assessed by several metrics via an
independent and thorough scheme to compare competing
methods. In breast cancer data studies on a metastasis
and death event, we show that the proposed methodology
can improve prediction accuracy and build a final model
with a hybrid signature that is parsimonious when
integrating both types of variables.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lazar:2012:SFT,
author = "Cosmin Lazar and Jonatan Taminau and Stijn Meganck and
David Steenhoff and Alain Coletta and Colin Molter and
Virginie de Schaetzen and Robin Duque and Hugues
Bersini and Ann Nowe",
title = "A Survey on Filter Techniques for Feature Selection in
Gene Expression Microarray Analysis",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1106--1119",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.33",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A plenitude of feature selection (FS) methods is
available in the literature, most of them rising as a
need to analyze data of very high dimension, usually
hundreds or thousands of variables. Such data sets are
now available in various application areas like
combinatorial chemistry, text mining, multivariate
imaging, or bioinformatics. As a general accepted rule,
these methods are grouped in filters, wrappers, and
embedded methods. More recently, a new group of methods
has been added in the general framework of FS: ensemble
techniques. The focus in this survey is on filter
feature selection methods for informative feature
discovery in gene expression microarray (GEM) analysis,
which is also known as differentially expressed genes
(DEGs) discovery, gene prioritization, or biomarker
discovery. We present them in a unified framework,
using standardized notations in order to reveal their
technical details and to highlight their common
characteristics as well as their particularities.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Felicioli:2012:BEA,
author = "Claudio Felicioli and Roberto Marangoni",
title = "{BpMatch}: An Efficient Algorithm for a Segmental
Analysis of Genomic Sequences",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1120--1127",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.30",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Here, we propose BpMatch: an algorithm that, working
on a suitably modified suffix-tree data structure, is
able to compute, in a fast and efficient way, the
coverage of a source sequence $S$ on a target sequence
$T$, by taking into account direct and reverse
segments, eventually overlapped. Using BpMatch, the
operator should define a priori, the minimum length $l$
of a segment and the minimum number of occurrences
minRep, so that only segments longer than $l$ and
having a number of occurrences greater than minRep are
considered to be significant. BpMatch outputs the
significant segments found and the computed
segment-based distance. On the worst case, assuming the
alphabet dimension $d$ is a constant, the time required
by BpMatch to calculate the coverage is $ O(l^2 n) $.
On the average, by setting $ l \ge 2 \log_d(n) $, the
time required to calculate the coverage is only $ O(n)
$. BpMatch, thanks to the minRep parameter, can also be
used to perform a self-covering: to cover a sequence
using segments coming from itself, by avoiding the
trivial solution of having a single segment coincident
with the whole sequence. The result of the
self-covering approach is a spectral representation of
the repeats contained in the sequence. BpMatch is
freely available on:
www.sourceforge.net/projects/bpmatch/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Willson:2012:CHB,
author = "Stephen Willson",
title = "{CSD} Homomorphisms between Phylogenetic Networks",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1128--1138",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.52",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Since Darwin, species trees have been used as a
simplified description of the relationships which
summarize the complicated network $N$ of reality.
Recent evidence of hybridization and lateral gene
transfer, however, suggest that there are situations
where trees are inadequate. Consequently it is
important to determine properties that characterize
networks closely related to $N$ and possibly more
complicated than trees but lacking the full complexity
of $N$. A connected surjective digraph map (CSD) is a
map $f$ from one network $N$ to another network $M$
such that every arc is either collapsed to a single
vertex or is taken to an arc, such that $f$ is
surjective, and such that the inverse image of a vertex
is always connected. CSD maps are shown to behave well
under composition. It is proved that if there is a CSD
map from $N$ to $M$, then there is a way to lift an
undirected version of $M$ into $N$, often with added
resolution. A CSD map from $N$ to $M$ puts strong
constraints on $N$. In general, it may be useful to
study classes of networks such that, for any $N$, there
exists a CSD map from $N$ to some standard member of
that class.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sengupta:2012:NDP,
author = "Soumi Sengupta and Sanghamitra Bandyopadhyay",
title = "De Novo Design of Potential {RecA} Inhibitors Using
{MultiObjective} Optimization",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1139--1154",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.35",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "De novo ligand design involves optimization of several
ligand properties such as binding affinity, ligand
volume, drug likeness, etc. Therefore, optimization of
these properties independently and simultaneously seems
appropriate. In this paper, the ligand design problem
is modeled in a multiobjective using Archived
MultiObjective Simulated Annealing (AMOSA) as the
underlying search algorithm. The multiple objectives
considered are the energy components similarity to a
known inhibitor and a novel drug likeliness measure
based on Lipinski's rule of five. RecA protein of
Mycobacterium tuberculosis, causative agent of
tuberculosis, is taken as the target for the drug
design. To gauge the goodness of the results, they are
compared to the outputs of LigBuilder, NEWLEAD, and
Variable genetic algorithm (VGA). The same problem has
also been modeled using a well-established genetic
algorithm-based multiobjective optimization technique,
Nondominated Sorting Genetic Algorithm-II (NSGA-II), to
find the efficacy of AMOSA through comparative
analysis. Results demonstrate that while some small
molecules designed by the proposed approach are
remarkably similar to the known inhibitors of RecA,
some new ones are discovered that may be potential
candidates for novel lead molecules against
tuberculosis.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2012:DOR,
author = "Peng Chen and Limsoon Wong and Jinyan Li",
title = "Detection of Outlier Residues for Improving Interface
Prediction in Protein Heterocomplexes",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1155--1165",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.58",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Sequence-based understanding and identification of
protein binding interfaces is a challenging research
topic due to the complexity in protein systems and the
imbalanced distribution between interface and
noninterface residues. This paper presents an outlier
detection idea to address the redundancy problem in
protein interaction data. The cleaned training data are
then used for improving the prediction performance. We
use three novel measures to describe the extent a
residue is considered as an outlier in comparison to
the other residues: the distance of a residue instance
from the center instance of all residue instances of
the same class label (Dist), the probability of the
class label of the residue instance (PCL), and the
importance of within-class and between-class (IWB)
residue instances. Outlier scores are computed by
integrating the three factors; instances with a
sufficiently large score are treated as outliers and
removed. The data sets without outliers are taken as
input for a support vector machine (SVM) ensemble. The
proposed SVM ensemble trained on input data without
outliers performs better than that with outliers. Our
method is also more accurate than many literature
methods on benchmark data sets. From our empirical
studies, we found that some outlier interface residues
are truly near to noninterface regions, and some
outlier noninterface residues are close to interface
regions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mirceva:2012:EAR,
author = "Georgina Mirceva and Ivana Cingovska and Zoran Dimov
and Danco Davcev",
title = "Efficient Approaches for Retrieving Protein Tertiary
Structures",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1166--1179",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2011.138",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The 3D conformation of a protein in the space is the
main factor which determines its function in living
organisms. Due to the huge amount of newly discovered
proteins, there is a need for fast and accurate
computational methods for retrieving protein
structures. Their purpose is to speed up the process of
understanding the structure-to-function relationship
which is crucial in the development of new drugs. There
are many algorithms addressing the problem of protein
structure retrieval. In this paper, we present several
novel approaches for retrieving protein tertiary
structures. We present our voxel-based descriptor. Then
we present our protein ray-based descriptors which are
applied on the interpolated protein backbone. We
introduce five novel wavelet descriptors which perform
wavelet transforms on the protein distance matrix. We
also propose an efficient algorithm for distance matrix
alignment named Matrix Alignment by Sequence Alignment
within Sliding Window (MASASW), which has shown as much
faster than DALI, CE, and MatAlign. We compared our
approaches between themselves and with several existing
algorithms, and they generally prove to be fast and
accurate. MASASW achieves the highest accuracy. The ray
and wavelet-based descriptors as well as MASASW are
more accurate than CE.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{DeFrancesco:2012:EGE,
author = "Nicoletta {De Francesco} and Giuseppe Lettieri and
Luca Martini",
title = "Efficient Genotype Elimination via Adaptive Allele
Consolidation",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1180--1189",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.46",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We propose the technique of Adaptive Allele
Consolidation, that greatly improves the performance of
the Lange-Goradia algorithm for genotype elimination in
pedigrees, while still producing equivalent output.
Genotype elimination consists in removing from a
pedigree those genotypes that are impossible according
to the Mendelian law of inheritance. This is used to
find errors in genetic data and is useful as a
preprocessing step in other analyses (such as linkage
analysis or haplotype imputation). The problem of
genotype elimination is intrinsically combinatorial,
and Allele Consolidation is an existing technique where
several alleles are replaced by a single ``lumped''
allele in order to reduce the number of combinations of
genotypes that have to be considered, possibly at the
expense of precision. In existing Allele Consolidation
techniques, alleles are lumped once and for all before
performing genotype elimination. The idea of Adaptive
Allele Consolidation is to dynamically change the set
of alleles that are lumped together during the
execution of the Lange-Goradia algorithm, so that both
high performance and precision are achieved. We have
implemented the technique in a tool called Celer and
evaluated it on a large set of scenarios, with good
results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2012:HSP,
author = "Yijia Zhang and Hongfei Lin and Zhihao Yang and Jian
Wang and Yanpeng Li",
title = "Hash Subgraph Pairwise Kernel for Protein-Protein
Interaction Extraction",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1190--1202",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.50",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Extracting protein-protein interaction (PPI) from
biomedical literature is an important task in
biomedical text mining (BioTM). In this paper, we
propose a hash subgraph pairwise (HSP) kernel-based
approach for this task. The key to the novel kernel is
to use the hierarchical hash labels to express the
structural information of subgraphs in a linear time.
We apply the graph kernel to compute dependency graphs
representing the sentence structure for protein-protein
interaction extraction task, which can efficiently make
use of full graph structural information, and
particularly capture the contiguous topological and
label information ignored before. We evaluate the
proposed approach on five publicly available PPI
corpora. The experimental results show that our
approach significantly outperforms all-path kernel
approach on all five corpora and achieves
state-of-the-art performance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Noor:2012:IGR,
author = "Amina Noor and Erchin Serpedin and Mohamed Nounou and
Hazem Nounou",
title = "Inferring Gene Regulatory Networks via Nonlinear
State-Space Models and Exploiting Sparsity",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1203--1211",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.32",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper considers the problem of learning the
structure of gene regulatory networks from gene
expression time series data. A more realistic scenario
when the state space model representing a gene network
evolves nonlinearly is considered while a linear model
is assumed for the microarray data. To capture the
nonlinearity, a particle filter-based state estimation
algorithm is considered instead of the contemporary
linear approximation-based approaches. The parameters
characterizing the regulatory relations among various
genes are estimated online using a Kalman filter. Since
a particular gene interacts with a few other genes
only, the parameter vector is expected to be sparse.
The state estimates delivered by the particle filter
and the observed microarray data are then subjected to
a LASSO-based least squares regression operation which
yields a parsimonious and efficient description of the
regulatory network by setting the irrelevant
coefficients to zero. The performance of the
aforementioned algorithm is compared with the extended
Kalman filter (EKF) and Unscented Kalman Filter (UKF)
employing the Mean Square Error (MSE) as the fidelity
criterion in recovering the parameters of gene
regulatory networks from synthetic data and real
biological data. Extensive computer simulations
illustrate that the proposed particle filter-based
network inference algorithm outperforms EKF and UKF,
and therefore, it can serve as a natural framework for
modeling gene regulatory networks with nonlinear and
sparse structure.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yang:2012:PRP,
author = "Chao Yang and Zengyou He and Can Yang and Weichuan
Yu",
title = "Peptide Reranking with Protein-Peptide Correspondence
and Precursor Peak Intensity Information",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1212--1219",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.29",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Searching tandem mass spectra against a protein
database has been a mainstream method for peptide
identification. Improving peptide identification
results by ranking true Peptide-Spectrum Matches (PSMs)
over their false counterparts leads to the development
of various reranking algorithms. In peptide reranking,
discriminative information is essential to distinguish
true PSMs from false PSMs. Generally, most peptide
reranking methods obtain discriminative information
directly from database search scores or by training
machine learning models. Information in the protein
database and MS1 spectra (i.e., single stage MS
spectra) is ignored. In this paper, we propose to use
information in the protein database and MS1 spectra to
rerank peptide identification results. To
quantitatively analyze their effects to peptide
reranking results, three peptide reranking methods are
proposed: PPMRanker, PPIRanker, and MIRanker. PPMRanker
only uses Protein-Peptide Map (PPM) information from
the protein database, PPIRanker only uses Precursor
Peak Intensity (PPI) information, and MIRanker employs
both PPM information and PPI information. According to
our experiments on a standard protein mixture data set,
a human data set and a mouse data set, PPMRanker and
MIRanker achieve better peptide reranking results than
PetideProphet, PeptideProphet+NSP (number of sibling
peptides) and a score regularization method SRPI. The
source codes of PPMRanker, PPIRanker, and MIRanker, and
all supplementary documents are available at our
website:
\path=http://bioinformatics.ust.hk/pepreranking/=.
Alternatively, these documents can also be downloaded
from:
\path=http://sourceforge.net/projects/pepreranking/=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jiang:2012:SFU,
author = "Haitao Jiang and Chunfang Zheng and David Sankoff and
Binhai Zhu",
title = "Scaffold Filling under the Breakpoint and Related
Distances",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1220--1229",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.57",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Motivated by the trend of genome sequencing without
completing the sequence of the whole genomes, a problem
on filling an incomplete multichromosomal genome (or
scaffold) I with respect to a complete target genome
$G$ was studied. The objective is to minimize the
resulting genomic distance between $ I' $ and $G$,
where $ I' $ is the corresponding filled scaffold. We
call this problem the one-sided scaffold filling
problem. In this paper, we conduct a systematic study
for the scaffold filling problem under the breakpoint
distance and its variants, for both unichromosomal and
multichromosomal genomes (with and without gene
repetitions). When the input genome contains no gene
repetition (i.e., is a fragment of a permutation), we
show that the two-sided scaffold filling problem (i.e.,
$G$ is also incomplete) is polynomially solvable for
unichromosomal genomes under the breakpoint distance
and for multichromosomal genomes under the genomic (or
DCJ-Double-Cut-and-Join) distance. However, when the
input genome contains some repeated genes, even the
one-sided scaffold filling problem becomes NP-complete
when the similarity measure is the maximum number of
adjacencies between two sequences. For this problem, we
also present efficient constant-factor approximation
algorithms: factor-2 for the general case and factor
1.33 for the one-sided case.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pal:2012:TDR,
author = "Ranadip Pal and Sonal Bhattacharya",
title = "Transient Dynamics of Reduced-Order Models of Genetic
Regulatory Networks",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1230--1244",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.37",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In systems biology, a number of detailed genetic
regulatory networks models have been proposed that are
capable of modeling the fine-scale dynamics of gene
expression. However, limitations on the type and
sampling frequency of experimental data often prevent
the parameter estimation of the detailed models.
Furthermore, the high computational complexity involved
in the simulation of a detailed model restricts its
use. In such a scenario, reduced-order models capturing
the coarse-scale behavior of the network are frequently
applied. In this paper, we analyze the dynamics of a
reduced-order Markov Chain model approximating a
detailed Stochastic Master Equation model. Utilizing a
reduction mapping that maintains the aggregated
steady-state probability distribution of stochastic
master equation models, we provide bounds on the
deviation of the Markov Chain transient distribution
from the transient aggregated distributions of the
stochastic master equation model.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Torres:2012:UGE,
author = "Jose Salavert Torres and Ignacio Blanquer Espert and
Andres Tomas Dominguez and Vicente Hernendez and
Ignacio Medina and Joaquin Terraga and Joaquin Dopazo",
title = "Using {GPUs} for the Exact Alignment of Short-Read
Genetic Sequences by Means of the {Burrows--Wheeler}
Transform",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1245--1256",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.49",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "General Purpose Graphic Processing Units (GPGPUs)
constitute an inexpensive resource for
computing-intensive applications that could exploit an
intrinsic fine-grain parallelism. This paper presents
the design and implementation in GPGPUs of an exact
alignment tool for nucleotide sequences based on the
Burrows--Wheeler Transform. We compare this algorithm
with state-of-the-art implementations of the same
algorithm over standard CPUs, and considering the same
conditions in terms of I/O. Excluding disk transfers,
the implementation of the algorithm in GPUs shows a
speedup larger than $ 12 \times $, when compared to CPU
execution. This implementation exploits the parallelism
by concurrently searching different sequences on the
same reference search tree, maximizing memory locality
and ensuring a symmetric access to the data. The paper
describes the behavior of the algorithm in GPU, showing
a good scalability in the performance, only limited by
the size of the GPU inner memory.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2012:NUF,
author = "Shaohong Zhang and Hau-San Wong and Ying Shen and
Dongqing Xie",
title = "A New Unsupervised Feature Ranking Method for Gene
Expression Data Based on Consensus Affinity",
journal = j-TCBB,
volume = "9",
number = "4",
pages = "1257--1263",
month = jul,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.34",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 25 16:09:45 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Feature selection is widely established as one of the
fundamental computational techniques in mining
microarray data. Due to the lack of categorized
information in practice, unsupervised feature selection
is more practically important but correspondingly more
difficult. Motivated by the cluster ensemble
techniques, which combine multiple clustering solutions
into a consensus solution of higher accuracy and
stability, recent efforts in unsupervised feature
selection proposed to use these consensus solutions as
oracles. However, these methods are dependent on both
the particular cluster ensemble algorithm used and the
knowledge of the true cluster number. These methods
will be unsuitable when the true cluster number is not
available, which is common in practice. In view of the
above problems, a new unsupervised feature ranking
method is proposed to evaluate the importance of the
features based on consensus affinity. Different from
previous works, our method compares the corresponding
affinity of each feature between a pair of instances
based on the consensus matrix of clustering solutions.
As a result, our method alleviates the need to know the
true number of clusters and the dependence on
particular cluster ensemble approaches as in previous
works. Experiments on real gene expression data sets
demonstrate significant improvement of the feature
ranking results when compared to several
state-of-the-art techniques.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2012:GEA,
author = "Yi-Ping Phoebe Chen",
title = "Guest Editorial: Application and Development of
Bioinformatics",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1265",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.96",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Handoko:2012:QAA,
author = "Stephanus Daniel Handoko and Xuchang Ouyang and Chinh
Tran To Su and Chee Keong Kwoh and Yew Soon Ong",
title = "{QuickVina}: Accelerating {AutoDock Vina} Using
Gradient-Based Heuristics for Global Optimization",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1266--1272",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.82",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Predicting binding between macromolecule and small
molecule is a crucial phase in the field of rational
drug design. AutoDock Vina, one of the most widely used
docking software released in 2009, uses an empirical
scoring function to evaluate the binding affinity
between the molecules and employs the iterated local
search global optimizer for global optimization,
achieving a significantly improved speed and better
accuracy of the binding mode prediction compared its
predecessor, AutoDock 4. In this paper, we propose
further improvement in the local search algorithm of
Vina by heuristically preventing some intermediate
points from undergoing local search. Our improved
version of Vina-dubbed QVina-achieved a maximum
acceleration of about 25 times with the average
speed-up of 8.34 times compared to the original Vina
when tested on a set of 231 protein-ligand complexes
while maintaining the optimal scores mostly identical.
Using our heuristics, larger number of different
ligands can be quickly screened against a given
receptor within the same time frame.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yang:2012:IXT,
author = "Pengyi Yang and Jie Ma and Penghao Wang and Yunping
Zhu and Bing B. Zhou and Yee Hwa Yang",
title = "Improving {X!Tandem} on Peptide Identification from
Mass Spectrometry by Self-Boosted Percolator",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1273--1280",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.86",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A critical component in mass spectrometry (MS)-based
proteomics is an accurate protein identification
procedure. Database search algorithms commonly generate
a list of peptide-spectrum matches (PSMs). The validity
of these PSMs is critical for downstream analysis since
proteins that are present in the sample are inferred
from those PSMs. A variety of postprocessing algorithms
have been proposed to validate and filter PSMs. Among
them, the most popular ones include a semi-supervised
learning (SSL) approach known as Percolator and an
empirical modeling approach known as PeptideProphet.
However, they are predominantly designed for commercial
database search algorithms, i.e., SEQUEST and MASCOT.
Therefore, it is highly desirable to extend and
optimize those PSM postprocessing algorithms for open
source database search algorithms such as X!Tandem. In
this paper, we propose a Self-boosted Percolator for
postprocessing X!Tandem search results. We find that
the SSL algorithm utilized by Percolator depends
heavily on the initial ranking of PSMs. Starting with a
poor PSM ranking list may cause Percolator to perform
suboptimally. By implementing Percolator in a cascade
learning manner, we can progressively improve the
performance through multiple boost runs, enabling many
more PSM identifications without sacrificing false
discovery rate (FDR).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wan:2012:CAD,
author = "Lin Wan and Fengzhu Sun",
title = "{CEDER}: Accurate Detection of Differentially
Expressed Genes by Combining Significance of Exons
Using {RNA-Seq}",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1281--1292",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.83",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "RNA-Seq is widely used in transcriptome studies, and
the detection of differentially expressed genes (DEGs)
between two classes of individuals, e.g., cases versus
controls, using RNA-Seq is of fundamental importance.
Many statistical methods for DEG detection based on
RNA-Seq data have been developed and most of them are
based on the read counts mapped to individual genes. On
the other hand, genes are composed of exons and the
distribution of reads for the different exons can be
heterogeneous. We hypothesize that the detection
accuracy of differentially expressed genes can be
increased by analyzing individual exons within a gene
and then combining the results of the exons. We
therefore developed a novel program, termed CEDER, to
accurately detect DEGs by combining the significance of
the exons. CEDER first tests for differentially
expressed exons yielding a p-value for each, and then
gives a score indicating the potential for a gene to be
differentially expressed by integrating the p-values of
the exons in the gene. We showed that CEDER can
significantly increase the accuracy of existing methods
for detecting DEGs on two benchmark RNA-Seq data sets
and simulated datasets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rottger:2012:HLD,
author = "Richard Rottger and Ulrich Ruckert and Jan Taubert and
Jan Baumbach",
title = "How Little Do We Actually Know? {On} the Size of Gene
Regulatory Networks",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1293--1300",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.71",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The National Center for Biotechnology Information
(NCBI) recently announced the availability of whole
genome sequences for more than 1,000 species. And the
number of sequenced individual organisms is growing.
Ongoing improvement of DNA sequencing technology will
further contribute to this, enabling large-scale
evolution and population genetics studies. However, the
availability of sequence information is only the first
step in understanding how cells survive, reproduce, and
adjust their behavior. The genetic control behind
organized development and adaptation of complex
organisms still remains widely undetermined. One major
molecular control mechanism is transcriptional gene
regulation. The direct juxtaposition of the total
number of sequenced species to the handful of model
organisms with known regulations is surprising. Here,
we investigate how little we even know about these
model organisms. We aim to predict the sizes of the
whole-organism regulatory networks of seven species. In
particular, we provide statistical lower bounds for the
expected number of regulations. For Escherichia coli we
estimate at most 37 percent of the expected gene
regulatory interactions to be already discovered, 24
percent for Bacillus subtilis, and $<$ 3\% human,
respectively. We conclude that even for our best
researched model organisms we still lack substantial
understanding of fundamental molecular control
mechanisms, at least on a large scale.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ashtawy:2012:CAR,
author = "Hossam M. Ashtawy and Nihar R. Mahapatra",
title = "A Comparative Assessment of Ranking Accuracies of
Conventional and Machine-Learning-Based Scoring
Functions for Protein-Ligand Binding Affinity
Prediction",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1301--1313",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.36",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Accurately predicting the binding affinities of large
sets of protein-ligand complexes efficiently is a key
challenge in computational biomolecular science, with
applications in drug discovery, chemical biology, and
structural biology. Since a scoring function (SF) is
used to score, rank, and identify drug leads, the
fidelity with which it predicts the affinity of a
ligand candidate for a protein's binding site has a
significant bearing on the accuracy of virtual
screening. Despite intense efforts in developing
conventional SFs, which are either force-field based,
knowledge-based, or empirical, their limited ranking
accuracy has been a major roadblock toward
cost-effective drug discovery. Therefore, in this work,
we explore a range of novel SFs employing different
machine-learning (ML) approaches in conjunction with a
variety of physicochemical and geometrical features
characterizing protein-ligand complexes. We assess the
ranking accuracies of these new ML-based SFs as well as
those of conventional SFs in the context of the 2007
and 2010 PDBbind benchmark data sets on both diverse
and protein-family-specific test sets. We also
investigate the influence of the size of the training
data set and the type and number of features used on
ranking accuracy. Within clusters of protein-ligand
complexes with different ligands bound to the same
target protein, we find that the best ML-based SF is
able to rank the ligands correctly based on their
experimentally determined binding affinities 62.5
percent of the time and identify the top binding ligand
78.1 percent of the time. For this SF, the Spearman
correlation coefficient between ranks of ligands
ordered by predicted and experimentally determined
binding affinities is 0.771. Given the challenging
nature of the ranking problem and that SFs are used to
screen millions of ligands, this represents a
significant improvement over the best conventional SF
we studied, for which the corresponding ranking
performance values are 57.8 percent, 73.4 percent, and
0.677.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fages:2012:GEI,
author = "Fran{\c{c}}ois Fages and Sylvain Soliman",
title = "{Guest Editors}' Introduction to the Special Section
on Computational Methods in Systems Biology",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1314--1315",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.97",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Omony:2012:EDS,
author = "Jimmy Omony and Astrid R. Mach-Aigner and Leo H. de
Graaff and Gerrit van Straten and Anton J. B. van
Boxtel",
title = "Evaluation of Design Strategies for Time Course
Experiments in Genetic Networks: Case Study of the
{XlnR} Regulon in \bioname{Aspergillus niger}",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1316--1325",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.59",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "One of the challenges in genetic network
reconstruction is finding experimental designs that
maximize the information content in a data set. In this
paper, the information value of mRNA transcription time
course experiments was used to compare experimental
designs. The study concerns the dynamic response of
genes in the XlnR regulon of Aspergillus niger, with
the goal to find the best moment in time to administer
an extra pulse of inducing D-xylose. Low and high
D-xylose pulses were used to perturb the XlnR regulon.
Evaluation of the experimental methods was based on
simulation of the regulon. Models that govern the
regulation of the target genes in this regulon were
used for the simulations. Parameter sensitivity
analysis, the Fisher Information Matrix (FIM) and the
modified E-criterion were used to assess the design
performances. The results show that the best time to
give a second D-xylose pulse is when the D-xylose
concentration from the first pulse has not yet
completely faded away. Due to the presence of a
repression effect the strength of the second pulse must
be optimized, rather than maximized. The results
suggest that the modified E-criterion is a better
metric than the sum of integrals of absolute
sensitivity for comparing alternative designs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Czeizler:2012:PHS,
author = "Eugen Czeizler and Vladimir Rogojin and Ion Petre",
title = "The Phosphorylation of the Heat Shock Factor as a
Modulator for the Heat Shock Response",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1326--1337",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.66",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The heat shock response is a well-conserved defence
mechanism against the accumulation of misfolded
proteins due to prolonged elevated heat. The cell
responds to heat shock by raising the levels of heat
shock proteins (hsp), which are responsible for
chaperoning protein refolding. The synthesis ofhspis
highly regulated at the transcription level by specific
heat shock (transcription) factors (hsf). One of the
regulation mechanisms is the phosphorylation ofhsf's.
Experimental evidence shows a connection between the
hyper-phosphorylation ofhsfs and the transactivation of
thehsp-encoding genes. In this paper, we incorporate
several (de)phosphorylation pathways into an existing
well-validated computational model of the heat shock
response. We analyze the quantitative control of each
of these pathways over the entire process. For each of
these pathways we create detailed computational models
which we subject to parameter estimation in order to
fit them to existing experimental data. In particular,
we find conclusive evidence supporting only one of the
analyzed pathways. Also, we corroborate our results
with a set of computational models of a more reduced
size.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Klarner:2012:TSD,
author = "Hannes Klarner and Heike Siebert and Alexander
Bockmayr",
title = "Time Series Dependent Analysis of Unparametrized
{Thomas} Networks",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1338--1351",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.61",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper is concerned with the analysis of labeled
Thomas networks using discrete time series. It focuses
on refining the given edge labels and on assessing the
data quality. The results are aimed at being
exploitable for experimental design and include the
prediction of new activatory or inhibitory effects of
given interactions and yet unobserved oscillations of
specific components in between specific sampling
intervals. On the formal side, we generalize the
concept of edge labels and introduce a discrete time
series interpretation. This interpretation features two
original concepts: (1) Incomplete measurements are
admissible, and (2) it allows qualitative assumptions
about the changes in gene expression by means of
monotonicity. On the computational side, we provide a
Python script, {\tt erda.py}, that automates the
suggested workflow by model checking and constraint
satisfaction. We illustrate the workflow by
investigating the yeast network IRMA.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Palaniappan:2012:HFF,
author = "Sucheendra K. Palaniappan and S. Akshay and Bing Liu
and Blaise Genest and P. S. Thiagarajan",
title = "A Hybrid Factored Frontier Algorithm for Dynamic
{Bayesian} Networks with a Biopathways Application",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1352--1365",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.60",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Dynamic Bayesian Networks (DBNs) can serve as succinct
probabilistic dynamic models of biochemical networks
[CHECK END OF SENTENCE]. To analyze these models, one
must compute the probability distribution over system
states at a given time point. Doing this exactly is
infeasible for large models; hence one must use
approximate algorithms. The Factored Frontier algorithm
(FF) is one such algorithm [CHECK END OF SENTENCE].
However FF as well as the earlier Boyen-Koller (BK)
algorithm [CHECK END OF SENTENCE] can incur large
errors. To address this, we present a new approximate
algorithm called the Hybrid Factored Frontier (HFF)
algorithm. At each time slice, in addition to
maintaining probability distributions over local
states-as FF does-HFF explicitly maintains the
probabilities of a number of global states called
spikes. When the number of spikes is 0, we get FF and
with all global states as spikes, we get the exact
inference algorithm. We show that by increasing the
number of spikes one can reduce errors while the
additional computational effort required is only
quadratic in the number of spikes. We validated the
performance of HFF on large DBN models of biopathways.
Each pathway has more than 30 species and the
corresponding DBN has more than 3,000 nodes.
Comparisons with FF and BK show that HFF is a useful
and powerful approximate inferencing algorithm for
DBNs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Paoletti:2012:MCM,
author = "Nicola Paoletti and Pietro Lio and Emanuela Merelli
and Marco Viceconti",
title = "Multilevel Computational Modeling and Quantitative
Analysis of Bone Remodeling",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1366--1378",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.51",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Our work focuses on bone remodeling with a multiscale
breadth that ranges from modeling intracellular and
intercellular RANK/RANKL signaling to tissue dynamics,
by developing a multilevel modeling framework. Several
important findings provide clear evidences of the
multiscale properties of bone formation and of the
links between RANK/RANKL and bone density in healthy
and disease conditions. Recent studies indicate that
the circulating levels of OPG and RANKL are inversely
related to bone turnover and Bone Mineral Density (BMD)
and contribute to the development of osteoporosis in
postmenopausal women, and thalassemic patients. We make
use of a spatial process algebra, the Shape Calculus,
to control stochastic cell agents that are continuously
remodeling the bone. We found that our description is
effective for such a multiscale, multilevel process and
that RANKL signaling small dynamic concentration
defects are greatly amplified by the continuous
alternation of absorption and formation resulting in
large structural bone defects. This work contributes to
the computational modeling of complex systems with a
multilevel approach connecting formal languages and
agent-based simulation tools.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hanczar:2012:NMC,
author = "Blaise Hanczar and Avner Bar-Hen",
title = "A New Measure of Classifier Performance for Gene
Expression Data",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1379--1386",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.21",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "One of the major aims of many microarray experiments
is to build discriminatory diagnosis and prognosis
models. A large number of supervised methods have been
proposed in literature for microarray-based
classification for this purpose. Model evaluation and
comparison is a critical issue and, the most of the
time, is based on the classification cost. This
classification cost is based on the costs of false
positives and false negative, that are generally
unknown in diagnostics problems. This uncertainty may
highly impact the evaluation and comparison of the
classifiers. We propose a new measure of classifier
performance that takes account of the uncertainty of
the error. We represent the available knowledge about
the costs by a distribution function defined on the
ratio of the costs. The performance of a classifier is
therefore computed over the set of all possible costs
weighted by their probability distribution. Our method
is tested on both artificial and real microarray data
sets. We show that the performance of classifiers is
very depending of the ratio of the classification
costs. In many cases, the best classifier can be
identified by our new measure whereas the classic error
measures fail.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kamath:2012:EAA,
author = "Uday Kamath and Jack Compton and Rezarta Islamaj Dogan
and Kenneth De Jong and Amarda Shehu",
title = "An Evolutionary Algorithm Approach for Feature
Generation from Sequence Data and Its Application to
{DNA} Splice Site Prediction",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1387--1398",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.53",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Associating functional information with biological
sequences remains a challenge for machine learning
methods. The performance of these methods often depends
on deriving predictive features from the sequences
sought to be classified. Feature generation is a
difficult problem, as the connection between the
sequence features and the sought property is not known
a priori. It is often the task of domain experts or
exhaustive feature enumeration techniques to generate a
few features whose predictive power is then tested in
the context of classification. This paper proposes an
evolutionary algorithm to effectively explore a large
feature space and generate predictive features from
sequence data. The effectiveness of the algorithm is
demonstrated on an important component of the
gene-finding problem, DNA splice site prediction. This
application is chosen due to the complexity of the
features needed to obtain high classification accuracy
and precision. Our results test the effectiveness of
the obtained features in the context of classification
by Support Vector Machines and show significant
improvement in accuracy and precision over
state-of-the-art approaches.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ansari:2012:DPS,
author = "Nadeem A. Ansari and Riyue Bao and Calin Voichita and
Sorin Draghici",
title = "Detecting Phenotype-Specific Interactions between
Biological Processes from Microarray Data and
Annotations",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1399--1409",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.65",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "High throughput technologies enable researchers to
measure expression levels on a genomic scale. However,
the correct and efficient biological interpretation of
such voluminous data remains a challenging problem.
Many tools have been developed for the analysis of GO
terms that are over- or under-represented in a list of
differentially expressed genes. However, a previously
unexplored aspect is the identification of changes in
the way various biological processes interact in a
given condition with respect to a reference. Here, we
present a novel approach that aims at identifying such
interactions between biological processes that are
significantly different in a given phenotype with
respect to normal. The proposed technique uses
vector-space representation, SVD-based dimensionality
reduction, differential weighting, and bootstrapping to
asses the significance of the interactions under the
multiple and complex dependencies expected between the
biological processes. We illustrate our approach on two
real data sets involving breast and lung cancer. More
than 88 percent of the interactions found by our
approach were deemed to be correct by an extensive
manual review of literature. An interesting subset of
such interactions is discussed in detail and shown to
have the potential to open new avenues for research in
lung and breast cancer.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Akutsu:2012:FPA,
author = "Tatsuya Akutsu and Sven Kosub and Avraham A. Melkman
and Takeyuki Tamura",
title = "Finding a Periodic Attractor of a {Boolean} Network",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1410--1421",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.87",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, we study the problem of finding a
periodic attractor of a Boolean network (BN), which
arises in computational systems biology and is known to
be NP-hard. Since a general case is quite hard to
solve, we consider special but biologically important
subclasses of BNs. For finding an attractor of period 2
of a BN consisting of $n$ OR functions of positive
literals, we present a polynomial time algorithm. For
finding an attractor of period 2 of a BN consisting of
$n$ AND/OR functions of literals, we present an $
O(1.985^n) $ time algorithm. For finding an attractor
of a fixed period of a BN consisting of $n$ nested
canalyzing functions and having constant treewidth $w$,
we present an $ O(n^{2 p (w + 1)} \poly (n)) $ time
algorithm.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pang:2012:GSU,
author = "Herbert Pang and Stephen L. George and Ken Hui and
Tiejun Tong",
title = "Gene Selection Using Iterative Feature Elimination
Random Forests for Survival Outcomes",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1422--1431",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.63",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Although many feature selection methods for
classification have been developed, there is a need to
identify genes in high-dimensional data with censored
survival outcomes. Traditional methods for gene
selection in classification problems have several
drawbacks. First, the majority of the gene selection
approaches for classification are single-gene based.
Second, many of the gene selection procedures are not
embedded within the algorithm itself. The technique of
random forests has been found to perform well in
high-dimensional data settings with survival outcomes.
It also has an embedded feature to identify variables
of importance. Therefore, it is an ideal candidate for
gene selection in high-dimensional data with survival
outcomes. In this paper, we develop a novel method
based on the random forests to identify a set of
prognostic genes. We compare our method with several
machine learning methods and various node split
criteria using several real data sets. Our method
performed well in both simulations and real data
analysis. Additionally, we have shown the advantages of
our approach over single-gene-based approaches. Our
method incorporates multivariate correlations in
microarray data for survival outcomes. The described
method allows us to better utilize the information
available from microarray data with survival
outcomes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Karacali:2012:HMV,
author = "Bilge Karacali",
title = "Hierarchical Motif Vectors for Prediction of
Functional Sites in Amino Acid Sequences Using
Quasi-Supervised Learning",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1432--1441",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.68",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We propose hierarchical motif vectors to represent
local amino acid sequence configurations for predicting
the functional attributes of amino acid sites on a
global scale in a quasi-supervised learning framework.
The motif vectors are constructed via wavelet
decomposition on the variations of physico-chemical
amino acid properties along the sequences. We then
formulate a prediction scheme for the functional
attributes of amino acid sites in terms of the
respective motif vectors using the quasi-supervised
learning algorithm that carries out predictions for all
sites in consideration using only the experimentally
verified sites. We have carried out comparative
performance evaluation of the proposed method on the
prediction of N-glycosylation of 55,184 sites
possessing the consensus N-glycosylation sequon
identified over 15,104 human proteins, out of which
only 1,939 were experimentally verified N-glycosylation
sites. In the experiments, the proposed method achieved
better predictive performance than the alternative
strategies from the literature. In addition, the
predicted N-glycosylation sites showed good agreement
with existing potential annotations, while the novel
predictions belonged to proteins known to be modified
by glycosylation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hennings-Yeomans:2012:IPC,
author = "Pablo H. Hennings-Yeomans and Gregory F. Cooper",
title = "Improving the Prediction of Clinical Outcomes from
Genomic Data Using Multiresolution Analysis",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1442--1450",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.80",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The prediction of patient's future clinical outcome,
such as Alzheimer's and cardiac disease, using only
genomic information is an open problem. In cases when
genome-wide association studies (GWASs) are able to
find strong associations between genomic predictors
(e.g., SNPs) and disease, pattern recognition methods
may be able to predict the disease well. Furthermore,
by using signal processing methods, we can capitalize
on latent multivariate interactions of genomic
predictors. Such an approach to genomic pattern
recognition for prediction of clinical outcomes is
investigated in this work. In particular, we show how
multiresolution transforms can be applied to genomic
data to extract cues of multivariate interactions and,
in some cases, improve on the predictive performance of
clinical outcomes of standard classification methods.
Our results show, for example, that an improvement of
about 6 percent increase of the area under the ROC
curve can be achieved using multiresolution spaces to
train logistic regression to predict late-onset
Alzheimer's disease (LOAD) compared to logistic
regression applied directly on SNP data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bonnel:2012:LFP,
author = "Nicolas Bonnel and Pierre-Fran{\c{c}}ois Marteau",
title = "{LNA}: Fast Protein Structural Comparison Using a
{Laplacian} Characterization of Tertiary Structure",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1451--1458",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.64",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In the last two decades, a lot of protein 3D shapes
have been discovered, characterized, and made available
thanks to the Protein Data Bank (PDB), that is
nevertheless growing very quickly. New scalable methods
are thus urgently required to search through the PDB
efficiently. This paper presents an approach entitled
LNA (Laplacian Norm Alignment) that performs a
structural comparison of two proteins with dynamic
programming algorithms. This is achieved by
characterizing each residue in the protein with scalar
features. The feature values are calculated using a
Laplacian operator applied on the graph corresponding
to the adjacency matrix of the residues. The weighted
Laplacian operator we use estimates, at various scales,
local deformations of the topology where each residue
is located. On some benchmarks, which are widely shared
by the community, we obtain qualitatively similar
results compared to other competing approaches, but
with an algorithm one or two order of magnitudes
faster. 180,000 protein comparisons can be done within
1 second with a single recent Graphical Processing Unit
(GPU), which makes our algorithm very scalable and
suitable for real-time database querying across the
web.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sambo:2012:MMO,
author = "Francesco Sambo and Marco A. Montes de Oca and Barbara
{Di Camillo} and Gianna Toffolo and Thomas Stutzle",
title = "{MORE}: Mixed Optimization for Reverse Engineering ---
an Application to Modeling Biological Networks Response
via Sparse Systems of Nonlinear Differential
Equations",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1459--1471",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.56",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Reverse engineering is the problem of inferring the
structure of a network of interactions between
biological variables from a set of observations. In
this paper, we propose an optimization algorithm,
called MORE, for the reverse engineering of biological
networks from time series data. The model inferred by
MORE is a sparse system of nonlinear differential
equations, complex enough to realistically describe the
dynamics of a biological system. MORE tackles
separately the discrete component of the problem, the
determination of the biological network topology, and
the continuous component of the problem, the strength
of the interactions. This approach allows us both to
enforce system sparsity, by globally constraining the
number of edges, and to integrate a priori information
about the structure of the underlying interaction
network. Experimental results on simulated and
real-world networks show that the mixed
discrete/continuous optimization approach of MORE
significantly outperforms standard continuous
optimization and that MORE is competitive with the
state of the art in terms of accuracy of the inferred
networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jacklin:2012:NCO,
author = "Neil Jacklin and Zhi Ding and Wei Chen and Chunqi
Chang",
title = "Noniterative Convex Optimization Methods for Network
Component Analysis",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1472--1481",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.81",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This work studies the reconstruction of gene
regulatory networks by the means of network component
analysis (NCA). We will expound a family of convex
optimization-based methods for estimating the
transcription factor control strengths and the
transcription factor activities (TFAs). The approach
taken in this work is to decompose the problem into a
network connectivity strength estimation phase and a
transcription factor activity estimation phase. In the
control strength estimation phase, we formulate a new
subspace-based method incorporating a choice of
multiple error metrics. For the source estimation phase
we propose a total least squares (TLS) formulation that
generalizes many existing methods. Both estimation
procedures are noniterative and yield the optimal
estimates according to various proposed error metrics.
We test the performance of the proposed algorithms on
simulated data and experimental gene expression data
for the yeast Saccharomyces cerevisiae and demonstrate
that the proposed algorithms have superior
effectiveness in comparison with both Bayesian
Decomposition (BD) and our previous FastNCA approach,
while the computational complexity is still orders of
magnitude less than BD.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Badaloni:2012:QRB,
author = "Silvana Badaloni and Barbara {Di Camillo} and
Francesco Sambo",
title = "Qualitative Reasoning for Biological Network Inference
from Systematic Perturbation Experiments",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1482--1491",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.69",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The systematic perturbation of the components of a
biological system has been proven among the most
informative experimental setups for the identification
of causal relations between the components. In this
paper, we present Systematic Perturbation-Qualitative
Reasoning (SPQR), a novel Qualitative Reasoning
approach to automate the interpretation of the results
of systematic perturbation experiments. Our method is
based on a qualitative abstraction of the experimental
data: for each perturbation experiment, measured values
of the observed variables are modeled as lower, equal
or higher than the measurements in the wild type
condition, when no perturbation is applied. The
algorithm exploits a set of IF-THEN rules to infer
causal relations between the variables, analyzing the
patterns of propagation of the perturbation signals
through the biological network, and is specifically
designed to minimize the rate of false positives among
the inferred relations. Tested on both simulated and
real perturbation data, SPQR indeed exhibits a
significantly higher precision than the state of the
art.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hawkins:2012:RFP,
author = "John C. Hawkins and Hongbo Zhu and Joan Teyra and M.
Teresa Pisabarro",
title = "Reduced False Positives in {PDZ} Binding Prediction
Using Sequence and Structural Descriptors",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1492--1503",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.54",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Identifying the binding partners of proteins is a
problem of fundamental importance in computational
biology. The PDZ is one of the most common and
well-studied protein binding domains, hence it is a
perfect model system for designing protein binding
predictors. The standard approach to identifying the
binding partners of PDZ domains uses multiple sequence
alignments to infer the set of contact residues that
are used in a predictive model. We expand on the
sequence alignment approach by incorporating structural
information to generate descriptors of the binding site
geometry. Furthermore, we generate a real-value score
for binary predictions by applying a filter based on
models that predict the probability distributions of
contact residues at each of the canonical PDZ ligand
binding positions. Under training cross validation, our
model produced an order of magnitude more predictions
at a false positive proportion (FPP) of 10 percent than
our benchmark model chosen from the literature.
Evaluated using an independent cross validation, with
computationally predicted structures, our model was
able to make five times as many predictions as the
benchmark model, with a Matthews' correlation
coefficient (MCC) of 0.33. In addition, our model
achieved a false positive proportion of 0.14, while the
benchmark model had a 0.25 false positive proportion.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sun:2012:RBC,
author = "Jianyong Sun and Jonathan M. Garibaldi and Kim
Kenobi",
title = "Robust {Bayesian} Clustering for Replicated Gene
Expression Data",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1504--1514",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.85",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Experimental scientific data sets, especially biology
data, usually contain replicated measurements. The
replicated measurements for the same object are
correlated, and this correlation must be carefully
dealt with in scientific analysis. In this paper, we
propose a robust Bayesian mixture model for clustering
data sets with replicated measurements. The model aims
not only to accurately cluster the data points taking
the replicated measurements into consideration, but
also to find the outliers (i.e., scattered objects)
which are possibly required to be studied further. A
tree-structured variational Bayes (VB) algorithm is
developed to carry out model fitting. Experimental
studies showed that our model compares favorably with
the infinite Gaussian mixture model, while maintaining
computational simplicity. We demonstrate the benefits
of including the replicated measurements in the model,
in terms of improved outlier detection rates in varying
measurement uncertainty conditions. Finally, we apply
the approach to clustering biological transcriptomics
mRNA expression data sets with replicated
measurements.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2012:SID,
author = "Zhi-Zhong Chen and Fei Deng and Lusheng Wang",
title = "Simultaneous Identification of Duplications, Losses,
and Lateral Gene Transfers",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1515--1528",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.79",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We give a fixed-parameter algorithm for the problem of
enumerating all minimum-cost LCA-reconciliations
involving gene duplications, gene losses, and lateral
gene transfers (LGTs) for a given species tree $S$ and
a given gene tree $G$. Our algorithm can work for the
weighted version of the problem, where the costs of a
gene duplication, a gene loss, and an LGT are left to
the user's discretion. The algorithm runs in $ O(m +
3^{k / c} n) $ time, where $m$ is the number of
vertices in $S$, $n$ is the number of vertices in $G$,
$c$ is the smaller between a gene duplication cost and
an LGT cost, and $k$ is the minimum cost of an
LCA-reconciliation between $S$ and $G$. The time
complexity is indeed better if the cost of a gene loss
is greater than 0. In particular, when the cost of a
gene loss is at least 0.614c, the running time of the
algorithm is $ O(m + 2.78^{k / c} n) $.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liao:2012:NMS,
author = "Bo Liao and Xiong Li and Wen Zhu and Zhi Cao",
title = "A Novel Method to Select Informative {SNPs} and Their
Application in Genetic Association Studies",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1529--1534",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.70",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The association studies between complex diseases and
single nucleotide polymorphisms (SNPs) or haplotypes
have recently received great attention. However, these
studies are limited by the cost of genotyping all SNPs.
Therefore, it is essential to find a small subset of
tag SNPs representing the rest of the SNPs. The
presence of linkage disequilibrium between tag SNPs and
the disease variant (genotyped or not), may allow fine
mapping study. In this paper, we combine a
nearest-means classifier (NMC) and ant colony algorithm
to select tags. Results show that our method (ACO/NMC)
can get a similar prediction accuracy with method
BPSO/SVM and is better than BPSO/STAMPA for small data
sets. For large data sets, although the prediction
accuracy of our method is lower than BPSO/SVM, ACO/ NMC
can reach a high accuracy ($ > 99 $ percent) in a
relatively short time. when the number of tags
increases, the time complexity of NMC is nearly linear
growth. To find out that the ability of tags to locate
disease locus, we simulate a case-control study and use
two-locus haplotype analysis to quantitatively assess
the power. The result showed that 20 percent of all
SNPs selected by NMC have about 10 percent higher power
than random tags, on average.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Joseph:2012:CPT,
author = "Shaini Joseph and Shreyas Karnik and Pravin Nilawe and
V. K. Jayaraman and Susan Idicula-Thomas",
title = "{ClassAMP}: a Prediction Tool for Classification of
Antimicrobial Peptides",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1535--1538",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.89",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Antimicrobial peptides (AMPs) are gaining popularity
as anti-infective agents. Information on sequence
features that contribute to target specificity of AMPs
will aid in accelerating drug discovery programs
involving them. In this study, an algorithm called
ClassAMP using Random Forests (RFs) and Support Vector
Machines (SVMs) has been developed to predict the
propensity of a protein sequence to have antibacterial,
antifungal, or antiviral activity. ClassAMP is
available at
\path=http://www.bicnirrh.res.in/classamp/=.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nounou:2012:MDB,
author = "M. N. Nounou and H. N. Nounou and N. Meskin and A.
Datta and E. R. Dougherty",
title = "Multiscale Denoising of Biological Data: a Comparative
Analysis",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1539--1545",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.67",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Measured microarray genomic and metabolic data are a
rich source of information about the biological systems
they represent. For example, time-series biological
data can be used to construct dynamic genetic
regulatory network models, which can be used to design
intervention strategies to cure or manage major
diseases. Also, copy number data can be used to
determine the locations and extent of aberrations in
chromosome sequences. Unfortunately, measured
biological data are usually contaminated with errors
that mask the important features in the data.
Therefore, these noisy measurements need to be filtered
to enhance their usefulness in practice. Wavelet-based
multiscale filtering has been shown to be a powerful
denoising tool. In this work, different batch as well
as online multiscale filtering techniques are used to
denoise biological data contaminated with white or
colored noise. The performances of these techniques are
demonstrated and compared to those of some conventional
low-pass filters using two case studies. The first case
study uses simulated dynamic metabolic data, while the
second case study uses real copy number data.
Simulation results show that significant improvement
can be achieved using multiscale filtering over
conventional filtering techniques.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Margaliot:2012:SAR,
author = "Michael Margaliot and Tamir Tuller",
title = "Stability Analysis of the Ribosome Flow Model",
journal = j-TCBB,
volume = "9",
number = "5",
pages = "1545--1552",
month = sep,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.88",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Aug 28 17:31:04 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gene translation is a central process in all living
organisms. Developing a better understanding of this
complex process may have ramifications to almost every
biomedical discipline. Recently, Reuveni et al.
proposed a new computational model of this process
called the ribosome flow model (RFM). In this study, we
show that the dynamical behavior of the RFM is
relatively simple. There exists a unique equilibrium
point $e$ and every trajectory converges to $e$.
Furthermore, convergence is monotone in the sense that
the distance to $e$ can never increase. This
qualitative behavior is maintained for any feasible set
of parameter values, suggesting that the RFM is highly
robust. Our analysis is based on a contraction
principle and the theory of monotone dynamical systems.
These analysis tools may prove useful in studying other
properties of the RFM as well as additional
intracellular biological processes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sagot:2012:EE,
author = "Marie-France Sagot",
title = "{EIC} Editorial",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1553--1557",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.155",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Degnan:2012:CSS,
author = "James H. Degnan and Noah A. Rosenberg and Tanja
Stadler",
title = "A Characterization of the Set of Species Trees that
Produce Anomalous Ranked Gene Trees",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1558--1568",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.110",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Ranked gene trees, which consider both the gene tree
topology and the sequence in which gene lineages
separate, can potentially provide a new source of
information for use in modeling genealogies and
performing inference of species trees. Recently, we
have calculated the probability distribution of ranked
gene trees under the standard multispecies coalescent
model for the evolution of gene lineages along the
branches of a fixed species tree, demonstrating the
existence of anomalous ranked gene trees (ARGTs), in
which a ranked gene tree that does not match the ranked
species tree can have greater probability under the
model than the matching ranked gene tree. Here, we
fully characterize the set of unranked species tree
topologies that give rise to ARGTs, showing that this
set contains all species tree topologies with five or
more taxa, with the exceptions of caterpillars and
pseudocaterpillars. The results have implications for
the use of ranked gene trees in phylogenetic
inference.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tang:2012:CEC,
author = "Yang Tang and Zidong Wang and Huijun Gao and Stephen
Swift and Jurgen Kurths",
title = "A Constrained Evolutionary Computation Method for
Detecting Controlling Regions of Cortical Networks",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1569--1581",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.124",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Controlling regions in cortical networks, which serve
as key nodes to control the dynamics of networks to a
desired state, can be detected by minimizing the
eigenratio R and the maximum imaginary part $ \sigma $
of an extended connection matrix. Until now, optimal
selection of the set of controlling regions is still an
open problem and this paper represents the first
attempt to include two measures of controllability into
one unified framework. The detection problem of
controlling regions in cortical networks is converted
into a constrained optimization problem (COP), where
the objective function R is minimized and $ \sigma $ is
regarded as a constraint. Then, the detection of
controlling regions of a weighted and directed complex
network (e.g., a cortical network of a cat), is
thoroughly investigated. The controlling regions of
cortical networks are successfully detected by means of
an improved dynamic hybrid framework (IDyHF). Our
experiments verify that the proposed IDyHF outperforms
two recently developed evolutionary computation methods
in constrained optimization field and some traditional
methods in control theory as well as graph theory.
Based on the IDyHF, the controlling regions are
detected in a microscopic and macroscopic way. Our
results unveil the dependence of controlling regions on
the number of driver nodes $l$ and the constraint $r$.
The controlling regions are largely selected from the
regions with a large in-degree and a small out-degree.
When $ r = + \infty $, there exists a concave shape of
the mean degrees of the driver nodes, i.e., the regions
with a large degree are of great importance to the
control of the networks when $l$ is small and the
regions with a small degree are helpful to control the
networks when $l$ increases. When $ r = 0 $, the mean
degrees of the driver nodes increase as a function of
$l$. We find that controlling $ \sigma $ is becoming
more important in controlling a cortical network with
increasing $l$. The methods and results of detecting
controlling regions in this paper would promote the
coordination and information consensus of various kinds
of real-world complex networks including transportation
networks, genetic regulatory networks, and social
networks, etc.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pirola:2012:FPA,
author = "Yuri Pirola and Gianluca Della Vedova and Stefano
Biffani and Alessandra Stella and Paola Bonizzoni",
title = "A Fast and Practical Approach to Genotype Phasing and
Imputation on a Pedigree with Erroneous and Incomplete
Information",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1582--1594",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.100",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The MINIMUM-RECOMBINANT HAPLOTYPE CONFIGURATION
problem (MRHC) has been highly successful in providing
a sound combinatorial formulation for the important
problem of genotype phasing on pedigrees. Despite
several algorithmic advances that have improved the
efficiency, its applicability to real data sets has
been limited since it does not take into account some
important phenomena such as mutations, genotyping
errors, and missing data. In this work, we propose the
MINIMUM-RECOMBINANT HAPLOTYPE CONFIGURATION WITH
BOUNDED ERRORS problem (MRHCE), which extends the
original MRHC formulation by incorporating the two most
common characteristics of real data: errors and missing
genotypes (including untyped individuals). We describe
a practical algorithm for MRHCE that is based on a
reduction to the well-known Satisfiability problem
(SAT) and exploits recent advances in the constraint
programming literature. An experimental analysis
demonstrates the biological soundness of the phasing
model and the effectiveness (on both accuracy and
performance) of the algorithm under several scenarios.
The analysis on real data and the comparison with
state-of-the-art programs reveals that our approach
couples better scalability to large and complex
pedigrees with the explicit inclusion of genotyping
errors into the model.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kazmi:2012:HCA,
author = "N. Kazmi and M. A. Hossain and R. M. Phillips",
title = "A Hybrid Cellular Automaton Model of Solid Tumor
Growth and Bioreductive Drug Transport",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1595--1606",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.118",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Bioreductive drugs are a class of hypoxia selective
drugs that are designed to eradicate the hypoxic
fraction of solid tumors. Their activity depends upon a
number of biological and pharmacological factors and we
used a mathematical modeling approach to explore the
dynamics of tumor growth, infusion, and penetration of
the bioreductive drug Tirapazamine (TPZ). An in-silico
model is implemented to calculate the tumor mass
considering oxygen and glucose as key
microenvironmental parameters. The next stage of the
model integrated extra cellular matrix (ECM), cell-cell
adhesion, and cell movement parameters as growth
constraints. The tumor microenvironments strongly
influenced tumor morphology and growth rates. Once the
growth model was established, a hybrid model was
developed to study drug dynamics inside the hypoxic
regions of tumors. The model used 10, 50 and 100 $ \mu
${\rm M} as TPZ initial concentrations and determined
TPZ pharmacokinetic (PK) (transport) and
pharmacodynamics (cytotoxicity) properties inside
hypoxic regions of solid tumor. The model results
showed that diminished drug transport is a reason for
TPZ failure and recommend the optimization of the drug
transport properties in the emerging TPZ generations.
The modeling approach used in this study is novel and
can be a step to explore the behavioral dynamics of
TPZ.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Abate:2012:MMS,
author = "Alessandro Abate and Stephane Vincent and Roel Dobbe
and Alberto Silletti and Neal Master and Jeffrey D.
Axelrod and Claire J. Tomlin",
title = "A Mathematical Model to Study the Dynamics of
Epithelial Cellular Networks",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1607--1620",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.126",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Epithelia are sheets of connected cells that are
essential across the animal kingdom. Experimental
observations suggest that the dynamical behavior of
many single-layered epithelial tissues has strong
analogies with that of specific mechanical systems,
namely large networks consisting of point masses
connected through spring-damper elements and undergoing
the influence of active and dissipating forces. Based
on this analogy, this work develops a modeling
framework to enable the study of the mechanical
properties and of the dynamic behavior of large
epithelial cellular networks. The model is built first
by creating a network topology that is extracted from
the actual cellular geometry as obtained from
experiments, then by associating a mechanical structure
and dynamics to the network via spring-damper elements.
This scalable approach enables running simulations of
large network dynamics: the derived modeling framework
in particular is predisposed to be tailored to study
general dynamics (for example, morphogenesis) of
various classes of single-layered epithelial cellular
networks. In this contribution, we test the model on a
case study of the dorsal epithelium of the Drosophila
melanogaster embryo during early dorsal closure (and,
less conspicuously, germband retraction).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cavuslar:2012:TSA,
author = "Gizem Cavuslar and Bulent Catay and Mehmet Serkan
Apaydin",
title = "A Tabu Search Approach for the {NMR} Protein
Structure-Based Assignment Problem",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1621--1628",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.122",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ma:2012:EAA,
author = "Christopher Ma and Thomas K. F. Wong and T. W. Lam and
W. K. Hon and K. Sadakane and S. M. Yiu",
title = "An Efficient Alignment Algorithm for Searching Simple
Pseudoknots over Long Genomic Sequence",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1629--1638",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.104",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Structural alignment has been shown to be an effective
computational method to identify structural noncoding
RNA (ncRNA) candidates as ncRNAs are known to be
conserved in secondary structures. However, the
complexity of the structural alignment algorithms
becomes higher when the structure has pseudoknots. Even
for the simplest type of pseudoknots (simple
pseudoknots), the fastest algorithm runs in $ O(m n^3)
$ time, where $m$, $n$ are the length of the query
ncRNA (with known structure) and the length of the
target sequence (with unknown structure), respectively.
In practice, we are usually given a long DNA sequence
and we try to locate regions in the sequence for
possible candidates of a particular ncRNA. Thus, we
need to run the structural alignment algorithm on every
possible region in the long sequence. For example,
finding candidates for a known ncRNA of length 100 on a
sequence of length 50,000, it takes more than one day.
In this paper, we provide an efficient algorithm to
solve the problem for simple pseudoknots and it is
shown to be 10 times faster. The speedup stems from an
effective pruning strategy consisting of the
computation of a lower bound score for the optimal
alignment and an estimation of the maximum score that a
candidate can achieve to decide whether to prune the
current candidate or not.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ozyurt:2012:AIC,
author = "I. Burak Ozyurt",
title = "Automatic Identification and Classification of Noun
Argument Structures in Biomedical Literature",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1639--1648",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.111",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The accelerating increase in the biomedical literature
makes keeping up with recent advances challenging for
researchers thus making automatic extraction and
discovery of knowledge from this vast literature a
necessity. Building such systems requires automatic
detection of lexico-semantic event structures governed
by the syntactic and semantic constraints of human
languages in sentences of biomedical texts. The
lexico-semantic event structures in sentences are
centered around the predicates and most semantic role
labeling (SRL) approaches focus only on the arguments
of verb predicates and neglect argument taking nouns
which also convey information in a sentence. In this
article, a noun argument structure (NAS) annotated
corpus named BioNom and a SRL system to identify and
classify these structures is introduced. Also, a
genetic algorithm-based feature selection (GAFS) method
is introduced and global inference is applied to
significantly improve the performance of the NAS Bio
SRL system.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wu:2012:BIC,
author = "Meng-Yun Wu and Dao-Qing Dai and Yu Shi and Hong Yan
and Xiao-Fei Zhang",
title = "Biomarker Identification and Cancer Classification
Based on Microarray Data Using {Laplace} Naive {Bayes}
Model with Mean Shrinkage",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1649--1662",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.105",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Biomarker identification and cancer classification are
two closely related problems. In gene expression data
sets, the correlation between genes can be high when
they share the same biological pathway. Moreover, the
gene expression data sets may contain outliers due to
either chemical or electrical reasons. A good gene
selection method should take group effects into account
and be robust to outliers. In this paper, we propose a
Laplace naive Bayes model with mean shrinkage (LNB-MS).
The Laplace distribution instead of the normal
distribution is used as the conditional distribution of
the samples for the reasons that it is less sensitive
to outliers and has been applied in many fields. The
key technique is the $ L_1 $ penalty imposed on the
mean of each class to achieve automatic feature
selection. The objective function of the proposed model
is a piecewise linear function with respect to the mean
of each class, of which the optimal value can be
evaluated at the breakpoints simply. An efficient
algorithm is designed to estimate the parameters in the
model. A new strategy that uses the number of selected
features to control the regularization parameter is
introduced. Experimental results on simulated data sets
and 17 publicly available cancer data sets attest to
the accuracy, sparsity, efficiency, and robustness of
the proposed algorithm. Many biomarkers identified with
our method have been verified in biochemical or
biomedical research. The analysis of biological and
functional correlation of the genes based on Gene
Ontology (GO) terms shows that the proposed method
guarantees the selection of highly correlated genes
simultaneously.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Irsoy:2012:DAC,
author = "Ozan Irsoy and Olcay Taner Yildiz and Ethem Alpaydin",
title = "Design and Analysis of Classifier Learning Experiments
in Bioinformatics: Survey and Case Studies",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1663--1675",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.117",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In many bioinformatics applications, it is important
to assess and compare the performances of algorithms
trained from data, to be able to draw conclusions
unaffected by chance and are therefore significant.
Both the design of such experiments and the analysis of
the resulting data using statistical tests should be
done carefully for the results to carry significance.
In this paper, we first review the performance measures
used in classification, the basics of experiment design
and statistical tests. We then give the results of our
survey over 1,500 papers published in the last two
years in three bioinformatics journals (including this
one). Although the basics of experiment design are well
understood, such as resampling instead of using a
single training set and the use of different
performance metrics instead of error, only 21 percent
of the papers use any statistical test for comparison.
In the third part, we analyze four different scenarios
which we encounter frequently in the bioinformatics
literature, discussing the proper statistical
methodology as well as showing an example case study
for each. With the supplementary software, we hope that
the guidelines we discuss will play an important role
in future studies.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ashlock:2012:DER,
author = "Wendy Ashlock and Suprakash Datta",
title = "Distinguishing Endogenous Retroviral {LTRs} from
{SINE} Elements Using Features Extracted from Evolved
Side Effect Machines",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1676--1689",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.116",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Side effect machines produce features for classifiers
that distinguish different types of DNA sequences. They
have the, as yet unexploited, potential to give insight
into biological features of the sequences. We introduce
several innovations to the production and use of side
effect machine sequence features. We compare the
results of using consensus sequences and genomic
sequences for training classifiers and find that more
accurate results can be obtained using genomic
sequences. Surprisingly, we were even able to build a
classifier that distinguished consensus sequences from
genomic sequences with high accuracy, suggesting that
consensus sequences are not always representative of
their genomic counterparts. We apply our techniques to
the problem of distinguishing two types of transposable
elements, solo LTRs and SINEs. Identifying these
sequences is important because they affect gene
expression, genome structure, and genetic diversity,
and they serve as genetic markers. They are of similar
length, neither codes for protein, and both have many
nearly identical copies throughout the genome. Being
able to efficiently and automatically distinguish them
will aid efforts to improve annotations of genomes. Our
approach reveals structural characteristics of the
sequences of potential interest to biologists.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tsai:2012:IPP,
author = "Richard Tzong-Han Tsai",
title = "Improving Protein-Protein Interaction Pair Ranking
with an Integrated Global Association Score",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1690--1695",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.99",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein-protein interaction (PPI) database curation
requires text-mining systems that can recognize and
normalize interactor genes and return a ranked list of
PPI pairs for each article. The order of PPI pairs in
this list is essential for ease of curation. Most of
the current PPI pair ranking approaches rely on
association analysis between the two genes in the pair.
However, we propose that ranking an extracted PPI pair
by considering both the association between the paired
genes and each of those genes' global associations with
all other genes mentioned in the paper can provide a
more reliable ranked list. In this work, we present a
composite interaction score that considers not only the
association score between two interactors (pair
association score) but also their global association
scores. We test three representative data fusion
algorithms to estimate this global association
score-two Borda-Fuse models and one linear combination
model (LCM). The three estimation methods are evaluated
using the data set of the BioCreative II.5 Interaction
Pair Task (IPT) in terms of area under the interpolated
precision/recall curve (AUC iP/R). Our experimental
results indicate that using LCM to estimate the global
association score can boost the AUC iP/R score from
0.0175 to 0.2396, outperforming the best BioCreative
II.5 IPT system.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hashemikhabir:2012:LSS,
author = "Seyedsasan Hashemikhabir and Eyup Serdar Ayaz and
Yusuf Kavurucu and Tolga Can and Tamer Kahveci",
title = "Large-Scale Signaling Network Reconstruction",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1696--1708",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.128",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Reconstructing the topology of a signaling network by
means of RNA interference (RNAi) technology is an
underdetermined problem especially when a single gene
in the network is knocked down or observed. In
addition, the exponential search space limits the
existing methods to small signaling networks of size
10-15 genes. In this paper, we propose integrating RNAi
data with a reference physical interaction network. We
formulate the problem of signaling network
reconstruction as finding the minimum number of edit
operations on a given reference network. The edit
operations transform the reference network to a network
that satisfies the RNAi observations. We show that
using a reference network does not simplify the
computational complexity of the problem. Therefore, we
propose two methods which provide near optimal results
and can scale well for reconstructing networks up to
hundreds of components. We validate the proposed
methods on synthetic and real data sets. Comparison
with the state of the art on real signaling networks
shows that the proposed methodology can scale better
and generates biologically significant results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sargsyan:2012:MSR,
author = "Khachik Sargsyan and Cosmin Safta and Bert Debusschere
and Habib Najm",
title = "Multiparameter Spectral Representation of
Noise-Induced Competence in \bioname{Bacillus Subtilis}",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1709--1723",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.107",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this work, the problem of representing a stochastic
forward model output with respect to a large number of
input parameters is considered. The methodology is
applied to a stochastic reaction network of competence
dynamics in Bacillus subtilis bacterium. In particular,
the dependence of the competence state on rate
constants of underlying reactions is investigated. We
base our methodology on Polynomial Chaos (PC) spectral
expansions that allow effective propagation of input
parameter uncertainties to outputs of interest. Given a
number of forward model training runs at sampled input
parameter values, the PC modes are estimated using a
Bayesian framework. As an outcome, these PC modes are
described with posterior probability distributions. The
resulting expansion can be regarded as an uncertain
response function and can further be used as a
computationally inexpensive surrogate instead of the
original reaction model for subsequent analyses such as
calibration or optimization studies. Furthermore, the
methodology is enhanced with a classification-based
mixture PC formulation that overcomes the difficulties
associated with representing potentially nonsmooth
input-output relationships. Finally, the global
sensitivity analysis based on the multiparameter
spectral representation of an observable of interest
provides biological insight and reveals the most
important reactions and their couplings for the
competence dynamics.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Margaliot:2012:SSD,
author = "Michael Margaliot and Tamir Tuller",
title = "On the Steady-State Distribution in the Homogeneous
Ribosome Flow Model",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1724--1736",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.120",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A central biological process in all living organisms
is gene translation. Developing a deeper understanding
of this complex process may have ramifications to
almost every biomedical discipline. Reuveni et al.
recently proposed a new computational model of gene
translation called the Ribosome Flow Model (RFM). In
this paper, we consider a particular case of this
model, called the Homogeneous Ribosome Flow Model
(HRFM). From a biological viewpoint, this corresponds
to the case where the transition rates of all the
coding sequence codons are identical. This regime has
been suggested recently based on experiments in mouse
embryonic cells. We consider the steady-state
distribution of the HRFM. We provide formulas that
relate the different parameters of the model in steady
state. We prove the following properties: (1) the
ribosomal density profile is monotonically decreasing
along the coding sequence; (2) the ribosomal density at
each codon monotonically increases with the initiation
rate; and (3) for a constant initiation rate, the
translation rate monotonically decreases with the
length of the coding sequence. In addition, we analyze
the translation rate of the HRFM at the limit of very
high and very low initiation rate, and provide explicit
formulas for the translation rate in these two cases.
We discuss the relationship between these theoretical
results and biological findings on the translation
process.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Marschall:2012:PAA,
author = "Tobias Marschall and Inke Herms and Hans-Michael
Kaltenbach and Sven Rahmann",
title = "Probabilistic Arithmetic Automata and Their
Applications",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1737--1750",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.109",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present a comprehensive review on probabilistic
arithmetic automata (PAAs), a general model to describe
chains of operations whose operands depend on chance,
along with two algorithms to numerically compute the
distribution of the results of such probabilistic
calculations. PAAs provide a unifying framework to
approach many problems arising in computational biology
and elsewhere. We present five different applications,
namely (1) pattern matching statistics on random texts,
including the computation of the distribution of
occurrence counts, waiting times, and clump sizes under
hidden Markov background models; (2) exact analysis of
window-based pattern matching algorithms; (3)
sensitivity of filtration seeds used to detect
candidate sequence alignments; (4) length and mass
statistics of peptide fragments resulting from
enzymatic cleavage reactions; and (5) read length
statistics of 454 and IonTorrent sequencing reads. The
diversity of these applications indicates the
flexibility and unifying character of the presented
framework. While the construction of a PAA depends on
the particular application, we single out a frequently
applicable construction method: We introduce
deterministic arithmetic automata (DAAs) to model
deterministic calculations on sequences, and
demonstrate how to construct a PAA from a given DAA and
a finite-memory random text model. This procedure is
used for all five discussed applications and greatly
simplifies the construction of PAAs. Implementations
are available as part of the MoSDi package. Its
application programming interface facilitates the rapid
development of new applications based on the PAA
framework.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2012:STS,
author = "Zhiwen Yu and Le Li and Jane You and Hau-San Wong and
Guoqiang Han",
title = "{SC$^3$}: Triple Spectral Clustering-Based Consensus
{Clustering} Framework for Class Discovery from Cancer
Gene Expression Profiles",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1751--1765",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.108",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In order to perform successful diagnosis and treatment
of cancer, discovering, and classifying cancer types
correctly is essential. One of the challenging
properties of class discovery from cancer data sets is
that cancer gene expression profiles not only include a
large number of genes, but also contains a lot of noisy
genes. In order to reduce the effect of noisy genes in
cancer gene expression profiles, we propose two new
consensus clustering frameworks, named as triple
spectral clustering-based consensus clustering (SC$^3$
) and double spectral clustering-based consensus
clustering (SC$^2$Ncut) in this paper, for cancer
discovery from gene expression profiles. SC$^3$
integrates the spectral clustering (SC) algorithm
multiple times into the ensemble framework to process
gene expression profiles. Specifically, spectral
clustering is applied to perform clustering on the gene
dimension and the cancer sample dimension, and also
used as the consensus function to partition the
consensus matrix constructed from multiple clustering
solutions. Compared with SC$^3$, SC$^2$Ncut adopts the
normalized cut algorithm, instead of spectral
clustering, as the consensus function. Experiments on
both synthetic data sets and real cancer gene
expression profiles illustrate that the proposed
approaches not only achieve good performance on gene
expression profiles, but also outperforms most of the
existing approaches in the process of class discovery
from these profiles.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ma:2012:SBP,
author = "Xin Ma and Jing Guo and Hong-De Liu and Jian-Ming Xie
and Xiao Sun",
title = "Sequence-Based Prediction of {DNA}-Binding Residues in
Proteins with Conservation and Correlation
Information",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1766--1775",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.106",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2012:SCM,
author = "Jianxin Wang and Yuannan Huang and Fang-Xiang Wu and
Yi Pan",
title = "Symmetry Compression Method for Discovering Network
Motifs",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1776--1789",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.119",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Discovering network motifs could provide a significant
insight into systems biology. Interestingly, many
biological networks have been found to have a high
degree of symmetry (automorphism), which is inherent in
biological network topologies. The symmetry due to the
large number of basic symmetric subgraphs (BSSs) causes
a certain redundant calculation in discovering network
motifs. Therefore, we compress all basic symmetric
subgraphs before extracting compressed subgraphs and
propose an efficient decompression algorithm to
decompress all compressed subgraphs without loss of any
information. In contrast to previous approaches, the
novel Symmetry Compression method for Motif Detection,
named as SCMD, eliminates most redundant calculations
caused by widespread symmetry of biological networks.
We use SCMD to improve three notable exact algorithms
and two efficient sampling algorithms. Results of all
exact algorithms with SCMD are the same as those of the
original algorithms, since SCMD is a lossless method.
The sampling results show that the use of SCMD almost
does not affect the quality of sampling results. For
highly symmetric networks, we find that SCMD used in
both exact and sampling algorithms can help get a
remarkable speedup. Furthermore, SCMD enables us to
find larger motifs in biological networks with notable
symmetry than previously possible.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Amin:2012:TSG,
author = "Mohammad Shafkat Amin and Russell L. {Finley, Jr.} and
Hasan M. Jamil",
title = "Top-$k$ Similar Graph Matching Using {TraM} in
Biological Networks",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1790--1804",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.90",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Many emerging database applications entail
sophisticated graph-based query manipulation,
predominantly evident in large-scale scientific
applications. To access the information embedded in
graphs, efficient graph matching tools and algorithms
have become of prime importance. Although the
prohibitively expensive time complexity associated with
exact subgraph isomorphism techniques has limited its
efficacy in the application domain, approximate yet
efficient graph matching techniques have received much
attention due to their pragmatic applicability. Since
public domain databases are noisy and incomplete in
nature, inexact graph matching techniques have proven
to be more promising in terms of inferring knowledge
from numerous structural data repositories. In this
paper, we propose a novel technique called TraM for
approximate graph matching that off-loads a significant
amount of its processing on to the database making the
approach viable for large graphs. Moreover, the vector
space embedding of the graphs and efficient filtration
of the search space enables computation of approximate
graph similarity at a throw-away cost. We annotate
nodes of the query graphs by means of their global
topological properties and compare them with
neighborhood biased segments of the data-graph for
proper matches. We have conducted experiments on
several real data sets, and have demonstrated the
effectiveness and efficiency of the proposed method",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dwight:2012:UWB,
author = "Zachary L. Dwight and Robert Palais and Carl T.
Wittwer",
title = "{uAnalyze}: {Web}-Based High-Resolution {DNA} Melting
Analysis with Comparison to Thermodynamic Predictions",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1805--1811",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.112",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Re:2012:FRA,
author = "Matteo Re and Marco Mesiti and Giorgio Valentini",
title = "A Fast Ranking Algorithm for Predicting Gene Functions
in Biomolecular Networks",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1812--1818",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.114",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Ranking genes in functional networks according to a
specific biological function is a challenging task
raising relevant performance and computational
complexity problems. To cope with both these problems
we developed a transductive gene ranking method based
on kernelized score functions able to fully exploit the
topology and the graph structure of biomolecular
networks and to capture significant functional
relationships between genes. We run the method on a
network constructed by integrating multiple
biomolecular data sources in the yeast model organism,
achieving significantly better results than the
compared state-of-the-art network-based algorithms for
gene function prediction, and with relevant savings in
computational time. The proposed approach is general
and fast enough to be in perspective applied to other
relevant node ranking problems in large and complex
biological networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nounou:2012:FIB,
author = "Hazem N. Nounou and Mohamed N. Nounou and Nader Meskin
and Aniruddha Datta and Edward R. Dougherty",
title = "Fuzzy Intervention in Biological Phenomena",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1819--1825",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.113",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "An important objective of modeling biological
phenomena is to develop therapeutic intervention
strategies to move an undesirable state of a diseased
network toward a more desirable one. Such transitions
can be achieved by the use of drugs to act on some
genes/metabolites that affect the undesirable behavior.
Due to the fact that biological phenomena are complex
processes with nonlinear dynamics that are impossible
to perfectly represent with a mathematical model, the
need for model-free nonlinear intervention strategies
that are capable of guiding the target variables to
their desired values often arises. In many
applications, fuzzy systems have been found to be very
useful for parameter estimation, model development and
control design of nonlinear processes. In this paper, a
model-free fuzzy intervention strategy (that does not
require a mathematical model of the biological
phenomenon) is proposed to guide the target variables
of biological systems to their desired values. The
proposed fuzzy intervention strategy is applied to
three different biological models: a
glycolytic-glycogenolytic pathway model, a purine
metabolism pathway model, and a generic pathway model.
The simulation results for all models demonstrate the
effectiveness of the proposed scheme.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Papadakis:2012:SSD,
author = "George Papadakis and Electra Gizeli",
title = "In Silico Search of {DNA} Drugs Targeting Oncogenes",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1826--1830",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.127",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Triplex forming oligonucleotides (TFOs) represent a
class of drug candidates for antigene therapy. Based on
strict criteria, we investigated the potential of 25
known oncogenes to be regulated by TFOs in the mRNA
synthesis level and we report specific target sequences
found in seven of these genes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bicego:2012:ITM,
author = "Manuele Bicego and Pietro Lovato and Alessandro Perina
and Marianna Fasoli and Massimo Delledonne and Mario
Pezzotti and Annalisa Polverari and Vittorio Murino",
title = "Investigating Topic Models' Capabilities in Expression
Microarray Data Classification",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1831--1836",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.121",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In recent years a particular class of probabilistic
graphical models-called topic models-has proven to
represent an useful and interpretable tool for
understanding and mining microarray data. In this
context, such models have been almost only applied in
the clustering scenario, whereas the classification
task has been disregarded by researchers. In this
paper, we thoroughly investigate the use of topic
models for classification of microarray data, starting
from ideas proposed in other fields (e.g., computer
vision). A classification scheme is proposed, based on
highly interpretable features extracted from topic
models, resulting in a hybrid generative-discriminative
approach; an extensive experimental evaluation,
involving 10 different literature benchmarks, confirms
the suitability of the topic models for classifying
expression microarray data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Grassi:2012:KSP,
author = "Elena Grassi and Federico {Di Gregorio} and Ivan
Molineris",
title = "{KungFQ}: a Simple and Powerful Approach to Compress
{{\tt fastq}} Files",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1837--1842",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.123",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Nowadays storing data derived from deep sequencing
experiments has become pivotal and standard compression
algorithms do not exploit in a satisfying manner their
structure. A number of reference-based compression
algorithms have been developed but they are less
adequate when approaching new species without fully
sequenced genomes or nongenomic data. We developed a
tool that takes advantages of {\tt fastq}
characteristics and encodes them in a binary format
optimized in order to be further compressed with
standard tools (such as {\tt gzip} or {\tt lzma}). The
algorithm is straightforward and does not need any
external reference file, it scans the {\tt fastq} only
once and has a constant memory requirement. Moreover,
we added the possibility to perform lossy compression,
losing some of the original information (IDs and/or
qualities) but resulting in smaller files; it is also
possible to define a quality cutoff under which
corresponding base calls are converted to $N$. We
achieve 2.82 to 7.77 compression ratios on various {\tt
fastq} files without losing information and 5.37 to
8.77 losing IDs, which are often not used in common
analysis pipelines. In this paper, we compare the
algorithm performance with known tools, usually
obtaining higher compression levels.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Boucher:2012:HCS,
author = "Christina Boucher and Mohamed Omar",
title = "On the Hardness of Counting and Sampling Center
Strings",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1843--1846",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.84",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Given a set $S$ of $n$ strings, each of length $ \ell
$, and a nonnegative value $d$, we define a center
string as a string of length $ \ell $ that has Hamming
distance at most $d$ from each string in $S$. The
\#{\rm CLOSEST STRING} problem aims to determine the
number of center strings for a given set of strings $S$
and input parameters $n$, $ \ell $, and $d$. We show
\#{\rm CLOSEST STRING} is impossible to solve exactly
or even approximately in polynomial time, and that
restricting \#{\rm CLOSEST STRING} so that any one of
the parameters $n$, $ \ell $, or $d$ is fixed leads to
a fully polynomial-time randomized approximation scheme
(FPRAS). We show equivalent results for the problem of
efficiently sampling center strings uniformly at random
(u.a.r.).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gyorffy:2012:PMR,
author = "Daniel Gyorffy and Peter Zavodszky and Andras
Szilagyi",
title = "``Pull Moves'' for Rectangular Lattice Polymer Models
Are Not Fully Reversible",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1847--1849",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.129",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "``Pull moves'' is a popular move set for lattice
polymer model simulations. We show that the proof given
for its reversibility earlier is flawed, and some moves
are irreversible, which leads to biases in the
parameters estimated from the simulations. We show how
to make the move set fully reversible.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Campello:2012:SMA,
author = "Ricardo J. G. B. Campello and Davoud Moulavi and Joerg
Sander",
title = "A Simpler and More Accurate {AUTO--HDS} Framework for
Clustering and Visualization of Biological Data",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1850--1852",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.115",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In [CHECK END OF SENTENCE], the authors proposed a
framework for automated clustering and visualization of
biological data sets named AUTO-HDS. This letter is
intended to complement that framework by showing that
it is possible to get rid of a user-defined parameter
in a way that the clustering stage can be implemented
more accurately while having reduced computational
complexity",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Handoko:2012:EQA,
author = "Stephanus Daniel Handoko and Xuchang Ouyang and Chinh
Tran To Su and Chee Keong Kwoh and Yew Soon Ong",
title = "Erratum to {``QuickVina: Accelerating AutoDock Vina
Using Gradient-Based Heuristics for Global
Optimization''}",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1853",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.156",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2012:CPS,
author = "Anonymous",
title = "Call for Papers: Special Issue on `-Omics' Based
Companion Diagnostics for Personalized Medicine",
journal = j-TCBB,
volume = "9",
number = "6",
pages = "1855--1855",
month = nov,
year = "2012",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.150",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 19 17:33:56 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Prospective authors are requested to submit new,
unpublished manuscripts for inclusion in the upcoming
event described in this call for papers.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2013:ENE,
author = "Ying Xu",
title = "Editorial from the New {Editor-in--Chief}",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "1--1",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.56",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ray:2013:RSS,
author = "Shubhra Sankar Ray and Sankar K. Pal",
title = "{RNA} Secondary Structure Prediction Using Soft
Computing",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "2--17",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.159",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Prediction of RNA structure is invaluable in creating
new drugs and understanding genetic diseases. Several
deterministic algorithms and soft computing-based
techniques have been developed for more than a decade
to determine the structure from a known RNA sequence.
Soft computing gained importance with the need to get
approximate solutions for RNA sequences by considering
the issues related with kinetic effects,
cotranscriptional folding, and estimation of certain
energy parameters. A brief description of some of the
soft computing-based techniques, developed for RNA
secondary structure prediction, is presented along with
their relevance. The basic concepts of RNA and its
different structural elements like helix, bulge,
hairpin loop, internal loop, and multiloop are
described. These are followed by different
methodologies, employing genetic algorithms, artificial
neural networks, and fuzzy logic. The role of various
metaheuristics, like simulated annealing, particle
swarm optimization, ant colony optimization, and tabu
search is also discussed. A relative comparison among
different techniques, in predicting 12 known RNA
secondary structures, is presented, as an example.
Future challenging issues are then mentioned.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Piovesan:2013:SFP,
author = "Teresa Piovesan and Steven Kelk",
title = "A Simple Fixed Parameter Tractable Algorithm for
Computing the Hybridization Number of Two (Not
Necessarily Binary) Trees",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "18--25",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.134",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Here, we present a new fixed parameter tractable
algorithm to compute the hybridization number $ (r) $
of two rooted, not necessarily binary phylogenetic
trees on taxon set {$ ({{\cal X}}) $} in time $ ((6^r
r!) \cdot p o l y(n)) $, where {$ (n = \vert {{\cal X}}
\vert) $}. The novelty of this approach is its use of
terminals, which are maximal elements of a natural
partial order on {$ ({{\cal X}}) $}, and several
insights from the softwired clusters literature. This
yields a surprisingly simple and practical
bounded-search algorithm and offers an alternative
perspective on the underlying combinatorial structure
of the hybridization number problem.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wohlers:2013:DOD,
author = "Inken Wohlers and Rumen Andonov and Gunnar W. Klau",
title = "{DALIX}: Optimal {DALI} Protein Structure Alignment",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "26--36",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.143",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present a mathematical model and exact algorithm
for optimally aligning protein structures using the
dali scoring model. This scoring model is based on
comparing the interresidue distance matrices of
proteins and is used in the popular dali software tool,
a heuristic method for protein structure alignment. Our
model and algorithm extend an integer linear
programming approach that has been previously applied
for the related, but simpler, contact map overlap
problem. To this end, we introduce a novel type of
constraint that handles negative score values and relax
it in a Lagrangian fashion. The new algorithm, which we
call dalix, is applicable to any distance matrix-based
scoring scheme. We also review options that allow to
consider fewer pairs of interresidue distances
explicitly because their large number hinders the
optimization process. Using four known data sets of
varying structural similarity, we compute many provably
score-optimal dali alignments. This allowed, for the
first time, to evaluate the dali heuristic in sound
mathematical terms. The results indicate that dali
usually computes optimal or close to optimal
alignments. However, we detect a subset of small
proteins for which dali fails to generate any
significant alignment, although such alignments do
exist.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Higa:2013:GSG,
author = "Carlos H. A. Higa and Tales P. Andrade and Ronaldo
Fumio Hashimoto",
title = "Growing Seed Genes from Time Series Data and
Thresholded {Boolean} Networks with Perturbation",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "37--49",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.169",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Models of gene regulatory networks (GRN) have been
proposed along with algorithms for inferring their
structure. By structure, we mean the relationships
among the genes of the biological system under study.
Despite the large number of genes found in the genome
of an organism, it is believed that a small set of
genes is responsible for maintaining a specific core
regulatory mechanism (small subnetworks). We propose an
algorithm for inference of subnetworks of genes from a
small initial set of genes called seed and time series
gene expression data. The algorithm has two main steps:
First, it grows the seed of genes by adding genes to
it, and second, it searches for subnetworks that can be
biologically meaningful. The seed growing step is
treated as a feature selection problem and we used a
thresholded Boolean network with a perturbation model
to design the criterion function that is used to select
the features (genes). Given that the reverse
engineering of GRN is a problem that does not
necessarily have one unique solution, the proposed
algorithm has as output a set of networks instead of
one single network. The algorithm also analyzes the
dynamics of the networks which can be time-consuming.
Nevertheless, the algorithm is suitable when the number
of genes is small. The results showed that the
algorithm is capable of recovering an acceptable rate
of gene interactions and to generate regulatory
hypotheses that can be explored in the wet lab.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Muraro:2013:IGN,
author = "Daniele Muraro and Ute Vob and Michael Wilson and
Malcolm Bennett and Helen Byrne and Ive {De Smet} and
Charlie Hodgman and John King",
title = "Inference of the Genetic Network Regulating Lateral
Root Initiation in \bioname{Arabidopsis thaliana}",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "50--60",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.3",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Regulation of gene expression is crucial for organism
growth, and it is one of the challenges in systems
biology to reconstruct the underlying regulatory
biological networks from transcriptomic data. The
formation of lateral roots in Arabidopsis thaliana is
stimulated by a cascade of regulators of which only the
interactions of its initial elements have been
identified. Using simulated gene expression data with
known network topology, we compare the performance of
inference algorithms, based on different approaches,
for which ready-to-use software is available. We show
that their performance improves with the network size
and the inclusion of mutants. We then analyze two sets
of genes, whose activity is likely to be relevant to
lateral root initiation in Arabidopsis, and assess
causality of their regulatory interactions by
integrating sequence analysis with the intersection of
the results of the best performing methods on time
series and mutants. The methods applied capture known
interactions between genes that are candidate
regulators at early stages of development. The network
inferred from genes significantly expressed during
lateral root formation exhibits distinct scale free,
small world and hierarchical properties and the nodes
with a high out-degree may warrant further
investigation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Than:2013:MPD,
author = "Cuong V. Than and Noah A. Rosenberg",
title = "Mathematical Properties of the Deep Coalescence Cost",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "61--72",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.133",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In the minimizing-deep-coalescences (MDC) approach for
species tree inference, a tree that has the minimal
deep coalescence cost for reconciling a collection of
gene trees is taken as an estimate of the species tree
topology. The MDC method possesses the desirable Pareto
property, and in practice it is quite accurate and
computationally efficient. Here, in order to better
understand the MDC method, we investigate some
properties of the deep coalescence cost. We prove that
the unit neighborhood of either a rooted species tree
or a rooted gene tree under the deep coalescence cost
is exactly the same as the tree's unit neighborhood
under the rooted nearest-neighbor interchange (NNI)
distance. Next, for a fixed species tree, we obtain the
maximum deep coalescence cost across all gene trees as
well as the number of gene trees that achieve the
maximum cost. We also study corresponding problems for
a fixed gene tree.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Boyen:2013:MMM,
author = "Peter Boyen and Frank Neven and Dries {Van Dyck} and
Felipe Valentim and Aalt van Dijk",
title = "Mining Minimal Motif Pair Sets Maximally Covering
Interactions in a Protein-Protein Interaction Network",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "73--86",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.165",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Correlated motif covering (CMC) is the problem of
finding a set of motif pairs, i.e., pairs of patterns,
in the sequences of proteins from a protein-protein
interaction network (PPI-network) that describe the
interactions in the network as concisely as possible.
In other words, a perfect solution for CMC would be a
minimal set of motif pairs that describes the
interaction behavior perfectly in the sense that two
proteins from the network interact if and only if their
sequences match a motif pair in the minimal set. In
this paper, we introduce and formally define CMC and
show that it is closely related to the red-blue set
cover (RBSC) problem and its weighted version
(WRBSC)-both well-known NP-hard problems for that there
exist several algorithms with known approximation
factor guarantees. We prove the hardness of
approximation of CMC by providing an approximation
factor preserving reduction from RBSC to CMC. We show
the existence of a theoretical approximation algorithm
for CMC by providing an approximation factor preserving
reduction from CMC to WRBSC. We adapt the latter
algorithm into a functional heuristic for CMC, called
CMC-approx, and experimentally assess its performance
and biological relevance. The implementation in Java
can be found at {\tt
http://bioinformatics.uhasselt.be}.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rajapakse:2013:MGS,
author = "Jagath C. Rajapakse and Piyushkumar A. Mundra",
title = "Multiclass Gene Selection Using {Pareto}-Fronts",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "87--97",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.1",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Filter methods are often used for selection of genes
in multiclass sample classification by using microarray
data. Such techniques usually tend to bias toward a few
classes that are easily distinguishable from other
classes due to imbalances of strong features and sample
sizes of different classes. It could therefore lead to
selection of redundant genes while missing the relevant
genes, leading to poor classification of tissue
samples. In this manuscript, we propose to decompose
multiclass ranking statistics into class-specific
statistics and then use Pareto-front analysis for
selection of genes. This alleviates the bias induced by
class intrinsic characteristics of dominating classes.
The use of Pareto-front analysis is demonstrated on two
filter criteria commonly used for gene selection:
F-score and KW-score. A significant improvement in
classification performance and reduction in redundancy
among top-ranked genes were achieved in experiments
with both synthetic and real-benchmark data sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yang:2013:PEL,
author = "Ye Yang and Farnoosh A. Aghababazadeh and David R.
Bickel",
title = "Parametric Estimation of the Local False Discovery
Rate for Identifying Genetic Associations",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "98--108",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.140",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Many genome-wide association studies have been
conducted to identify single nucleotide polymorphisms
(SNPs) that are associated with particular diseases or
other traits. The local false discovery rate (LFDR)
estimated using semiparametric models has enjoyed
success in simultaneous inference. However,
semiparametric LFDR estimators can be biased because
they tend to overestimate the proportion of the
nonassociated SNPs. We address the problem by adapting
a simple parametric mixture model (PMM) and by
comparing this model to the semiparametric mixture
model (SMM) behind an LFDR estimator that is known to
be conservatively biased. Then, we also compare the PMM
with a parametric nonmixture model (PNM). In our
simulation studies, we thoroughly analyze the
performances of the three models under different values
of $ (p_1) $, a prior probability that is approximately
equal to the proportion of SNPs that are associated
with the disease. When $ (p_1 > 10 \%) $, the PMM
generally performs better than the SMM. When $ (p_1 <
0.1 \%) $, the SMM outperforms PMM. When $ (p_1) $ lies
between 0.1 and 10 percent, both methods have about the
same performance. In that setting, the PMM may be
preferred since it has the advantage of supplying an
estimate of the detectability level of the
nonassociated SNPs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Todor:2013:PBN,
author = "Andrei Todor and Alin Dobra and Tamer Kahveci",
title = "Probabilistic Biological Network Alignment",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "109--121",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.142",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Interactions between molecules are probabilistic
events. An interaction may or may not happen with some
probability, depending on a variety of factors such as
the size, abundance, or proximity of the interacting
molecules. In this paper, we consider the problem of
aligning two biological networks. Unlike existing
methods, we allow one of the two networks to contain
probabilistic interactions. Allowing interaction
probabilities makes the alignment more biologically
relevant at the expense of explosive growth in the
number of alternative topologies that may arise from
different subsets of interactions that take place. We
develop a novel method that efficiently and precisely
characterizes this massive search space. We represent
the topological similarity between pairs of aligned
molecules (i.e., proteins) with the help of random
variables and compute their expected values. We
validate our method showing that, without sacrificing
the running time performance, it can produce novel
alignments. Our results also demonstrate that our
method identifies biologically meaningful mappings
under a comprehensive set of criteria used in the
literature as well as the statistical coherence measure
that we developed to analyze the statistical
significance of the similarity of the functions of the
aligned protein pairs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Biller:2013:RBP,
author = "Priscila Biller and Pedro Feijao and Joao Meidanis",
title = "Rearrangement-Based Phylogeny Using the
Single-Cut-or-Join Operation",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "122--134",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.168",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recently, the Single-Cut-or-Join (SCJ) operation was
proposed as a basis for a new rearrangement distance
between multichromosomal genomes, leading to very fast
algorithms, both in theory and in practice. However, it
was not clear how well this new distance fares when it
comes to using it to solve relevant problems, such as
the reconstruction of evolutionary history. In this
paper, we advance current knowledge, by testing SCJ's
ability regarding evolutionary reconstruction in two
aspects: (1) How well does SCJ reconstruct evolutionary
topologies? and (2) How well does SCJ reconstruct
ancestral genomes? In the process of answering these
questions, we implemented SCJ-based methods, and made
them available to the community. We ran experiments
using as many as 200 genomes, with as many as 3,000
genes. For the first question, we found out that SCJ
can recover typically between 60 percent and more than
95 percent of the topology, as measured through the
Robinson--Foulds distance (a.k.a. split distance)
between trees. In other words, 60 percent to more than
95 percent of the original splits are also present in
the reconstructed tree. For the second question, given
a topology, SCJ's ability to reconstruct ancestral
genomes depends on how far from the leaves the
ancestral is. For nodes close to the leaves, about 85
percent of the gene adjacencies can be recovered. This
percentage decreases as we move up the tree, but, even
at the root, about 50 percent of the adjacencies are
recovered, for as many as 64 leaves. Our findings
corroborate the fact that SCJ leads to very
conservative genome reconstructions, yielding very few
false-positive gene adjacencies in the ancestrals, at
the expense of a relatively larger amount of false
negatives. In addition, experiments with real data from
the Campanulaceae and Protostomes groups show that SCJ
reconstructs topologies of quality comparable to the
accepted trees of the species involved. As far as time
is concerned, the methods we implemented can find a
topology for 64 genomes with 2,000 genes each in about
10.7 minutes, and reconstruct the ancestral genomes in
a 64-leaf tree in about 3 seconds, both on a typical
desktop computer. It should be noted that our code is
written in Java and we made no significant effort to
optimize it.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Axenopoulos:2013:SDP,
author = "Apostolos Axenopoulos and Petros Daras and Georgios E.
Papadopoulos and Elias Houstis",
title = "{SP-Dock}: Protein-Protein Docking Using Shape and
Physicochemical Complementarity",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "135--150",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.149",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, a framework for protein-protein docking
is proposed, which exploits both shape and
physicochemical complementarity to generate improved
docking predictions. Shape complementarity is achieved
by matching local surface patches. However, unlike
existing approaches, which are based on single-patch or
two-patch matching, we developed a new algorithm that
compares simultaneously, groups of neighboring patches
from the receptor with groups of neighboring patches
from the ligand. Taking into account the fact that
shape complementarity in protein surfaces is mostly
approximate rather than exact, the proposed group-based
matching algorithm fits perfectly to the nature of
protein surfaces. This is demonstrated by the high
performance that our method achieves especially in the
case where the unbound structures of the proteins are
considered. Additionally, several physicochemical
factors, such as desolvation energy, electrostatic
complementarity (EC), hydrophobicity (HP), Coulomb
potential (CP), and Lennard-Jones potential are
integrated using an optimized scoring function,
improving geometric ranking in more than 60 percent of
the complexes of Docking Benchmark 2.4.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Grunewald:2013:SCS,
author = "Stefan Grunewald and Andreas Spillner and Sarah
Bastkowski and Anja Bogershausen and Vincent Moulton",
title = "{SuperQ}: Computing Supernetworks from Quartets",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "151--160",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.8",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Supertrees are a commonly used tool in phylogenetics
to summarize collections of partial phylogenetic trees.
As a generalization of supertrees, phylogenetic
supernetworks allow, in addition, the visual
representation of conflict between the trees that is
not possible to observe with a single tree. Here, we
introduce SuperQ, a new method for constructing such
supernetworks (SuperQ is freely available at
http://www.uea.ac.uk/computing/superq). It works by
first breaking the input trees into quartet trees, and
then stitching these together to form a special kind of
phylogenetic network, called a split network. This
stitching process is performed using an adaptation of
the QNet method for split network reconstruction
employing a novel approach to use the branch lengths
from the input trees to estimate the branch lengths in
the resulting network. Compared with previous
supernetwork methods, SuperQ has the advantage of
producing a planar network. We compare the performance
of SuperQ to the Z-closure and Q-imputation
supernetwork methods, and also present an analysis of
some published data sets as an illustration of its
applicability.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Irigoien:2013:DPI,
author = "Itziar Irigoien and Francesc Mestres and Concepcion
Arenas",
title = "The Depth Problem: Identifying the Most Representative
Units in a Data Group",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "161--172",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.147",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper presents a solution to the problem of how
to identify the units in groups or clusters that have
the greatest degree of centrality and best characterize
each group. This problem frequently arises in the
classification of data such as types of tumor, gene
expression profiles or general biomedical data. It is
particularly important in the common context that many
units do not properly belong to any cluster.
Furthermore, in gene expression data classification,
good identification of the most central units in a
cluster enables recognition of the most important
samples in a particular pathological process. We
propose a new depth function that allows us to identify
central units. As our approach is based on a measure of
distance or dissimilarity between any pair of units, it
can be applied to any kind of multivariate data
(continuous, binary or multiattribute data). Therefore,
it is very valuable in many biomedical applications,
which usually involve noncontinuous data, such as
clinical, pathological, or biological data sources. We
validate the approach using artificial examples and
apply it to empirical data. The results show the good
performance of our statistical approach.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wu:2013:ROC,
author = "Xia Wu and Juan Li and Napatkamon Ayutyanont and
Hillary Protas and William Jagust and Adam Fleisher and
Eric Reiman and Li Yao and Kewei Chen",
title = "The Receiver Operational Characteristic for Binary
Classification with Multiple Indices and Its
Application to the Neuroimaging Study of {Alzheimer}'s
Disease",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "173--180",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.141",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Given a single index, the receiver operational
characteristic (ROC) curve analysis is routinely
utilized for characterizing performances in
distinguishing two conditions/groups in terms of
sensitivity and specificity. Given the availability of
multiple data sources (referred to as multi-indices),
such as multimodal neuroimaging data sets, cognitive
tests, and clinical ratings and genomic data in
Alzheimer's disease (AD) studies, the
single-index-based ROC underutilizes all available
information. For a long time, a number of
algorithmic/analytic approaches combining multiple
indices have been widely used to simultaneously
incorporate multiple sources. In this study, we propose
an alternative for combining multiple indices using
logical operations, such as ``AND,'' ``OR,'' and ``at
least $ (n) $'' (where $ (n) $ is an integer), to
construct multivariate ROC (multiV-ROC) and
characterize the sensitivity and specificity
statistically associated with the use of multiple
indices. With and without the ``leave-one-out''
cross-validation, we used two data sets from AD studies
to showcase the potentially increased
sensitivity/specificity of the multiV-ROC in comparison
to the single-index ROC and linear discriminant
analysis (an analytic way of combining multi-indices).
We conclude that, for the data sets we investigated,
the proposed multiV-ROC approach is capable of
providing a natural and practical alternative with
improved classification accuracy as compared to
univariate ROC and linear discriminant analysis.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Shao:2013:UMB,
author = "Gui-Fang Shao and Fan Yang and Qian Zhang and Qi-Feng
Zhou and Lin-Kai Luo",
title = "Using the Maximum Between-Class Variance for Automatic
Gridding of {cDNA} Microarray Images",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "181--192",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.130",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gridding is the first and most important step to
separate the spots into distinct areas in microarray
image analysis. Human intervention is necessary for
most gridding methods, even if some so-called fully
automatic approaches also need preset parameters. The
applicability of these methods is limited in certain
domains and will cause variations in the gene
expression results. In addition, improper gridding,
which is influenced by both the misalignment and high
noise level, will affect the high throughput analysis.
In this paper, we have presented a fully automatic
gridding technique to break through the limitation of
traditional mathematical morphology gridding methods.
First, a preprocessing algorithm was applied for noise
reduction. Subsequently, the optimal threshold was
gained by using the improved Otsu method to actually
locate each spot. In order to diminish the error, the
original gridding result was optimized according to the
heuristic techniques by estimating the distribution of
the spots. Intensive experiments on six different data
sets indicate that our method is superior to the
traditional morphology one and is robust in the
presence of noise. More importantly, the algorithm
involved in our method is simple. Furthermore, human
intervention and parameters presetting are unnecessary
when the algorithm is applied in different types of
microarray images.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lamiable:2013:AGT,
author = "Alexis Lamiable and Franck Quessette and Sandrine Vial
and Dominique Barth and Alain Denise",
title = "An Algorithmic Game-Theory Approach for Coarse-Grain
Prediction of {RNA} {$3$D} Structure",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "193--199",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.148",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present a new approach for the prediction of the
coarse-grain 3D structure of RNA molecules. We model a
molecule as being made of helices and junctions. Those
junctions are classified into topological families that
determine their preferred 3D shapes. All the parts of
the molecule are then allowed to establish
long-distance contacts that induce a 3D folding of the
molecule. An algorithm relying on game theory is
proposed to discover such long-distance contacts that
allow the molecule to reach a Nash equilibrium. As
reported by our experiments, this approach allows one
to predict the global shape of large molecules of
several hundreds of nucleotides that are out of reach
of the state-of-the-art methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ovaska:2013:GRO,
author = "Kristian Ovaska and Lauri Lyly and Biswajyoti Sahu and
Olli A. Janne and Sampsa Hautaniemi",
title = "Genomic Region Operation Kit for Flexible Processing
of Deep Sequencing Data",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "200--206",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.170",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Computational analysis of data produced in deep
sequencing (DS) experiments is challenging due to large
data volumes and requirements for flexible analysis
approaches. Here, we present a mathematical formalism
based on set algebra for frequently performed
operations in DS data analysis to facilitate
translation of biomedical research questions to
language amenable for computational analysis. With the
help of this formalism, we implemented the Genomic
Region Operation Kit (GROK), which supports various
DS-related operations such as preprocessing, filtering,
file conversion, and sample comparison. GROK provides
high-level interfaces for R, Python, Lua, and command
line, as well as an extension C++ API. It supports
major genomic file formats and allows storing custom
genomic regions in efficient data structures such as
red-black trees and SQL databases. To demonstrate the
utility of GROK, we have characterized the roles of two
major transcription factors (TFs) in prostate cancer
using data from 10 DS experiments. GROK is freely
available with a user guide from {\tt
http://csbi.ltdk.helsinki.fi/grok/}.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wan:2013:HFA,
author = "Xiang Wan and Can Yang and Qiang Yang and Hongyu Zhao
and Weichuan Yu",
title = "{HapBoost}: a Fast Approach to Boosting Haplotype
Association Analyses in Genome-Wide Association
Studies",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "207--212",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.6",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genome-wide association study (GWAS) has been
successful in identifying genetic variants that are
associated with complex human diseases. In GWAS,
multilocus association analyses through linkage
disequilibrium (LD), named haplotype-based analyses,
may have greater power than single-locus analyses for
detecting disease susceptibility loci. However, the
large number of SNPs genotyped in GWAS poses great
computational challenges in the detection of haplotype
associations. We present a fast method named HapBoost
for finding haplotype associations, which can be
applied to quickly screen the whole genome. The
effectiveness of HapBoost is demonstrated by using both
synthetic and real data sets. The experimental results
show that the proposed approach can achieve comparably
accurate results while it performs much faster than
existing methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Howison:2013:HTC,
author = "Mark Howison",
title = "High-Throughput Compression of {FASTQ} Data with
{SeqDB}",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "213--218",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.160",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Compression has become a critical step in storing
next-generation sequencing (NGS) data sets because of
both the increasing size and decreasing costs of such
data. Recent research into efficiently compressing
sequence data has focused largely on improving
compression ratios. Yet, the throughputs of current
methods now lag far behind the I/O bandwidths of modern
storage systems. As biologists move their analyses to
high-performance systems with greater I/O bandwidth,
low-throughput compression becomes a limiting factor.
To address this gap, we present a new storage model
called SeqDB, which offers high-throughput compression
of sequence data with minimal sacrifice in compression
ratio. It achieves this by combining the existing
multithreaded Blosc compressor with a new data-parallel
byte-packing scheme, called SeqPack, which interleaves
sequence data and quality scores.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhu:2013:ISI,
author = "Yuan Zhu and Xiao-Fei Zhang and Dao-Qing Dai and
Meng-Yun Wu",
title = "Identifying Spurious Interactions and Predicting
Missing Interactions in the Protein-Protein Interaction
Networks via a Generative Network Model",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "219--225",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.164",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "With the rapid development of high-throughput
experiment techniques for protein-protein interaction
(PPI) detection, a large amount of PPI network data are
becoming available. However, the data produced by these
techniques have high levels of spurious and missing
interactions. This study assigns a new reliably
indication for each protein pairs via the new
generative network model (RIGNM) where the scale-free
property of the PPI network is considered to reliably
identify both spurious and missing interactions in the
observed high-throughput PPI network. The experimental
results show that the RIGNM is more effective and
interpretable than the compared methods, which
demonstrate that this approach has the potential to
better describe the PPI networks and drive new
discoveries.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Poleksic:2013:IAM,
author = "Aleksandar Poleksic",
title = "Improved Algorithms for Matching $r$-Separated Sets
with Applications to Protein Structure Alignment",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "226--229",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.135",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The Largest Common Point-set (LCP) and the Pattern
Matching (PM) problems have received much attention in
the fields of pattern matching, computer vision and
computational biology. Perhaps, the most important
application of these problems is the protein structural
alignment, which seeks to find a superposition of a
pair of input proteins that maximizes a given protein
structure similarity metric. Although it has been shown
that LCP and PM are both tractable problems, the
running times of existing algorithms are high-degree
polynomials. Here, we present novel methods for finding
approximate and exact threshold-LCP and threshold-PM
for r-separated sets, in general, and protein 3D
structures, in particular. Improved running times of
our methods are achieved by building upon several
different, previously published techniques.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhou:2013:MAD,
author = "Xiaowei Zhou and Can Yang and Xiang Wan and Hongyu
Zhao and Weichuan Yu",
title = "Multisample {aCGH} Data Analysis via Total Variation
and Spectral Regularization",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "230--235",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.166",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "DNA copy number variation (CNV) accounts for a large
proportion of genetic variation. One commonly used
approach to detecting CNVs is array-based comparative
genomic hybridization (aCGH). Although many methods
have been proposed to analyze aCGH data, it is not
clear how to combine information from multiple samples
to improve CNV detection. In this paper, we propose to
use a matrix to approximate the multisample aCGH data
and minimize the total variation of each sample as well
as the nuclear norm of the whole matrix. In this way,
we can make use of the smoothness property of each
sample and the correlation among multiple samples
simultaneously in a convex optimization framework. We
also developed an efficient and scalable algorithm to
handle large-scale data. Experiments demonstrate that
the proposed method outperforms the state-of-the-art
techniques under a wide range of scenarios and it is
capable of processing large data sets with millions of
probes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Caceres:2013:WSN,
author = "Alan Joseph J. Caceres and Juan Castillo and Jinnie
Lee and Katherine {St. John}",
title = "Walks on {SPR} Neighborhoods",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "236--239",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.136",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A nearest-neighbor-interchange (NNI)-walk is a
sequence of unrooted phylogenetic trees, {$ (T_1, T_2,
\ldots, T_k) $} where each consecutive pair of trees
differs by a single NNI move. We give tight bounds on
the length of the shortest NNI-walks that visit all
trees in a subtree-prune-and-regraft (SPR) neighborhood
of a given tree. For any unrooted, binary tree, {$ (T)
$}, on $ (n) $ leaves, the shortest walk takes {$
(\Theta (n^2)) $} additional steps more than the number
of trees in the SPR neighborhood. This answers Bryant's
Second Combinatorial Challenge from the Phylogenetics
Challenges List, the Isaac Newton Institute, 2011, and
the Penny Ante Problem List, 2009.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2013:RL,
author = "Anonymous",
title = "2012 Reviewers List",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "240--243",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.51",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The publication offers a note of thanks and lists its
reviewers.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Title:2013:AI,
author = "Title",
title = "2012 Annual Index",
journal = j-TCBB,
volume = "10",
number = "1",
pages = "244--270",
month = jan,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.42",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 10 07:28:56 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This index covers all technical items --- papers,
correspondence, reviews, etc. --- that appeared in this
periodical during 2012, and items from previous years
that were commented upon or corrected in 2012.
Departments and other items may also be covered if they
have been judged to have archival value. The Author
Index contains the primary entry for each item, listed
under the first author's name. The primary entry
includes the coauthors' names, the title of the paper
or other item, and its location, specified by the
publication abbreviation, year, month, and inclusive
pagination. The Subject Index contains entries
describing the item under all appropriate subject
headings, plus the First author's name, the publication
abbreviation, month, and year, and inclusive pages.
Note that the item title is found only under the
primary entry in the Author Index.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2013:GEA,
author = "Yi-Ping Phoebe Chen",
title = "Guest Editorial: Advanced Algorithms of
Bioinformatics",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "273--273",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.93",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lei:2013:CRC,
author = "Jikai Lei and Prapaporn Techa-angkoon and Yanni Sun",
title = "{Chain-RNA}: a Comparative {ncRNA} Search Tool Based
on the Two-Dimensional Chain Algorithm",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "274--285",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.137",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Noncoding RNA (ncRNA) identification is highly
important to modern biology. The state-of-the-art
method for ncRNA identification is based on comparative
genomics, in which evolutionary conservations of
sequences and secondary structures provide important
evidence for ncRNA search. For ncRNAs with low sequence
conservation but high structural similarity,
conventional local alignment tools such as BLAST yield
low sensitivity. Thus, there is a need for ncRNA search
methods that can incorporate both sequence and
structural similarities. We introduce chain-RNA, a
pairwise structural alignment tool that can effectively
locate cross-species conserved RNA elements with low
sequence similarity. In chain-RNA, stem-loop structures
are extracted from dot plots generated by an efficient
local-folding algorithm. Then, we formulate stem
alignment as an extended 2D chain problem and employ
existing chain algorithms. Chain-RNA is tested on a
data set containing annotated ncRNA homologs and is
applied to novel ncRNA search in a transcriptomic data
set. The experimental results show that chain-RNA has
better tradeoff between sensitivity and false positive
rate in ncRNA prediction than conventional sequence
similarity search tools and is more time efficient than
structural alignment tools. The source codes of
chain-RNA can be downloaded at
http://sourceforge.net/projects/chain-rna/ or at
http://www.cse.msu.edu/~leijikai/chain-rna/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Maji:2013:RFC,
author = "Pradipta Maji and Sushmita Paul",
title = "Rough-Fuzzy Clustering for Grouping Functionally
Similar Genes from Microarray Data",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "286--299",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.103",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gene expression data clustering is one of the
important tasks of functional genomics as it provides a
powerful tool for studying functional relationships of
genes in a biological process. Identifying coexpressed
groups of genes represents the basic challenge in gene
clustering problem. In this regard, a gene clustering
algorithm, termed as robust rough-fuzzy $ (c) $-means,
is proposed judiciously integrating the merits of rough
sets and fuzzy sets. While the concept of lower and
upper approximations of rough sets deals with
uncertainty, vagueness, and incompleteness in cluster
definition, the integration of probabilistic and
possibilistic memberships of fuzzy sets enables
efficient handling of overlapping partitions in noisy
environment. The concept of possibilistic lower bound
and probabilistic boundary of a cluster, introduced in
robust rough-fuzzy $ (c) $-means, enables efficient
selection of gene clusters. An efficient method is
proposed to select initial prototypes of different gene
clusters, which enables the proposed $ (c) $-means
algorithm to converge to an optimum or near optimum
solutions and helps to discover coexpressed gene
clusters. The effectiveness of the algorithm, along
with a comparison with other algorithms, is
demonstrated both qualitatively and quantitatively on
14 yeast microarray data sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Le:2013:CRT,
author = "Ngoc Tu Le and Tu Bao Ho and Bich Hai Ho",
title = "Computational Reconstruction of Transcriptional
Relationships from {ChIP}-Chip Data",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "300--307",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.102",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Eukaryotic gene transcription is a complex process,
which requires the orchestrated recruitment of a large
number of proteins, such as sequence-specific DNA
binding factors, chromatin remodelers and modifiers,
and general transcription machinery, to regulatory
regions. Previous works have shown that these
regulatory proteins favor specific organizational theme
along promoters. Details about how they cooperatively
regulate transcriptional process, however, remain
unclear. We developed a method to reconstruct a
Bayesian network (BN) model representing functional
relationships among various transcriptional components.
The positive/negative influence between these
components was measured from protein binding and
nucleosome occupancy data and embedded into the model.
Application on S.cerevisiae ChIP-Chip data showed that
the proposed method can recover confirmed
relationships, such as Isw1-Pol II, TFIIH-Pol II,
TFIIB-TBP, Pol II-H3K36Me3, H3K4Me3-H3K14Ac, etc.
Moreover, it can distinguish colocating components from
functionally related ones. Novel relationships, e.g.,
ones between Mediator and chromatin remodeling
complexes (CRCs), and the combinatorial regulation of
Pol II recruitment and activity by CRCs and general
transcription factors (GTFs), were also suggested.
Conclusion: protein binding events during transcription
positively influence each other. Among contributing
components, GTFs and CRCs play pivotal roles in
transcriptional regulation. These findings provide
insights into the regulatory mechanism.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fages:2013:GEI,
author = "Fran{\c{c}}ois Fages and Sylvain Soliman",
title = "{Guest Editors}' Introduction to the Special Section
on Computational Methods in Systems Biology",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "308--309",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.94",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Henzinger:2013:PAC,
author = "Thomas A. Henzinger and Maria Mateescu",
title = "The Propagation Approach for Computing Biochemical
Reaction Networks",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "310--322",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.91",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We introduce propagation models (PMs), a formalism
able to express several kinds of equations that
describe the behavior of biochemical reaction networks.
Furthermore, we introduce the propagation abstract data
type (PADT), which separates concerns regarding
different numerical algorithms for the transient
analysis of biochemical reaction networks from concerns
regarding their implementation, thus allowing for
portable and efficient solutions. The state of a
propagation abstract data type is given by a vector
that assigns mass values to a set of nodes, and its $
({\bf next}) $ operator propagates mass values through
this set of nodes. We propose an approximate
implementation of the $ ({\bf next}) $ operator, based
on threshold abstraction, which propagates only
``significant'' mass values and thus achieves a
compromise between efficiency and accuracy. Finally, we
give three use cases for propagation models: the
chemical master equation (CME), the reaction rate
equation (RRE), and a hybrid method that combines these
two equations. These three applications use propagation
models in order to propagate probabilities and/or
expected values and variances of the model's
variables.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Murthy:2013:CAC,
author = "Abhishek Murthy and Ezio Bartocci and Flavio H. Fenton
and James Glimm and Richard A. Gray and Elizabeth M.
Cherry and Scott A. Smolka and Radu Grosu",
title = "Curvature Analysis of Cardiac Excitation Wavefronts",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "323--336",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.125",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present the Spiral Classification Algorithm (SCA),
a fast and accurate algorithm for classifying
electrical spiral waves and their associated breakup in
cardiac tissues. The classification performed by SCA is
an essential component of the detection and analysis of
various cardiac arrhythmic disorders, including
ventricular tachycardia and fibrillation. Given a
digitized frame of a propagating wave, SCA constructs a
highly accurate representation of the front and the
back of the wave, piecewise interpolates this
representation with cubic splines, and subjects the
result to an accurate curvature analysis. This analysis
is more comprehensive than methods based on spiral-tip
tracking, as it considers the entire wave front and
back. To increase the smoothness of the resulting
symbolic representation, the SCA uses weighted
overlapping of adjacent segments which increases the
smoothness at join points. SCA has been applied to a
number of representative types of spiral waves, and,
for each type, a distinct curvature evolution in time
(signature) has been identified. Distinct signatures
have also been identified for spiral breakup. These
results represent a significant first step in
automatically determining parameter ranges for which a
computational cardiac-cell network accurately
reproduces a particular kind of cardiac arrhythmia,
such as ventricular fibrillation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gao:2013:MMA,
author = "Qian Gao and David Gilbert and Monika Heiner and Fei
Liu and Daniele Maccagnola and David Tree",
title = "Multiscale Modeling and Analysis of Planar Cell
Polarity in the \bioname{Drosophila} Wing",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "337--351",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.101",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Modeling across multiple scales is a current challenge
in Systems Biology, especially when applied to
multicellular organisms. In this paper, we present an
approach to model at different spatial scales, using
the new concept of Hierarchically Colored Petri Nets
(HCPN). We apply HCPN to model a tissue comprising
multiple cells hexagonally packed in a honeycomb
formation in order to describe the phenomenon of Planar
Cell Polarity (PCP) signaling in Drosophila wing. We
have constructed a family of related models, permitting
different hypotheses to be explored regarding the
mechanisms underlying PCP. In addition our models
include the effect of well-studied genetic mutations.
We have applied a set of analytical techniques
including clustering and model checking over time
series of primary and secondary data. Our models
support the interpretation of biological observations
reported in the literature.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bozdag:2013:GTA,
author = "Serdar Bozdag and Timothy J. Close and Stefano
Lonardi",
title = "A Graph-Theoretical Approach to the Selection of the
Minimum Tiling Path from a Physical Map",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "352--360",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.26",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The problem of computing the minimum tiling path (MTP)
from a set of clones arranged in a physical map is a
cornerstone of hierarchical (clone-by-clone) genome
sequencing projects. We formulate this problem in a
graph theoretical framework, and then solve by a
combination of minimum hitting set and minimum spanning
tree algorithms. The tool implementing this strategy,
called FMTP, shows improved performance compared to the
widely used software FPC. When we execute FMTP and FPC
on the same physical map, the MTP produced by FMTP
covers a higher portion of the genome, and uses a
smaller number of clones. For instance, on the rice
genome the MTP produced by our tool would reduce by
about 11 percent the cost of a clone-by-clone
sequencing project. Source code, benchmark data sets,
and documentation of FMTP are freely available at {\tt
http://code.google.com/p/fingerprint-based-minimal-tiling-path/}
under MIT license.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yang:2013:EBC,
author = "Cheng-Hong Yang and Yu-Da Lin and Li-Yeh Chaung and
Hsueh-Wei Chang",
title = "Evaluation of Breast Cancer Susceptibility Using
Improved Genetic Algorithms to Generate Genotype {SNP}
Barcodes",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "361--371",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.27",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genetic association is a challenging task for the
identification and characterization of genes that
increase the susceptibility to common complex
multifactorial diseases. To fully execute genetic
studies of complex diseases, modern geneticists face
the challenge of detecting interactions between loci. A
genetic algorithm (GA) is developed to detect the
association of genotype frequencies of cancer cases and
noncancer cases based on statistical analysis. An
improved genetic algorithm (IGA) is proposed to improve
the reliability of the GA method for high-dimensional
SNP-SNP interactions. The strategy offers the top five
results to the random population process, in which they
guide the GA toward a significant search course. The
IGA increases the likelihood of quickly detecting the
maximum ratio difference between cancer cases and
noncancer cases. The study systematically evaluates the
joint effect of 23 SNP combinations of six steroid
hormone metabolisms, and signaling-related genes
involved in breast carcinogenesis pathways were
systematically evaluated, with IGA successfully
detecting significant ratio differences between breast
cancer cases and noncancer cases. The possible breast
cancer risks were subsequently analyzed by odds-ratio
(OR) and risk-ratio analysis. The estimated OR of the
best SNP barcode is significantly higher than 1
(between 1.15 and 7.01) for specific combinations of
two to 13 SNPs. Analysis results support that the IGA
provides higher ratio difference values than the GA
between breast cancer cases and noncancer cases over
3-SNP to 13-SNP interactions. A more specific SNP-SNP
interaction profile for the risk of breast cancer is
also provided.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yang:2013:FNF,
author = "Jiaoyun Yang and Yun Xu and Xiaohui Yao and Guoliang
Chen",
title = "{FNphasing}: a Novel Fast Heuristic Algorithm for
Haplotype Phasing Based on Flow Network Model",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "372--382",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.18",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "An enormous amount of sequence data has been generated
with the development of new DNA sequencing
technologies, which presents great challenges for
computational biology problems such as haplotype
phasing. Although arduous efforts have been made to
address this problem, the current methods still cannot
efficiently deal with the incoming flood of large-scale
data. In this paper, we propose a flow network model to
tackle haplotype phasing problem, and explain some
classical haplotype phasing rules based on this model.
By incorporating the heuristic knowledge obtained from
these classical rules, we design an algorithm FNphasing
based on the flow network model. Theoretically, the
time complexity of our algorithm is {$ (O(n^2 m + m^2))
$}, which is better than that of 2SNP, one of the most
efficient algorithms currently. After testing the
performance of FNphasing with several simulated data
sets, the experimental results show that when applied
on large-scale data sets, our algorithm is
significantly faster than the state-of-the-art Beagle
algorithm. FNphasing also achieves an equal or superior
accuracy compared with other approaches.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lazar:2013:GNA,
author = "Cosmin Lazar and Jonatan Taminau and Stijn Meganck and
David Steenhoff and Alain Coletta and David Y. Weiss
Solis and Colin Molter and Robin Duque and Hugues
Bersini and Ann Nowe",
title = "{GENESHIFT}: a Nonparametric Approach for Integrating
Microarray Gene Expression Data Based on the Inner
Product as a Distance Measure between the Distributions
of Genes",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "383--392",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.12",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The potential of microarray gene expression (MAGE)
data is only partially explored due to the limited
number of samples in individual studies. This
limitation can be surmounted by merging or integrating
data sets originating from independent MAGE
experiments, which are designed to study the same
biological problem. However, this process is hindered
by batch effects that are study-dependent and result in
random data distortion; therefore numerical
transformations are needed to render the integration of
different data sets accurate and meaningful. Our
contribution in this paper is two-fold. First we
propose GENESHIFT, a new nonparametric batch effect
removal method based on two key elements from
statistics: empirical density estimation and the inner
product as a distance measure between two probability
density functions; second we introduce a new validation
index of batch effect removal methods based on the
observation that samples from two independent studies
drawn from a same population should exhibit similar
probability density functions. We evaluated and
compared the GENESHIFT method with four other
state-of-the-art methods for batch effect removal:
Batch-mean centering, empirical Bayes or COMBAT,
distance-weighted discrimination, and cross-platform
normalization. Several validation indices providing
complementary information about the efficiency of batch
effect removal methods have been employed in our
validation framework. The results show that none of the
methods clearly outperforms the others. More than that,
most of the methods used for comparison perform very
well with respect to some validation indices while
performing very poor with respect to others. GENESHIFT
exhibits robust performances and its average rank is
the highest among the average ranks of all methods used
for comparison.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bernardini:2013:GRN,
author = "Camilla Bernardini and Federica Censi and Wanda
Lattanzi and Giovanni Calcagnini and Alessandro
Giuliani",
title = "Gene Regulation Networks in Early Phase of {Duchenne}
Muscular Dystrophy",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "393--400",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.24",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The aim of this study was to analyze previously
published gene expression data of skeletal muscle
biopsies of Duchenne muscular dystrophy (DMD) patients
and controls (gene expression omnibus database,
accession \#GSE6011) using systems biology approaches.
We applied an unsupervised method to discriminate
patient and control populations, based on principal
component analysis, using the gene expressions as units
and patients as variables. The genes having the highest
absolute scores in the discrimination between the
groups, were then analyzed in terms of gene expression
networks, on the basis of their mutual correlation in
the two groups. The correlation network structures
suggest two different modes of gene regulation in the
two groups, reminiscent of important aspects of DMD
pathogenesis.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Baya:2013:HMC,
author = "Ariel E. Baya and Pablo M. Granitto",
title = "How Many Clusters: a Validation Index for
Arbitrary-Shaped Clusters",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "401--414",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.32",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Clustering validation indexes are intended to assess
the goodness of clustering results. Many methods used
to estimate the number of clusters rely on a validation
index as a key element to find the correct answer. This
paper presents a new validation index based on graph
concepts, which has been designed to find arbitrary
shaped clusters by exploiting the spatial layout of the
patterns and their clustering label. This new
clustering index is combined with a solid statistical
detection framework, the gap statistic. The resulting
method is able to find the right number of
arbitrary-shaped clusters in diverse situations, as we
show with examples where this information is available.
A comparison with several relevant validation methods
is carried out using artificial and gene expression
data sets. The results are very encouraging, showing
that the underlying structure in the data can be more
accurately detected with the new clustering index. Our
gene expression data results also indicate that this
new index is stable under perturbation of the input
data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2013:MFP,
author = "Qingfeng Chen and Wei Lan and Jianxin Wang",
title = "Mining Featured Patterns of {MiRNA} Interaction Based
on Sequence and Structure Similarity",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "415--422",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.5",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "MicroRNA (miRNA) is an endogenous small noncoding RNA
that plays an important role in gene expression through
the post-transcriptional gene regulation pathways.
There are many literature works focusing on predicting
miRNA targets and exploring gene regulatory networks of
miRNA families. We suggest, however, the study to
identify the interaction between miRNAs is
insufficient. This paper presents a framework to
identify relationships between miRNAs using joint
entropy, to investigate the regulatory features of
miRNAs. Both the sequence and secondary structure are
taken into consideration to make our method more
relevant from the biological viewpoint. Further, joint
entropy is applied to identify correlated miRNAs, which
are more desirable from the perspective of the gene
regulatory network. A data set including {\em
Drosophila melanogaster\/} and \bioname{Anopheles
gambiae\/} is used in the experiment. The results
demonstrate that our approach is able to identify known
miRNA interaction and uncover novel patterns of miRNA
regulatory network.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Maulik:2013:MQB,
author = "Ujjwal Maulik and Anirban Mukhopadhyay and Malay
Bhattacharyya and Lars Kaderali and Benedikt Brors and
Sanghamitra Bandyopadhyay and Roland Eils",
title = "Mining Quasi-Bicliques from {HIV-1}-Human Protein
Interaction Network: a Multiobjective Biclustering
Approach",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "423--435",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.139",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this work, we model the problem of mining
quasi-bicliques from weighted viral-host
protein-protein interaction network as a biclustering
problem for identifying strong interaction modules. In
this regard, a multiobjective genetic algorithm-based
biclustering technique is proposed that simultaneously
optimizes three objective functions to obtain dense
biclusters having high mean interaction strengths. The
performance of the proposed technique has been compared
with that of other existing biclustering methods on an
artificial data. Subsequently, the proposed
biclustering method is applied on the records of
biologically validated and predicted interactions
between a set of HIV-1 proteins and a set of human
proteins to identify strong interaction modules. For
this, the entire interaction information is realized as
a bipartite graph. We have further investigated the
biological significance of the obtained biclusters. The
human proteins involved in the strong interaction
module have been found to share common biological
properties and they are identified as the gateways of
viral infection leading to various diseases. These
human proteins can be potential drug targets for
developing anti-HIV drugs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2013:MLR,
author = "Xiao Wang and Guo-Zheng Li",
title = "Multilabel Learning via Random Label Selection for
Protein Subcellular Multilocations Prediction",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "436--446",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.21",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Prediction of protein subcellular localization is an
important but challenging problem, particularly when
proteins may simultaneously exist at, or move between,
two or more different subcellular location sites. Most
of the existing protein subcellular localization
methods are only used to deal with the single-location
proteins. In the past few years, only a few methods
have been proposed to tackle proteins with multiple
locations. However, they only adopt a simple strategy,
that is, transforming the multilocation proteins to
multiple proteins with single location, which does not
take correlations among different subcellular locations
into account. In this paper, a novel method named
random label selection (RALS) (multilabel learning via
RALS), which extends the simple binary relevance (BR)
method, is proposed to learn from multilocation
proteins in an effective and efficient way. RALS does
not explicitly find the correlations among labels, but
rather implicitly attempts to learn the label
correlations from data by augmenting original feature
space with randomly selected labels as its additional
input features. Through the fivefold cross-validation
test on a benchmark data set, we demonstrate our
proposed method with consideration of label
correlations obviously outperforms the baseline BR
method without consideration of label correlations,
indicating correlations among different subcellular
locations really exist and contribute to improvement of
prediction performance. Experimental results on two
benchmark data sets also show that our proposed methods
achieve significantly higher performance than some
other state-of-the-art methods in predicting
subcellular multilocations of proteins. The prediction
web server is available at {\tt
http://levis.tongji.edu.cn:8080/bioinfo/MLPred-Euk/}
for the public usage.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2013:NLS,
author = "Yifeng Li and Alioune Ngom",
title = "Nonnegative Least-Squares Methods for the
Classification of High-Dimensional Biological Data",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "447--456",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.30",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Microarray data can be used to detect diseases and
predict responses to therapies through classification
models. However, the high dimensionality and low sample
size of such data result in many computational problems
such as reduced prediction accuracy and slow
classification speed. In this paper, we propose a novel
family of nonnegative least-squares classifiers for
high-dimensional microarray gene expression and
comparative genomic hybridization data. Our approaches
are based on combining the advantages of using local
learning, transductive learning, and ensemble learning,
for better prediction performance. To study the
performances of our methods, we performed computational
experiments on 17 well-known data sets with diverse
characteristics. We have also performed statistical
comparisons with many classification techniques
including the well-performing SVM approach and two
related but recent methods proposed in literature.
Experimental results show that our approaches are
faster and achieve generally a better prediction
performance over compared methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2013:NFV,
author = "Hong-Jie Yu and De-Shuang Huang",
title = "Normalized Feature Vectors: a Novel Alignment-Free
Sequence Comparison Method Based on the Numbers of
Adjacent Amino Acids",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "457--467",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.10",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Based on all kinds of adjacent amino acids (AAA), we
map each protein primary sequence into a 400 by ({$ (L
- 1) $}) matrix {$ ({\schmi M}) $}. In addition, we
further derive a normalized 400-tuple mathematical
descriptors {$ ({\schmi D}) $}, which is extracted from
the primary protein sequences via singular values
decomposition (SVD) of the matrix. The obtained 400-D
normalized feature vectors (NFVs) further facilitate
our quantitative analysis of protein sequences. Using
the normalized representation of the primary protein
sequences, we analyze the similarity for different
sequences upon two data sets: (1) ND5 sequences from
nine species and (2) transferrin sequences of 24
vertebrates. We also compared the results in this study
with those from other related works. These two
experiments illustrate that our proposed NFV-AAA
approach does perform well in the field of similarity
analysis of sequence.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tu:2013:INR,
author = "Chien-Ta Tu and Bor-Sen Chen",
title = "On the Increase in Network Robustness and Decrease in
Network Response Ability during the Aging Process: a
Systems Biology Approach via Microarray Data",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "468--480",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.23",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Aging, an extremely complex and system-level process,
has attracted much attention in medical research,
especially since chronic diseases are quite prevalent
in the elderly population. These may be the result of
both gene mutations that lead to intrinsic
perturbations and environmental changes that may
stimulate signaling in the body. Therefore, analysis of
network robustness to tolerate intrinsic perturbations
and network response ability of gene networks to
respond to external stimuli during the aging process
may provide insight into the systematic changes caused
by aging. We first propose novel methods to estimate
network robustness and measure network response ability
of gene regulatory networks by using their
corresponding microarray data in the aging process.
Then, we find that an aging-related gene network is
more robust to intrinsic perturbations in the elderly
than the young, and therefore is less responsive to
external stimuli. Finally, we find that the response
abilities of individual genes, especially FOXOs,
NF-{\^I}${}^o$B, and p53, are significantly different
in the young versus the aged subjects. These
observations are consistent with experimental findings
in the aged population, e.g., elevated incidence of
tumorigenesis and diminished resistance to oxidative
stress. The proposed method can also be used for
exploring and analyzing the dynamic properties of other
biological processes via corresponding microarray data
to provide useful information on clinical strategy and
drug target selection.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{DeRonne:2013:POP,
author = "Kevin W. DeRonne and George Karypis",
title = "{Pareto} Optimal Pairwise Sequence Alignment",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "481--493",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.2",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Sequence alignment using evolutionary profiles is a
commonly employed tool when investigating a protein.
Many profile-profile scoring functions have been
developed for use in such alignments, but there has not
yet been a comprehensive study of Pareto optimal
pairwise alignments for combining multiple such
functions. We show that the problem of generating
Pareto optimal pairwise alignments has an optimal
substructure property, and develop an efficient
algorithm for generating Pareto optimal frontiers of
pairwise alignments. All possible sets of two, three,
and four profile scoring functions are used from a pool
of 11 functions and applied to 588 pairs of proteins in
the ce\_ref data set. The performance of the best
objective combinations on ce\_ref is also evaluated on
an independent set of 913 protein pairs extracted from
the BAliBASE RV11 data set. Our
dynamic-programming-based heuristic approach produces
approximated Pareto optimal frontiers of pairwise
alignments that contain comparable alignments to those
on the exact frontier, but on average in less than
1/58th the time in the case of four objectives. Our
results show that the Pareto frontiers contain
alignments whose quality is better than the alignments
obtained by single objectives. However, the task of
identifying a single high-quality alignment among those
in the Pareto frontier remains challenging.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tsai:2013:PBL,
author = "Tsung-Heng Tsai and Mahlet G. Tadesse and Yue Wang and
Habtom W. Ressom",
title = "Profile-Based {LC-MS} Data Alignment --- a {Bayesian}
Approach",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "494--503",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.25",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A Bayesian alignment model (BAM) is proposed for
alignment of liquid chromatography-mass spectrometry
(LC-MS) data. BAM belongs to the category of
profile-based approaches, which are composed of two
major components: a prototype function and a set of
mapping functions. Appropriate estimation of these
functions is crucial for good alignment results. BAM
uses Markov chain Monte Carlo (MCMC) methods to draw
inference on the model parameters and improves on
existing MCMC-based alignment methods through (1) the
implementation of an efficient MCMC sampler and (2) an
adaptive selection of knots. A block
Metropolis--Hastings algorithm that mitigates the
problem of the MCMC sampler getting stuck at local
modes of the posterior distribution is used for the
update of the mapping function coefficients. In
addition, a stochastic search variable selection (SSVS)
methodology is used to determine the number and
positions of knots. We applied BAM to a simulated data
set, an LC-MS proteomic data set, and two LC-MS
metabolomic data sets, and compared its performance
with the Bayesian hierarchical curve registration
(BHCR) model, the dynamic time-warping (DTW) model, and
the continuous profile model (CPM). The advantage of
applying appropriate profile-based retention time
correction prior to performing a feature-based approach
is also demonstrated through the metabolomic data
sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rud:2013:RFL,
author = "Ali Gholami Rud and Saeed Shahrivari and Saeed Jalili
and Zahra Razaghi Moghadam Kashani",
title = "{RANGI}: a Fast List-Colored Graph Motif Finding
Algorithm",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "504--513",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.167",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Given a multiset of colors as the query and a
list-colored graph, i.e., an undirected graph with a
set of colors assigned to each of its vertices, in the
NP-hard list-colored graph motif problem the goal is to
find the largest connected subgraph such that one can
select a color from the set of colors assigned to each
of its vertices to obtain a subset of the query. This
problem was introduced to find functional motifs in
biological networks. We present a branch-and-bound
algorithm named RANGI for finding and enumerating
list-colored graph motifs. As our experimental results
show, RANGI's pruning methods and heuristics make it
quite fast in practice compared to the algorithms
presented in the literature. We also present a parallel
version of RANGI that achieves acceptable
scalability.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2013:RSN,
author = "Wei Liu and Dong Li and Yunping Zhu and Hongwei Xie
and Fuchu He",
title = "Reconstruction of Signaling Network from Protein
Interactions Based on Function Annotations",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "514--521",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.20",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The directionality of protein interactions is the
prerequisite of forming various signaling networks, and
the construction of signaling networks is a critical
issue in the discovering the mechanism of the life
process. In this paper, we proposed a novel method to
infer the directionality in protein-protein interaction
networks and furthermore construct signaling networks.
Based on the functional annotations of proteins, we
proposed a novel parameter GODS and established the
prediction model. This method shows high sensitivity
and specificity to predict the directionality of
protein interactions, evaluated by fivefold cross
validation. By taking the threshold value of GODS as 2,
we achieved accuracy 95.56 percent and coverage 74.69
percent in the human test set. Also, this method was
successfully applied to reconstruct the classical
signaling pathways in human. This study not only
provided an effective method to unravel the unknown
signaling pathways, but also the deeper understanding
for the signaling networks, from the aspect of protein
function.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gorecki:2013:UTR,
author = "Pawel Gorecki and Oliver Eulenstein and Jerzy Tiuryn",
title = "Unrooted Tree Reconciliation: a Unified Approach",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "522--536",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.22",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Tree comparison functions are widely used in
phylogenetics for comparing evolutionary trees.
Unrooted trees can be compared with rooted trees by
identifying all rootings of the unrooted tree that
minimize some provided comparison function between two
rooted trees. The plateau property is satisfied by the
provided function, if all optimal rootings form a
subtree, or plateau, in the unrooted tree, from which
the rootings along every path toward a leaf have
monotonically increasing costs. This property is
sufficient for the linear-time identification of all
optimal rootings and rooting costs. However, the
plateau property has only been proven for a few rooted
comparison functions, requiring individual proofs for
each function without benefitting from inherent
structural features of such functions. Here, we
introduce the consistency condition that is sufficient
for a general function to satisfy the plateau property.
For consistent functions, we introduce general
linear-time solutions that identify optimal rootings
and all rooting costs. Further, we identify novel
relationships between consistent functions in terms of
plateaus, especially the plateau of the well-studied
duplication-loss function is part of a plateau of every
other consistent function. We introduce a novel
approach for identifying consistent cost functions by
defining a formal language of Boolean costs. Formulas
in this language can be interpreted as cost functions.
Finally, we demonstrate the performance of our general
linear-time solutions in practice using empirical and
simulation studies.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Meskin:2013:PEB,
author = "N. Meskin and H. Nounou and M. Nounou and A. Datta",
title = "Parameter Estimation of Biological Phenomena: an
Unscented {Kalman} Filter Approach",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "537--543",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.19",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recent advances in high-throughput technologies for
biological data acquisition have spurred a broad
interest in the construction of mathematical models for
biological phenomena. The development of such
mathematical models relies on the estimation of unknown
parameters of the system using the time-course profiles
of different metabolites in the system. One of the main
challenges in the parameter estimation of biological
phenomena is the fact that the number of unknown
parameters is much more than the number of metabolites
in the system. Moreover, the available metabolite
measurements are corrupted by noise. In this paper, a
new parameter estimation algorithm is developed based
on the stochastic estimation framework for nonlinear
systems, namely the unscented Kalman filter (UKF). A
new iterative UKF algorithm with covariance resetting
is developed in which the UKF algorithm is applied
iteratively to the available noisy time profiles of the
metabolites. The proposed estimation algorithm is
applied to noisy time-course data synthetically
produced from a generic branched pathway as well as
real time-course profile for the Cad system of E. coli.
The simulation results demonstrate the effectiveness of
the proposed scheme.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2013:CPSa,
author = "Anonymous",
title = "Call for Papers: Special issue on sofware and
databases in {TCBB}",
journal = j-TCBB,
volume = "10",
number = "2",
pages = "544--544",
month = mar,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.88",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 16 07:55:23 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rathore:2013:RSC,
author = "Saima Rathore and Mutawarra Hussain and Ahmad Ali and
Asifullah Khan",
title = "A Recent Survey on Colon Cancer Detection Techniques",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "545--563",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.84",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Colon cancer causes deaths of about half a million
people every year. Common method of its detection is
histopathological tissue analysis, which, though leads
to vital diagnosis, is significantly correlated to the
tiredness, experience, and workload of the pathologist.
Researchers have been working since decades to get rid
of manual inspection, and to develop trustworthy
systems for detecting colon cancer. Several techniques,
based on spectral/spatial analysis of colon biopsy
images, and serum and gene analysis of colon samples,
have been proposed in this regard. Due to rapid
evolution of colon cancer detection techniques, a
latest review of recent research in this field is
highly desirable. The aim of this paper is to discuss
various colon cancer detection techniques. In this
survey, we categorize the techniques on the basis of
the adopted methodology and underlying data set, and
provide detailed description of techniques in each
category. Additionally, this study provides an
extensive comparison of various colon cancer detection
categories, and of multiple techniques within each
category. Further, most of the techniques have been
evaluated on similar data set to provide a fair
performance comparison. Analysis reveals that neither
of the techniques is perfect; however, research
community is progressively inching toward the finest
possible solution.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dehzangi:2013:CFE,
author = "Abdollah Dehzangi and Kuldip Paliwal and Alok Sharma
and Omid Dehzangi and Abdul Sattar",
title = "A Combination of Feature Extraction Methods with an
Ensemble of Different Classifiers for Protein
Structural Class Prediction Problem",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "564--575",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.65",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Better understanding of structural class of a given
protein reveals important information about its overall
folding type and its domain. It can also be directly
used to provide critical information on general
tertiary structure of a protein which has a profound
impact on protein function determination and drug
design. Despite tremendous enhancements made by pattern
recognition-based approaches to solve this problem, it
still remains as an unsolved issue for bioinformatics
that demands more attention and exploration. In this
study, we propose a novel feature extraction model that
incorporates physicochemical and evolutionary-based
information simultaneously. We also propose overlapped
segmented distribution and autocorrelation-based
feature extraction methods to provide more local and
global discriminatory information. The proposed feature
extraction methods are explored for 15 most promising
attributes that are selected from a wide range of
physicochemical-based attributes. Finally, by applying
an ensemble of different classifiers namely,
Adaboost.M1, LogitBoost, naive Bayes, multilayer
perceptron (MLP), and support vector machine (SVM) we
show enhancement of the protein structural class
prediction accuracy for four popular benchmarks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bordewich:2013:AGP,
author = "Magnus Bordewich and Radu Mihaescu",
title = "Accuracy Guarantees for Phylogeny Reconstruction
Algorithms Based on Balanced Minimum Evolution",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "576--583",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.39",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Distance-based phylogenetic methods attempt to
reconstruct an accurate phylogenetic tree from an
estimated matrix of pairwise distances between taxa.
This paper examines two distance-based algorithms
(GreedyBME and FastME) that are based on the principle
of minimizing the balanced minimum evolution score of
the output tree in relation to the given estimated
distance matrix. This is also the principle that
underlies the neighbor-joining (NJ) algorithm. We show
that GreedyBME and FastME both reconstruct the entire
correct tree if the input data are quartet consistent,
and also that if the maximum error of any distance
estimate is $ (\epsilon) $, then both algorithms output
trees containing all sufficiently long edges of the
true tree: those having length at least $ (3 \epsilon)
$. That is to say, the algorithms have edge safety
radius 1/3. In contrast, quartet consistency of the
data is not sufficient to guarantee the NJ algorithm
reconstructs the correct tree, and moreover, the NJ
algorithm has edge safety radius of 1/4: Only edges of
the true tree of length at least $ (4 \epsilon) $ can
be guaranteed to appear in the output. These results
give further theoretical support to the experimental
evidence suggesting FastME is a more suitable
distance-based phylogeny reconstruction method than the
NJ algorithm.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zou:2013:BNM,
author = "Yi Ming Zou",
title = "{Boolean} Networks with Multiexpressions and
Parameters",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "584--592",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.79",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "To model biological systems using networks, it is
desirable to allow more than two levels of expression
for the nodes and to allow the introduction of
parameters. Various modeling and simulation methods
addressing these needs using Boolean models, both
synchronous and asynchronous, have been proposed in the
literature. However, analytical study of these more
general Boolean networks models is lagging. This paper
aims to develop a concise theory for these different
Boolean logic-based modeling methods. Boolean models
for networks where each node can have more than two
levels of expression and Boolean models with parameters
are defined algebraically with examples provided.
Certain classes of random asynchronous Boolean networks
and deterministic moduli asynchronous Boolean networks
are investigated in detail using the setting introduced
in this paper. The derived theorems provide a clear
picture for the attractor structures of these
asynchronous Boolean networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2013:CFS,
author = "Hsi-Che Liu and Pei-Chen Peng and Tzung-Chien Hsieh
and Ting-Chi Yeh and Chih-Jen Lin and Chien-Yu Chen and
Jen-Yin Hou and Lee-Yung Shih and Der-Cherng Liang",
title = "Comparison of Feature Selection Methods for
Cross-Laboratory Microarray Analysis",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "593--604",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.70",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The amount of gene expression data of microarray has
grown exponentially. To apply them for extensive
studies, integrated analysis of cross-laboratory
(cross-lab) data becomes a trend, and thus, choosing an
appropriate feature selection method is an essential
issue. This paper focuses on feature selection for
Affymetrix (Affy) microarray studies across different
labs. We investigate four feature selection methods: $
(t) $-test, significance analysis of microarrays (SAM),
rank products (RP), and random forest (RF). The four
methods are applied to acute lymphoblastic leukemia,
acute myeloid leukemia, breast cancer, and lung cancer
Affy data which consist of three cross-lab data sets
each. We utilize a rank-based normalization method to
reduce the bias from cross-lab data sets. Training on
one data set or two combined data sets to test the
remaining data set(s) are both considered. Balanced
accuracy is used for prediction evaluation. This study
provides comprehensive comparisons of the four feature
selection methods in cross-lab microarray analysis.
Results show that SAM has the best classification
performance. RF also gets high classification accuracy,
but it is not as stable as SAM. The most naive method
is $ (t) $-test, but its performance is the worst among
the four methods. In this study, we further discuss the
influence from the number of training samples, the
number of selected genes, and the issue of unbalanced
data sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jamil:2013:DIC,
author = "Hasan M. Jamil",
title = "Designing Integrated Computational Biology Pipelines
Visually",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "605--618",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.69",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The long-term cost of developing and maintaining a
computational pipeline that depends upon data
integration and sophisticated workflow logic is too
high to even contemplate ``what if'' or ad hoc type
queries. In this paper, we introduce a novel
application building interface for computational
biology research, called VizBuilder, by leveraging a
recent query language called BioFlow for life sciences
databases. Using VizBuilder, it is now possible to
develop ad hoc complex computational biology
applications at throw away costs. The underlying query
language supports data integration and workflow
construction almost transparently and fully
automatically, using a best effort approach. Users
express their application by drawing it with VizBuilder
icons and connecting them in a meaningful way.
Completed applications are compiled and translated as
BioFlow queries for execution by the data management
system LifeDB, for which VizBuilder serves as a front
end. We discuss VizBuilder features and functionalities
in the context of a real life application after we
briefly introduce BioFlow. The architecture and design
principles of VizBuilder are also discussed. Finally,
we outline future extensions of VizBuilder. To our
knowledge, VizBuilder is a unique system that allows
visually designing computational biology pipelines
involving distributed and heterogeneous resources in an
ad hoc manner.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Abate:2013:GLB,
author = "Francesco Abate and Andrea Acquaviva and Elisa Ficarra
and Roberto Piva and Enrico Macii",
title = "{Gelsius}: a Literature-Based Workflow for Determining
Quantitative Associations between Genes and Biological
Processes",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "619--631",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.11",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "An effective knowledge extraction and quantification
methodology from biomedical literature would allow the
researcher to organize and analyze the results of
high-throughput experiments on microarrays and
next-generation sequencing technologies. Despite the
large amount of raw information available on the web, a
tool able to extract a measure of the correlation
between a list of genes and biological processes is not
yet available. In this paper, we present Gelsius, a
workflow that incorporates biomedical literature to
quantify the correlation between genes and terms
describing biological processes. To achieve this
target, we build different modules focusing on query
expansion and document cononicalization. In this way,
we reached to improve the measurement of correlation,
performed using a latent semantic analysis approach. To
the best of our knowledge, this is the first complete
tool able to extract a measure of genes-biological
processes correlation from literature. We demonstrate
the effectiveness of the proposed workflow on six
biological processes and a set of genes, by showing
that correlation results for known relationships are in
accordance with definitions of gene functions provided
by NCI Thesaurus. On the other side, the tool is able
to propose new candidate relationships for later
experimental validation. The tool is available at {\tt
http://bioeda1.polito.it:8080/medSearchServlet/}.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Haque:2013:GQB,
author = "Md. Muksitul Haque and Lawrence B. Holder and Michael
K. Skinner and Diane J. Cook",
title = "Generalized Query-Based Active Learning to Identify
Differentially Methylated Regions in {DNA}",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "632--644",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.38",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Active learning is a supervised learning technique
that reduces the number of examples required for
building a successful classifier, because it can choose
the data it learns from. This technique holds promise
for many biological domains in which classified
examples are expensive and time-consuming to obtain.
Most traditional active learning methods ask very
specific queries to the Oracle (e.g., a human expert)
to label an unlabeled example. The example may consist
of numerous features, many of which are irrelevant.
Removing such features will create a shorter query with
only relevant features, and it will be easier for the
Oracle to answer. We propose a generalized query-based
active learning (GQAL) approach that constructs
generalized queries based on multiple instances. By
constructing appropriately generalized queries, we can
achieve higher accuracy compared to traditional active
learning methods. We apply our active learning method
to find differentially DNA methylated regions (DMRs).
DMRs are DNA locations in the genome that are known to
be involved in tissue differentiation, epigenetic
regulation, and disease. We also apply our method on 13
other data sets and show that our method is better than
another popular active learning technique.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gremme:2013:GCS,
author = "Gordon Gremme and Sascha Steinbiss and Stefan Kurtz",
title = "{GenomeTools}: a Comprehensive Software Library for
Efficient Processing of Structured Genome Annotations",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "645--656",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.68",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genome annotations are often published as plain text
files describing genomic features and their
subcomponents by an implicit annotation graph. In this
paper, we present the GenomeTools, a convenient and
efficient software library and associated software
tools for developing bioinformatics software intended
to create, process or convert annotation graphs. The
GenomeTools strictly follow the annotation graph
approach, offering a unified graph-based
representation. This gives the developer intuitive and
immediate access to genomic features and tools for
their manipulation. To process large annotation sets
with low memory overhead, we have designed and
implemented an efficient pull-based approach for
sequential processing of annotations. This allows to
handle even the largest annotation sets, such as a
complete catalogue of human variations. Our
object-oriented C-based software library enables a
developer to conveniently implement their own
functionality on annotation graphs and to integrate it
into larger workflows, simultaneously accessing
compressed sequence data if required. The careful C
implementation of the GenomeTools does not only ensure
a light-weight memory footprint while allowing full
sequential as well as random access to the annotation
graph, but also facilitates the creation of bindings to
a variety of script programming languages (like Python
and Ruby) sharing the same interface.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2013:HFC,
author = "Zhiwen Yu and Hantao Chen and Jane You and Guoqiang
Han and Le Li",
title = "Hybrid Fuzzy Cluster Ensemble Framework for Tumor
Clustering from Biomolecular Data",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "657--670",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.59",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Cancer class discovery using biomolecular data is one
of the most important tasks for cancer diagnosis and
treatment. Tumor clustering from gene expression data
provides a new way to perform cancer class discovery.
Most of the existing research works adopt
single-clustering algorithms to perform tumor
clustering is from biomolecular data that lack
robustness, stability, and accuracy. To further improve
the performance of tumor clustering from biomolecular
data, we introduce the fuzzy theory into the cluster
ensemble framework for tumor clustering from
biomolecular data, and propose four kinds of hybrid
fuzzy cluster ensemble frameworks (HFCEF), named as
HFCEF-I, HFCEF-II, HFCEF-III, and HFCEF-IV,
respectively, to identify samples that belong to
different types of cancers. The difference between
HFCEF-I and HFCEF-II is that they adopt different
ensemble generator approaches to generate a set of
fuzzy matrices in the ensemble. Specifically, HFCEF-I
applies the affinity propagation algorithm (AP) to
perform clustering on the sample dimension and
generates a set of fuzzy matrices in the ensemble based
on the fuzzy membership function and base samples
selected by AP. HFCEF-II adopts AP to perform
clustering on the attribute dimension, generates a set
of subspaces, and obtains a set of fuzzy matrices in
the ensemble by performing fuzzy c-means on subspaces.
Compared with HFCEF-I and HFCEF-II, HFCEF-III and
HFCEF-IV consider the characteristics of HFCEF-I and
HFCEF-II. HFCEF-III combines HFCEF-I and HFCEF-II in a
serial way, while HFCEF-IV integrates HFCEF-I and
HFCEF-II in a concurrent way. HFCEFs adopt suitable
consensus functions, such as the fuzzy c-means
algorithm or the normalized cut algorithm (Ncut), to
summarize generated fuzzy matrices, and obtain the
final results. The experiments on real data sets from
UCI machine learning repository and cancer gene
expression profiles illustrate that (1) the proposed
hybrid fuzzy cluster ensemble frameworks work well on
real data sets, especially biomolecular data, and (2)
the proposed approaches are able to provide more
robust, stable, and accurate results when compared with
the state-of-the-art single clustering algorithms and
traditional cluster ensemble approaches.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{ElBakry:2013:IGR,
author = "Ola ElBakry and M. Omair Ahmad and M. N. S. Swamy",
title = "Inference of Gene Regulatory Networks with Variable
Time Delay from Time-Series Microarray Data",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "671--687",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.73",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Regulatory interactions among genes and gene products
are dynamic processes and hence modeling these
processes is of great interest. Since genes work in a
cascade of networks, reconstruction of gene regulatory
network (GRN) is a crucial process for a thorough
understanding of the underlying biological
interactions. We present here an approach based on
pairwise correlations and lasso to infer the GRN,
taking into account the variable time delays between
various genes. The proposed method is applied to both
synthetic and real data sets, and the results on
synthetic data show that the proposed approach
outperforms the current methods. Further, the results
using real data are more consistent with the existing
knowledge concerning the possible gene interactions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2013:ISS,
author = "Xiong Li and Bo Liao and Lijun Cai and Zhi Cao and Wen
Zhu",
title = "Informative {SNPs} Selection Based on Two-Locus and
Multilocus Linkage Disequilibrium: Criteria of
Max-Correlation and Min-Redundancy",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "688--695",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.61",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Currently, there are lots of methods to select
informative SNPs for haplotype reconstruction. However,
there are still some challenges that render them
ineffective for large data sets. First, some
traditional methods belong to wrappers which are of
high computational complexity. Second, some methods
ignore linkage disequilibrium that it is hard to
interpret selection results. In this study, we
innovatively derive optimization criteria by combining
two-locus and multilocus LD measure to obtain the
criteria of Max-Correlation and Min-Redundancy (MCMR).
Then, we use a greedy algorithm to select the candidate
set of informative SNPs constrained by the criteria.
Finally, we use backward scheme to refine the candidate
subset. We separately use small and middle ($ > 1, 000
$ SNPs) data sets to evaluate MCMR in terms of the
reconstuction accuracy, the time complexity, and the
compactness. Additionally, to demonstrate that MCMR is
practical for large data sets, we design a parameter $
(w) $ to adapt to various platforms and introduce
another replacement scheme for larger data sets, which
sharply narrow down the computational complexity of
evaluating the reconstruct ratio. Then, we first apply
our method based on haplotype reconstruction for large
size ($ > 5, 000 $ SNPs) data sets. The results confirm
that MCMR leads to promising improvement in informative
SNPs selection and prediction accuracy.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chan:2013:MAP,
author = "Tak-Ming Chan and Leung-Yau Lo and Ho-Yin Sze-To and
Kwong-Sak Leung and Xinshu Xiao and Man-Hon Wong",
title = "Modeling Associated Protein-{DNA} Pattern Discovery
with Unified Scores",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "696--707",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.60",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Understanding protein-DNA interactions, specifically
transcription factor (TF) and transcription factor
binding site (TFBS) bindings, is crucial in deciphering
gene regulation. The recent associated TF-TFBS pattern
discovery combines one-sided motif discovery on both
the TF and the TFBS sides. Using sequences only, it
identifies the short protein-DNA binding cores
available only in high-resolution 3D structures. The
discovered patterns lead to promising subtype and
disease analysis applications. While the related
studies use either association rule mining or existing
TFBS annotations, none has proposed any formal unified
(both-sided) model to prioritize the top verifiable
associated patterns. We propose the unified scores and
develop an effective pipeline for associated TF-TFBS
pattern discovery. Our stringent instance-level
evaluations show that the patterns with the top unified
scores match with the binding cores in 3D structures
considerably better than the previous works, where up
to 90 percent of the top 20 scored patterns are
verified. We also introduce extended verification from
literature surveys, where the high unified scores
correspond to even higher verification percentage. The
top scored patterns are confirmed to match the known
WRKY binding cores with no available 3D structures and
agree well with the top binding affinities of in vivo
experiments.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tang:2013:MIC,
author = "Yang Tang and Huijun Gao and Jurgen Kurths",
title = "Multiobjective Identification of Controlling Areas in
Neuronal Networks",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "708--720",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.72",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, we investigate the multiobjective
identification of controlling areas in the neuronal
network of a cat's brain by considering two measures of
controllability simultaneously. By utilizing
nondominated sorting mechanisms and composite
differential evolution (CoDE), a reference-point-based
nondominated sorting composite differential evolution
(RP-NSCDE) is developed to tackle the multiobjective
identification of controlling areas in the neuronal
network. The proposed RP-NSCDE shows its promising
performance in terms of accuracy and convergence speed,
in comparison to nondominated sorting genetic
algorithms II. The proposed method is also compared
with other representative statistical methods in the
complex network theory, single objective, and
constraint optimization methods to illustrate its
effectiveness and reliability. It is shown that there
exists a tradeoff between minimizing two objectives,
and therefore Pareto fronts (PFs) can be plotted. The
developed approaches and findings can also be applied
to coordination control of various kinds of real-world
complex networks including biological networks and
social networks, and so on.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Humphries:2013:NT,
author = "Peter J. Humphries and Taoyang Wu",
title = "On the Neighborhoods of Trees",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "721--728",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.66",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Tree rearrangement operations typically induce a
metric on the space of phylogenetic trees. One
important property of these metrics is the size of the
neighborhood, that is, the number of trees exactly one
operation from a given tree. We present an exact
expression for the size of the TBR (tree bisection and
reconnection) neighborhood, thus answering a question
first posed by Allen and Steel. In addition, we also
obtain a characterization of the extremal trees whose
TBR neighborhoods are maximized and minimized.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2013:PCP,
author = "Yijia Zhang and Hongfei Lin and Zhihao Yang and Jian
Wang and Yanpeng Li and Bo Xu",
title = "Protein Complex Prediction in Large Ontology
Attributed Protein-Protein Interaction Networks",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "729--741",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.86",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein complexes are important for unraveling the
secrets of cellular organization and function. Many
computational approaches have been developed to predict
protein complexes in protein-protein interaction (PPI)
networks. However, most existing approaches focus
mainly on the topological structure of PPI networks,
and largely ignore the gene ontology (GO) annotation
information. In this paper, we constructed ontology
attributed PPI networks with PPI data and GO resource.
After constructing ontology attributed networks, we
proposed a novel approach called CSO (clustering based
on network structure and ontology attribute
similarity). Structural information and GO attribute
information are complementary in ontology attributed
networks. CSO can effectively take advantage of the
correlation between frequent GO annotation sets and the
dense subgraph for protein complex prediction. Our
proposed CSO approach was applied to four different
yeast PPI data sets and predicted many well-known
protein complexes. The experimental results showed that
CSO was valuable in predicting protein complexes and
achieved state-of-the-art performance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sengupta:2013:RKO,
author = "Debarka Sengupta and Aroonalok Pyne and Ujjwal Maulik
and Sanghamitra Bandyopadhyay",
title = "Reformulated {Kemeny} Optimal Aggregation with
Application in Consensus Ranking of {microRNA}
Targets",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "742--751",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.74",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "MicroRNAs are very recently discovered small noncoding
RNAs, responsible for negative regulation of gene
expression. Members of this endogenous family of small
RNA molecules have been found implicated in many
genetic disorders. Each microRNA targets tens to
hundreds of genes. Experimental validation of target
genes is a time- and cost-intensive procedure.
Therefore, prediction of microRNA targets is a very
important problem in computational biology. Though,
dozens of target prediction algorithms have been
reported in the past decade, they disagree
significantly in terms of target gene ranking (based on
predicted scores). Rank aggregation is often used to
combine multiple target orderings suggested by
different algorithms. This technique has been used in
diverse fields including social choice theory, meta
search in web, and most recently, in bioinformatics.
Kemeny optimal aggregation (KOA) is considered the more
profound objective for rank aggregation. The consensus
ordering obtained through Kemeny optimal aggregation
incurs minimum pairwise disagreement with the input
orderings. Because of its computational intractability,
heuristics are often formulated to obtain a near
optimal consensus ranking. Unlike its real time use in
meta search, there are a number of scenarios in
bioinformatics (e.g., combining microRNA target
rankings, combining disease-related gene rankings
obtained from microarray experiments) where
evolutionary approaches can be afforded with the
ambition of better optimization. We conjecture that an
ideal consensus ordering should have its total
disagreement shared, as equally as possible, with the
input orderings. This is also important to refrain the
evolutionary processes from getting stuck to local
extremes. In the current work, we reformulate Kemeny
optimal aggregation while introducing a trade-off
between the total pairwise disagreement and its
distribution. A simulated annealing-based
implementation of the proposed objective has been found
effective in context of microRNA target ranking.
Supplementary data and source code link are available
at: {\tt
http://www.isical.ac.in/bioinfo_miu/ieee_tcbb_kemeny.rar}.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wu:2013:SBP,
author = "Jian-Sheng Wu and Zhi-Hua Zhou",
title = "Sequence-Based Prediction of {microRNA}-Binding
Residues in Proteins Using Cost-Sensitive {Laplacian}
Support Vector Machines",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "752--759",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.75",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The recognition of microRNA (miRNA)-binding residues
in proteins is helpful to understand how miRNAs silence
their target genes. It is difficult to use existing
computational method to predict miRNA-binding residues
in proteins due to the lack of training examples. To
address this issue, unlabeled data may be exploited to
help construct a computational model. Semisupervised
learning deals with methods for exploiting unlabeled
data in addition to labeled data automatically to
improve learning performance, where no human
intervention is assumed. In addition, miRNA-binding
proteins almost always contain a much smaller number of
binding than nonbinding residues, and cost-sensitive
learning has been deemed as a good solution to the
class imbalance problem. In this work, a novel model is
proposed for recognizing miRNA-binding residues in
proteins from sequences using a cost-sensitive
extension of Laplacian support vector machines
(CS-LapSVM) with a hybrid feature. The hybrid feature
consists of evolutionary information of the amino acid
sequence (position-specific scoring matrices), the
conservation information about three biochemical
properties (HKM) and mutual interaction propensities in
protein-miRNA complex structures. The CS-LapSVM
receives good performance with an F1 score of $ (26.23
\pm 2.55 \%) $ and an AUC value of $ (0.805 \pm 0.020)
$ superior to existing approaches for the recognition
of RNA-binding residues. A web server called SARS is
built and freely available for academic usage.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Haack:2013:SRL,
author = "Fiete Haack and Kevin Burrage and Ronald Redmer and
Adelinde M. Uhrmacher",
title = "Studying the Role of Lipid Rafts on Protein Receptor
Bindings with Cellular Automata",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "760--770",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.40",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "It is widely accepted that lipid rafts promote
receptor clustering and thereby facilitate signaling
transduction. The role of lipid rafts in inducing and
promoting receptor accumulation within the cell
membrane has been explored by several computational and
experimental studies. However, it remains unclear
whether lipid rafts influence the recruitment and
binding of proteins from the cytosol as well. To
provide an answer to this question a spatial membrane
model has been developed based on cellular automata.
Our results indicate that lipid rafts indeed influence
protein receptor bindings. In particular processes with
slow dissociation and binding kinetics are promoted by
lipid rafts, whereas fast binding processes are
slightly hampered. However, the impact depends on a
variety of parameters, such as the size and mobility of
the lipid rafts, the induced slow down of receptors
within rafts, and also the dissociation and binding
kinetics of the cytosolic proteins. Thus, for any
individual signaling pathway the influence of lipid
rafts on protein binding might be different. To
facilitate analyzing this influence given a specific
pathway, our approach has been generalized into LiRaM,
a modeling and simulation tool for lipid rafts
models.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2013:SAM,
author = "Wei Zhang and Xiufen Zou",
title = "Systematic Analysis of the Mechanisms of
Virus-Triggered {Type I IFN} Signaling Pathways through
Mathematical Modeling",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "771--779",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.31",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Based on biological experimental data, we developed a
mathematical model of the virus-triggered signaling
pathways that lead to induction of type I IFNs and
systematically analyzed the mechanisms of the cellular
antiviral innate immune responses, including the
negative feedback regulation of ISG56 and the positive
feedback regulation of IFNs. We found that the time
between 5 and 48 hours after viral infection is vital
for the control and/or elimination of the virus from
the host cells and demonstrated that the ISG56-induced
inhibition of MITA activation is stronger than the
ISG56-induced inhibition of TBK1 activation. The global
parameter sensitivity analysis suggests that the
positive feedback regulation of IFNs is very important
in the innate antiviral system. Furthermore, the
robustness of the innate immune signaling network was
demonstrated using a new robustness index. These
results can help us understand the mechanisms of the
virus-induced innate immune response at a system level
and provide instruction for further biological
experiments.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hu:2013:UBT,
author = "Allen L. Hu and Keith C. C. Chan",
title = "Utilizing Both Topological and Attribute Information
for Protein Complex Identification in {PPI} Networks",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "780--792",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.37",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Many computational approaches developed to identify
protein complexes in protein-protein interaction (PPI)
networks perform their tasks based only on network
topologies. The attributes of the proteins in the
networks are usually ignored. As protein attributes
within a complex may also be related to each other, we
have developed a PCIA algorithm to take into
consideration both such information and network
topology in the identification process of protein
complexes. Given a PPI network, PCIA first finds
information about the attributes of the proteins in a
PPI network in the Gene Ontology databases and uses
such information for the identification of protein
complexes. PCIA then computes a Degree of Association
measure for each pair of interacting proteins to
quantitatively determine how much their attribute
values associate with each other. Based on this
association measure, PCIA is able to discover dense
graph clusters consisting of proteins whose attribute
values are significantly closer associated with each
other. PCIA has been tested with real data and
experimental results seem to indicate that attributes
of the proteins in the same complex do have some
association with each other and, therefore, that
protein complexes can be more accurately identified
when protein attributes are taken into consideration.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Garcia:2013:GDA,
author = "Sara P. Garcia and Joao M. O. S. Rodrigues and Sergio
Santos and Diogo Pratas and Vera Afreixo and Carlos
Bastos and Paulo J. S. G. Ferreira and Armando J.
Pinho",
title = "A Genomic Distance for Assembly Comparison Based on
Compressed Maximal Exact Matches",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "793--798",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.77",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genome assemblies are typically compared with respect
to their contiguity, coverage, and accuracy. We propose
a genome-wide, alignment-free genomic distance based on
compressed maximal exact matches and suggest adding it
to the benchmark of commonly used assembly quality
metrics. Maximal exact matches are perfect repeats,
without gaps or misspellings, which cannot be further
extended to either their left- or right-end side
without loss of similarity. The genomic distance here
proposed is based on the normalized compression
distance, an information-theoretic measure of the
relative compressibility of two sequences estimated
using multiple finite-context models. This measure
exposes similarities between the sequences, as well as,
the nesting structure underlying the assembly of larger
maximal exact matches from smaller ones. We use four
human genome assemblies for illustration and discuss
the impact of genome sequencing and assembly in the
final content of maximal exact matches and the genomic
distance here proposed.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Goni:2013:NAI,
author = "Osman Goni",
title = "A New Approach to Implement Absorbing Boundary
Condition in Biomolecular Electrostatics",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "799--804",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.96",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper discusses a novel approach to employ the
absorbing boundary condition in conjunction with the
finite-element method (FEM) in biomolecular
electrostatics. The introduction of Bayliss-Turkel
absorbing boundary operators in electromagnetic
scattering problem has been incorporated by few
researchers. However, in the area of biomolecular
electrostatics, this boundary condition has not been
investigated yet. The objective of this paper is
twofold. First, to solve nonlinear Poisson--Boltzmann
equation using Newton's method and second, to find an
efficient and acceptable solution with minimum number
of unknowns. In this work, a Galerkin finite-element
formulation is used along with a Bayliss-Turkel
absorbing boundary operator that explicitly accounts
for the open field problem by mapping the Sommerfeld
radiation condition from the far field to near field.
While the Bayliss-Turkel condition works well when the
artificial boundary is far from the scatterer, an
acceptable tolerance of error can be achieved with the
second order operator. Numerical results on test case
with simple sphere show that the treatment is able to
reach the same level of accuracy achieved by the
analytical method while using a lower grid density.
Bayliss-Turkel absorbing boundary condition (BTABC)
combined with the FEM converges to the exact solution
of scattering problems to within discretization
error.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Milotti:2013:CAB,
author = "Edoardo Milotti and Vladislav Vyshemirsky and Michela
Sega and Sabrina Stella and Federico Dogo and Roberto
Chignola",
title = "Computer-Aided Biophysical Modeling: a Quantitative
Approach to Complex Biological Systems",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "805--810",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.35",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "When dealing with the biophysics of tumors, analytical
and numerical modeling tools have long been regarded as
potentially useful but practically immature tools.
Further developments could not just overturn this
predicament, but lead to completely new perspectives in
biology. Here, we give an account of our own
computational tool and how we have put it to good use,
and we discuss a paradigmatic example to outline a path
to making cell biology more quantitative and
predictive.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ivanov:2013:QPP,
author = "Stefan Ivanov and Ivan Dimitrov and Irina
Doytchinova",
title = "Quantitative Prediction of Peptide Binding to
{HLA-DP1} Protein",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "811--815",
month = may,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.78",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The exogenous proteins are processed by the host
antigen-processing cells. Peptidic fragments of them
are presented on the cell surface bound to the major
hystocompatibility complex (MHC) molecules class II and
recognized by the CD4+ T lymphocytes. The MHC binding
is considered as the crucial prerequisite for T-cell
recognition. Only peptides able to form stable
complexes with the MHC proteins are recognized by the
T-cells. These peptides are known as T-cell epitopes.
All T-cell epitopes are MHC binders, but not all MHC
binders are T-cell epitopes. The T-cell epitope
prediction is one of the main priorities of
immunoinformatics. In the present study, three
chemometric techniques are combined to derive a model
for in silico prediction of peptide binding to the
human MHC class II protein HLA-DP1. The structures of a
set of known peptide binders are described by amino
acid z-descriptors. Data are processed by an iterative
self-consisted algorithm using the method of partial
least squares, and a quantitative matrix (QM) for
peptide binding prediction to HLA-DP1 is derived. The
QM is validated by two sets of proteins and showed an
average accuracy of 86 percent.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2013:CPSb,
author = "Anonymous",
title = "Call for Papers: Special Issue on Software and
Databases in {TCBB}",
journal = j-TCBB,
volume = "10",
number = "3",
pages = "816--816",
month = may,
year = "2013",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Nov 27 16:23:40 MST 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2013:PFP,
author = "Guoxian Yu and Huzefa Rangwala and Carlotta Domeniconi
and Guoji Zhang and Zhiwen Yu",
title = "Protein Function Prediction using Multi-label Ensemble
Classification",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "1--1",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.111",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "High-throughput experimental techniques produce
several heterogeneous proteomic and genomic datasets.
To computationally annotate proteins, it is necessary
and promising to integrate these heterogeneous data
sources. Some methods transform these data sources into
different kernels or feature representations. Next,
these kernels are linearly (or non-linearly) combined
into a composite kernel. The composite kernel is
utilized to develop a predictive model to infer the
function of proteins. A protein can have multiple roles
and functions (or labels). Therefore, multi-label
learning methods are also adapted for protein function
prediction. We develop a transductive multi-label
classifier (TMC) to predict multiple functions of
proteins using several unlabeled proteins. We also
propose a method called transductive multi-label
ensemble classifier (TMEC) for integrating the
different data sources using an ensemble approach. TMEC
trains a graph-based multi-label classifier on each
single data source and then combines the predictions of
the individual classifiers. We use a directed
bi-relational graph to captures three types of
relationships between pairs of proteins, between pairs
of functions, and between proteins and functions. We
evaluate the effectiveness of TMC and TMEC to predict
the functions of proteins on three benchmarks. We show
that our approaches perform better than recently
proposed protein function prediction methods on
composite and multiple kernels.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{deSouto:2013:GES,
author = "Marcilio C. P. de Souto and Maricel Kann",
title = "Guest Editorial for Special Section on {BSB 2012}",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "817--818",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.173",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Feijao:2013:EAF,
author = "Pedro Feijao and Joao Meidanis",
title = "Extending the Algebraic Formalism for Genome
Rearrangements to Include Linear Chromosomes",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "819--831",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.161",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Algebraic rearrangement theory, as introduced by
Meidanis and Dias, focuses on representing the order in
which genes appear in chromosomes, and applies to
circular chromosomes only. By shifting our attention to
genome adjacencies, we introduce the adjacency
algebraic theory, extending the original algebraic
theory to linear chromosomes in a very natural way,
also allowing the original algebraic distance formula
to be used to the general multichromosomal case, with
both linear and circular chromosomes. The resulting
distance, which we call algebraic distance here, is
very similar to, but not quite the same as,
double-cut-and-join distance. We present linear time
algorithms to compute it and to sort genomes. We show
how to compute the rearrangement distance from the
adjacency graph, for an easier comparison with other
rearrangement distances. A thorough discussion on the
relationship between the chromosomal and adjacency
representation is also given, and we show how all
classic rearrangement operations can be modeled using
the algebraic theory.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lorenz:2013:MQR,
author = "Ronny Lorenz and Stephan H. Bernhart and Jing Qin and
Christian Honer zu Siederdissen and Andrea Tanzer and
Fabian Amman and Ivo L. Hofacker and Peter F. Stadler",
title = "{$2$D} Meets {$4$G}: {$G$}-Quadruplexes in {RNA}
Secondary Structure Prediction",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "832--844",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.7",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "G-quadruplexes are abundant locally stable structural
elements in nucleic acids. The combinatorial theory of
RNA structures and the dynamic programming algorithms
for RNA secondary structure prediction are extended
here to incorporate G-quadruplexes using a simple but
plausible energy model. With preliminary energy
parameters, we find that the overwhelming majority of
putative quadruplex-forming sequences in the human
genome are likely to fold into canonical secondary
structures instead. Stable G-quadruplexes are strongly
enriched, however, in the 5{\^E}$^{}^1$UTR of protein
coding mRNAs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jaskowiak:2013:PMC,
author = "Pablo A. Jaskowiak and Ricardo J. G. B. Campello and
Ivan G. Costa Filho",
title = "Proximity Measures for Clustering Gene Expression
Microarray Data: a Validation Methodology and a
Comparative Analysis",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "845--857",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.9",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Cluster analysis is usually the first step adopted to
unveil information from gene expression microarray
data. Besides selecting a clustering algorithm,
choosing an appropriate proximity measure (similarity
or distance) is of great importance to achieve
satisfactory clustering results. Nevertheless, up to
date, there are no comprehensive guidelines concerning
how to choose proximity measures for clustering
microarray data. Pearson is the most used proximity
measure, whereas characteristics of other ones remain
unexplored. In this paper, we investigate the choice of
proximity measures for the clustering of microarray
data by evaluating the performance of 16 proximity
measures in 52 data sets from time course and cancer
experiments. Our results support that measures rarely
employed in the gene expression literature can provide
better results than commonly employed ones, such as
Pearson, Spearman, and Euclidean distance. Given that
different measures stood out for time course and cancer
data evaluations, their choice should be specific to
each scenario. To evaluate measures on time-course
data, we preprocessed and compiled 17 data sets from
the microarray literature in a benchmark along with a
new methodology, called Intrinsic Biological Separation
Ability (IBSA). Both can be employed in future research
to assess the effectiveness of new measures for gene
time-course data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Panja:2013:CLC,
author = "Surajit Panja and Sourav Patra and Anirban Mukherjee
and Madhumita Basu and Sanghamitra Sengupta and Pranab
K. Dutta",
title = "A Closed-Loop Control Scheme for Steering Steady
States of Glycolysis and Glycogenolysis Pathway",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "858--868",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.82",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Biochemical networks normally operate in the
neighborhood of one of its multiple steady states. It
may reach from one steady state to other within a
finite time span. In this paper, a closed-loop control
scheme is proposed to steer states of the glycolysis
and glycogenolysis (GG) pathway from one of its steady
states to other. The GG pathway is modeled in the
synergism and saturation system formalism, known as
S-system. This S-system model is linearized into the
controllable Brunovsky canonical form using a feedback
linearization technique. For closed-loop control, the
linear-quadratic regulator (LQR) and the
linear-quadratic Gaussian (LQG) regulator are invoked
to design a controller for tracking prespecified steady
states. In the feedback linearization technique, a
global diffeomorphism function is proposed that
facilitates in achieving the regulation requirement.
The robustness of the regulated GG pathway is studied
considering input perturbation and with measurement
noise.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ozsoy:2013:DCA,
author = "Oyku Eren Ozsoy and Tolga Can",
title = "A Divide and Conquer Approach for Construction of
Large-Scale Signaling Networks from {PPI} and {RNAi}
Data Using Linear Programming",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "869--883",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.80",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Inference of topology of signaling networks from
perturbation experiments is a challenging problem.
Recently, the inference problem has been formulated as
a reference network editing problem and it has been
shown that finding the minimum number of edit
operations on a reference network to comply with
perturbation experiments is an NP-complete problem. In
this paper, we propose an integer linear optimization
(ILP) model for reconstruction of signaling networks
from RNAi data and a reference network. The ILP model
guarantees the optimal solution; however, is practical
only for small signaling networks of size 10-15 genes
due to computational complexity. To scale for large
signaling networks, we propose a divide and
conquer-based heuristic, in which a given reference
network is divided into smaller subnetworks that are
solved separately and the solutions are merged together
to form the solution for the large network. We validate
our proposed approach on real and synthetic data sets,
and comparison with the state of the art shows that our
proposed approach is able to scale better for large
networks while attaining similar or better biological
accuracy.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nguyen:2013:KBM,
author = "Ken D. Nguyen and Yi Pan",
title = "A Knowledge-Based Multiple-Sequence Alignment
Algorithm",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "884--896",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.102",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A common and cost-effective mechanism to identify the
functionalities, structures, or relationships between
species is multiple-sequence alignment, in which
DNA/RNA/protein sequences are arranged and aligned so
that similarities between sequences are clustered
together. Correctly identifying and aligning these
sequence biological similarities help from unwinding
the mystery of species evolution to drug design. We
present our knowledge-based multiple sequence alignment
(KB-MSA) technique that utilizes the existing knowledge
databases such as SWISSPROT, GENBANK, or HOMSTRAD to
provide a more realistic and reliable sequence
alignment. We also provide a modified version of this
algorithm (CB-MSA) that utilizes the sequence
consistency information when sequence knowledge
databases are not available. Our benchmark tests on
BAliBASE, PREFAB, HOMSTRAD, and SABMARK references show
accuracy improvements up to 10 percent on twilight data
sets against many leading alignment tools such as
ISPALIGN, PADT, CLUSTALW, MAFFT, PROBCONS, and
T-COFFEE.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2013:IAA,
author = "Nan Liu and Haitao Jiang and Daming Zhu and Binhai
Zhu",
title = "An Improved Approximation Algorithm for Scaffold
Filling to Maximize the Common Adjacencies",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "905--913",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.100",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Scaffold filling is a new combinatorial optimization
problem in genome sequencing. The one-sided scaffold
filling problem can be described as given an incomplete
genome $ (I) $ and a complete (reference) genome $ (G)
$, fill the missing genes into $ (I) $ such that the
number of common (string) adjacencies between the
resulting genome $ (I^{\prime }) $ and $ (G) $ is
maximized. This problem is NP-complete for genome with
duplicated genes and the best known approximation
factor is 1.33, which uses a greedy strategy. In this
paper, we prove a better lower bound of the optimal
solution, and devise a new algorithm by exploiting the
maximum matching method and a local improvement
technique, which improves the approximation factor to
1.25. For genome with gene repetitions, this is the
only known NP-complete problem which admits an
approximation with a small constant factor (less than
1.5).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Das:2013:ORS,
author = "Mouli Das and C. A. Murthy and Rajat K. De",
title = "An Optimization Rule for In Silico Identification of
Targeted Overproduction in Metabolic Pathways",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "914--926",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.67",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In an extension of previous work, here we introduce a
second-order optimization method for determining
optimal paths from the substrate to a target product of
a metabolic network, through which the amount of the
target is maximum. An objective function for the said
purpose, along with certain linear constraints, is
considered and minimized. The basis vectors spanning
the null space of the stoichiometric matrix, depicting
the metabolic network, are computed, and their convex
combinations satisfying the constraints are considered
as flux vectors. A set of other constraints,
incorporating weighting coefficients corresponding to
the enzymes in the pathway, are considered. These
weighting coefficients appear in the objective function
to be minimized. During minimization, the values of
these weighting coefficients are estimated and learned.
These values, on minimization, represent an optimal
pathway, depicting optimal enzyme concentrations,
leading to overproduction of the target. The results on
various networks demonstrate the usefulness of the
methodology in the domain of metabolic engineering. A
comparison with the standard gradient descent and the
extreme pathway analysis technique is also performed.
Unlike the gradient descent method, the present method,
being independent of the learning parameter, exhibits
improved results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Luo:2013:ARA,
author = "Chao Luo and Xingyuan Wang",
title = "Algebraic Representation of Asynchronous
Multiple-Valued Networks and Its Dynamics",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "927--938",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.112",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, dynamics of asynchronous
multiple-valued networks (AMVNs) are investigated based
on linear representation. By semitensor product of
matrices, we convert AMVNs into the discrete-time
linear representation. A general formula to calculate
all of network transition matrices of a specific AMVN
is achieved. A necessary and sufficient algebraic
criterion to determine whether a given state belongs to
loose attractors of length $ (s) $ is proposed.
Formulas for the numbers of attractors in AMVNs are
provided. Finally, algorithms are presented to detect
all of the attractors and basins. Examples are shown to
demonstrate the feasibility of the proposed scheme.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Halasz:2013:ASS,
author = "Adam M. Halasz and Hong-Jian Lai and Meghan M. McCabe
and Krishnan Radhakrishnan and Jeremy S. Edwards",
title = "Analytical Solution of Steady-State Equations for
Chemical Reaction Networks with Bilinear Rate Laws",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "957--969",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.41",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "True steady states are a rare occurrence in living
organisms, yet their knowledge is essential for
quasi-steady-state approximations, multistability
analysis, and other important tools in the
investigation of chemical reaction networks (CRN) used
to describe molecular processes on the cellular level.
Here, we present an approach that can provide closed
form steady-state solutions to complex systems,
resulting from CRN with binary reactions and
mass-action rate laws. We map the nonlinear algebraic
problem of finding steady states onto a linear problem
in a higher-dimensional space. We show that the
linearized version of the steady-state equations obeys
the linear conservation laws of the original CRN. We
identify two classes of problems for which complete,
minimally parameterized solutions may be obtained using
only the machinery of linear systems and a judicious
choice of the variables used as free parameters. We
exemplify our method, providing explicit formulae, on
CRN describing signal initiation of two important types
of RTK receptor-ligand systems, VEGF and EGF-ErbB1.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Todor:2013:CTP,
author = "Andrei Todor and Alin Dobra and Tamer Kahveci",
title = "Characterizing the Topology of Probabilistic
Biological Networks",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "970--983",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.108",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Biological interactions are often uncertain events,
that may or may not take place with some probability.
This uncertainty leads to a massive number of
alternative interaction topologies for each such
network. The existing studies analyze the degree
distribution of biological networks by assuming that
all the given interactions take place under all
circumstances. This strong and often incorrect
assumption can lead to misleading results. In this
paper, we address this problem and develop a sound
mathematical basis to characterize networks in the
presence of uncertain interactions. Using our
mathematical representation, we develop a method that
can accurately describe the degree distribution of such
networks. We also take one more step and extend our
method to accurately compute the joint-degree
distributions of node pairs connected by edges. The
number of possible network topologies grows
exponentially with the number of uncertain
interactions. However, the mathematical model we
develop allows us to compute these degree distributions
in polynomial time in the number of interactions. Our
method works quickly even for entire protein-protein
interaction (PPI) networks. It also helps us find an
adequate mathematical model using MLE. We perform a
comparative study of node-degree and joint-degree
distributions in two types of biological networks: the
classical deterministic networks and the more flexible
probabilistic networks. Our results confirm that
power-law and log-normal models best describe degree
distributions for both probabilistic and deterministic
networks. Moreover, the inverse correlation of degrees
of neighboring nodes shows that, in probabilistic
networks, nodes with large number of interactions
prefer to interact with those with small number of
interactions more frequently than expected. We also
show that probabilistic networks are more robust for
node-degree distribution computation than the
deterministic ones. Availability: all the data sets
used, the software implemented and the alignments found
in this paper are available at
http://bioinformatics.cise.ufl.edu/projects/probNet/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Torres-Sanchez:2013:GFG,
author = "Sergio Torres-Sanchez and Nuria Medina-Medina and
Chris Gignoux and Maria del Mar Abad-Grau and Esteban
Gonzalez-Burchard",
title = "{GeneOnEarth}: Fitting Genetic {PC} Plots on the
Globe",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "1009--1016",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.81",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Principal component (PC) plots have become widely used
to summarize genetic variation of individuals in a
sample. The similarity between genetic distance in PC
plots and geographical distance has shown to be quite
impressive. However, in most situations, individual
ancestral origins are not precisely known or they are
heterogeneously distributed; hence, they are hardly
linked to a geographical area. We have developed
GeneOnEarth, a user-friendly web-based tool to help
geneticists to understand whether a linear
isolation-by-distance model may apply to a genetic data
set; thus, genetic distances among a set of individuals
resemble geographical distances among their origins.
Its main goal is to allow users to first apply a
by-view Procrustes method to visually learn whether
this model holds. To do that, the user can choose the
exact geographical area from an on line 2D or 3D world
map by using, respectively, Google Maps or Google
Earth, and rotate, flip, and resize the images.
GeneOnEarth can also compute the optimal rotation angle
using Procrustes analysis and assess statistical
evidence of similarity when a different rotation angle
has been chosen by the user. An online version of
GeneOnEarth is available for testing and using purposes
at http://bios.ugr.es/GeneOnEarth.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhu:2013:IDB,
author = "Yuan Zhu and Weiqiang Zhou and Dao-Qing Dai and Hong
Yan",
title = "Identification of {DNA}-Binding and Protein-Binding
Proteins Using Enhanced Graph Wavelet Features",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "1017--1031",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.117",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Interactions between biomolecules play an essential
role in various biological processes. For predicting
DNA-binding or protein-binding proteins, many
machine-learning-based techniques have used various
types of features to represent the interface of the
complexes, but they only deal with the properties of a
single atom in the interface and do not take into
account the information of neighborhood atoms directly.
This paper proposes a new feature representation method
for biomolecular interfaces based on the theory of
graph wavelet. The enhanced graph wavelet features
(EGWF) provides an effective way to characterize
interface feature through adding physicochemical
features and exploiting a graph wavelet formulation.
Particularly, graph wavelet condenses the information
around the center atom, and thus enhances the
discrimination of features of biomolecule binding
proteins in the feature space. Experiment results show
that EGWF performs effectively for predicting
DNA-binding and protein-binding proteins in terms of
Matthew's correlation coefficient (MCC) score and the
area value under the receiver operating characteristic
curve (AUC).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Angione:2013:POO,
author = "Claudio Angione and Giovanni Carapezza and Jole
Costanza and Pietro Lio and Giuseppe Nicosia",
title = "{Pareto} Optimality in Organelle Energy Metabolism
Analysis",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "1032--1044",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.95",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In low and high eukaryotes, energy is collected or
transformed in compartments, the organelles. The rich
variety of size, characteristics, and density of the
organelles makes it difficult to build a general
picture. In this paper, we make use of the Pareto-front
analysis to investigate the optimization of energy
metabolism in mitochondria and chloroplasts. Using the
Pareto optimality principle, we compare models of
organelle metabolism on the basis of single- and
multiobjective optimization, approximation techniques
(the Bayesian Automatic Relevance Determination),
robustness, and pathway sensitivity analysis. Finally,
we report the first analysis of the metabolic model for
the hydrogenosome of \bioname{Trichomonas vaginalis}, which
is found in several protozoan parasites. Our analysis
has shown the importance of the Pareto optimality for
such comparison and for insights into the evolution of
the metabolism from cytoplasmic to organelle bound,
involving a model order reduction. We report that
Pareto fronts represent an asymptotic analysis useful
to describe the metabolism of an organism aimed at
maximizing concurrently two or more metabolite
concentrations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Requeno:2013:TLP,
author = "Jose Ignacio Requeno and Gregorio de Miguel Casado and
Roberto Blanco and Jose Manuel Colom",
title = "Temporal Logics for Phylogenetic Analysis via Model
Checking",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "1058--1070",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.87",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The need for general-purpose algorithms for studying
biological properties in phylogenetics motivates
research into formal verification frameworks.
Researchers can focus their efforts exclusively on
evolution trees and property specifications. To this
end, model checking, a mature automated verification
technique originating in computer science, is applied
to phylogenetic analysis. Our approach is based on
three cornerstones: a logical modeling of the evolution
with transition systems; the specification of both
phylogenetic properties and trees using flexible
temporal logic formulas; and the verification of the
latter by means of automated computer tools. The most
conspicuous result is the inception of a formal
framework which allows for a symbolic manipulation of
biological data (based on the codification of the
taxa). Additionally, different logical models of
evolution can be considered, complex properties can be
specified in terms of the logical composition of
others, and the refinement of unfulfilled properties as
well as the discovery of new properties can be
undertaken by exploiting the verification results. Some
experimental results using a symbolic model verifier
support the feasibility of the approach.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rajabli:2013:ADS,
author = "Farid Rajabli and Unal Goktas and Gul Inan",
title = "Application of {Dempster--Schafer} Method in
Family-Based Association Studies",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "1071--1075",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.85",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In experiments designed for family-based association
studies, methods such as transmission disequilibrium
test require large number of trios to identify
single-nucleotide polymorphisms associated with the
disease. However, unavailability of a large number of
trios is the Achilles' heel of many complex diseases,
especially for late-onset diseases. In this paper, we
propose a novel approach to this problem by means of
the Dempster-Shafer method. The simulation studies show
that the Dempster-Shafer method has a promising overall
performance, in identifying single-nucleotide
polymorphisms in the correct association class, as it
has 90 percent accuracy even with 60 trios.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gordon:2013:HWP,
author = "Kevaughn Gordon and Eric Ford and Katherine {St.
John}",
title = "{Hamiltonian} Walks of Phylogenetic Treespaces",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "1076--1079",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.105",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We answer Bryant's combinatorial challenge on minimal
walks of phylogenetic treespace under the
nearest-neighbor interchange (NNI) metric. We show that
the shortest path through the NNI-treespace of $ (n)
$-leaf trees is Hamiltonian for all $ (n) $. That is,
there is a minimal path that visits all binary trees
exactly once, under NNI moves.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2013:HCH,
author = "Tianwei Yu and Hesen Peng",
title = "Hierarchical Clustering of High-Throughput Expression
Data Based on General Dependences",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "1080--1085",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.99",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "High-throughput expression technologies, including
gene expression array and liquid chromatography--mass
spectrometry (LC-MS) and so on, measure thousands of
features, i.e., genes or metabolites, on a continuous
scale. In such data, both linear and nonlinear
relations exist between features. Nonlinear relations
can reflect critical regulation patterns in the
biological system. However, they are not identified and
utilized by traditional clustering methods based on
linear associations. Clustering based on general
dependences, i.e., both linear and nonlinear relations,
is hampered by the high dimensionality and high noise
level of the data. We developed a sensitive
nonparametric measure of general dependence between
(groups of) random variables in high dimensions. Based
on this dependence measure, we developed a hierarchical
clustering method. In simulation studies, the method
outperformed correlation- and mutual information
(MI)-based hierarchical clustering methods in
clustering features with nonlinear dependences. We
applied the method to a microarray data set measuring
the gene expression in cell-cycle time series to show
it generates biologically relevant results. The R code
is available at
http://userwww.service.emory.edu/~tyu8/GDHC.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2013:CPSc,
author = "Anonymous",
title = "Call for Papers: Special Issue on Software and
Databases in {TCBB}",
journal = j-TCBB,
volume = "10",
number = "4",
pages = "1086--1086",
month = jul,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.154",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:33:59 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2013:NBC,
author = "Kuan-Liang Liu and Tzu-Tsung Wong",
title = "Na{\"\i}ve {Bayesian} Classifiers with Multinomial
Models for {rRNA} Taxonomic Assignment",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1--1",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.114",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The introduction of next generation sequencing in
ecological studies has created a major revolution in
microbial and fungal ecology. Direct sequencing of
hypervariable regions from ribosomal RNA genes can
provide rapid and inexpensive analysis for ecological
communities. In order to get deep understanding from
these rRNA fragments, the Ribosomal Database Project
developed the 'RDP Classifier' utilizing 8-mer
nucleotide frequencies with Bayesian theorem to obtain
taxonomy affiliation. The classifier is computationally
efficient and works well with massive short sequences.
However, the binary model employed in the RDP
classifier does not consider the repetitive 8-mers in
each reference sequence. Previous studies have pointed
out that multinomial model usually results a better
performance than binary model. In this study, we
present the na{\"\i}ve Bayesian classifiers with
multinomial models that take repetitive 8-mers into
account for classifying microbial 16S and fungal 28S
rRNA sequences. The results obtained from the
multinomial approach were compared with those obtained
from the binomial RDP classifier by 250-bp, 400-bp,
800-bp, and full-length reads to demonstrate that the
multinomial approach can generally achieve a higher
prediction accuracy in most hypervariable regions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kahveci:2013:GEA,
author = "Tamer Kahveci and Mona Singh",
title = "Guest Editorial for {ACM BCB}",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1089--1090",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.182",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kuroshu:2013:NCP,
author = "Reginaldo M. Kuroshu",
title = "Nonoverlapping Clone Pooling for High-Throughput
Sequencing",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1091--1097",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.83",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Simultaneously sequencing multiple clones using
second-generation sequencers can speed up many
essential clone-based sequencing methods. However, in
applications such as fosmid clone sequencing and
full-length cDNA sequencing, it is important to create
pools of clones that do not overlap on the genome for
the identification of structural variations and
alternatively spliced transcripts, respectively. We
define the nonoverlapping clone pooling problem and
provide practical solutions based on optimal graph
coloring and bin-packing algorithms with constant
absolute worst-case ratios, and further extend them to
cope with repetitive mappings. Using theoretical
analysis and experiments, we also show that the
proposed methods are applicable.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hossain:2013:IMS,
author = "K. S. M. Tozammel Hossain and Debprakash Patnaik and
Srivatsan Laxman and Prateek Jain and Chris
Bailey-Kellogg and Naren Ramakrishnan",
title = "Improved Multiple Sequence Alignments Using Coupled
Pattern Mining",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1098--1112",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.36",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present alignment refinement by mining coupled
residues (ARMiCoRe), a novel approach to a classical
bioinformatics problem, viz., multiple sequence
alignment (MSA) of gene and protein sequences. Aligning
multiple biological sequences is a key step in
elucidating evolutionary relationships, annotating
newly sequenced segments, and understanding the
relationship between biological sequences and
functions. Classical MSA algorithms are designed to
primarily capture conservations in sequences whereas
couplings, or correlated mutations, are well known as
an additional important aspect of sequence evolution.
(Two sequence positions are coupled when mutations in
one are accompanied by compensatory mutations in
another). As a result, better exposition of couplings
is sometimes one of the reasons for hand-tweaking of
MSAs by practitioners. ARMiCoRe introduces a distinctly
pattern mining approach to improving MSAs: using
frequent episode mining as a foundational basis, we
define the notion of a coupled pattern and demonstrate
how the discovery and tiling of coupled patterns using
a max-flow approach can yield MSAs that are better than
conservation-based alignments. Although we were
motivated to improve MSAs for the sake of better
exposing couplings, we demonstrate that our MSAs are
also improvements in terms of traditional metrics of
assessment. We demonstrate the effectiveness of
ARMiCoRe on a large collection of data sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rahman:2013:REM,
author = "Ahsanur Rahman and Christopher L. Poirel and David J.
Badger and Craig Estep and T. M. Murali",
title = "Reverse Engineering Molecular Hypergraphs",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1113--1124",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.71",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Analysis of molecular interaction networks is
pervasive in systems biology. This research relies
almost entirely on graphs for modeling interactions.
However, edges in graphs cannot represent multiway
interactions among molecules, which occur very often
within cells. Hypergraphs may be better representations
for networks having such interactions, since hyperedges
can naturally represent relationships among multiple
molecules. Here, we propose using hypergraphs to
capture the uncertainty inherent in reverse engineering
gene-gene networks. Some subsets of nodes may induce
highly varying subgraphs across an ensemble of networks
inferred by a reverse engineering algorithm. We provide
a novel formulation of hyperedges to capture this
uncertainty in network topology. We propose a
clustering-based approach to discover hyperedges. We
show that our approach can recover hyperedges planted
in synthetic data sets with high precision and recall,
even for moderate amount of noise. We apply our
techniques to a data set of pathways inferred from
genetic interaction data in S. cerevisiae related to
the unfolded protein response. Our approach discovers
several hyperedges that capture the uncertain
connectivity of genes in relevant protein complexes,
suggesting that further experiments may be required to
precisely discern their interaction patterns. We also
show that these complexes are not discovered by an
algorithm that computes frequent and dense subgraphs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Caglar:2013:SMS,
author = "Mehmet Umut Caglar and Ranadip Pal",
title = "Stochastic Model Simulation Using {Kronecker} Product
Analysis and {Zassenhaus} Formula Approximation",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1125--1136",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.34",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Probabilistic Models are regularly applied in Genetic
Regulatory Network modeling to capture the stochastic
behavior observed in the generation of biological
entities such as mRNA or proteins. Several approaches
including Stochastic Master Equations and Probabilistic
Boolean Networks have been proposed to model the
stochastic behavior in genetic regulatory networks. It
is generally accepted that Stochastic Master Equation
is a fundamental model that can describe the system
being investigated in fine detail, but the application
of this model is computationally enormously expensive.
On the other hand, Probabilistic Boolean Network
captures only the coarse-scale stochastic properties of
the system without modeling the detailed interactions.
We propose a new approximation of the stochastic master
equation model that is able to capture the finer
details of the modeled system including bistabilities
and oscillatory behavior, and yet has a significantly
lower computational complexity. In this new method, we
represent the system using tensors and derive an
identity to exploit the sparse connectivity of
regulatory targets for complexity reduction. The
algorithm involves an approximation based on Zassenhaus
formula to represent the exponential of a sum of
matrices as product of matrices. We derive upper bounds
on the expected error of the proposed model
distribution as compared to the stochastic master
equation model distribution. Simulation results of the
application of the model to four different biological
benchmark systems illustrate performance comparable to
detailed stochastic master equation models but with
considerably lower computational complexity. The
results also demonstrate the reduced complexity of the
new approach as compared to commonly used Stochastic
Simulation Algorithm for equivalent accuracy.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tsai:2013:CBM,
author = "Ming-Chi Tsai and Guy E. Blelloch and R. Ravi and
Russell Schwartz",
title = "Coalescent-Based Method for Learning Parameters of
Admixture Events from Large-Scale Genetic Variation
Data",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1137--1149",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.98",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Detecting and quantifying the timing and the genetic
contributions of parental populations to a hybrid
population is an important but challenging problem in
reconstructing evolutionary histories from genetic
variation data. With the advent of high throughput
genotyping technologies, new methods suitable for
large-scale data are especially needed. Furthermore,
existing methods typically assume the assignment of
individuals into subpopulations is known, when that
itself is a difficult problem often unresolved for real
data. Here, we propose a novel method that combines
prior work for inferring nonreticulate population
structures with an MCMC scheme for sampling over
admixture scenarios to both identify population
assignments and learn divergence times and admixture
proportions for those populations using genome-scale
admixed genetic variation data. We validated our method
using coalescent simulations and a collection of real
bovine and human variation data. On simulated
sequences, our methods show better accuracy and faster
runtime than leading competitive methods in estimating
admixture fractions and divergence times. Analysis on
the real data further shows our methods to be effective
at matching our best current knowledge about the
relevant populations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tataw:2013:QAL,
author = "Oben M. Tataw and Gonehal Venugopala Reddy and Eamonn
J. Keogh and Amit K. Roy-Chowdhury",
title = "Quantitative Analysis of Live-Cell Growth at the Shoot
Apex of \bioname{Arabidopsis thaliana}: Algorithms for
Feature Measurement and Temporal Alignment",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1150--1161",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.64",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Study of the molecular control of organ growth
requires establishment of the causal relationship
between gene expression and cell behaviors. We seek to
understand this relationship at the shoot apical
meristem (SAM) of model plant \bioname{Arabidopsis
thaliana}. This requires the spatial mapping and
temporal alignment of different functional domains into
a single template. Live-cell imaging techniques allow
us to observe real-time organ primordia growth and gene
expression dynamics at cellular resolution. In this
paper, we propose a framework for the measurement of
growth features at the 3D reconstructed surface of
organ primordia, as well as algorithms for robust time
alignment of primordia. We computed areas and
deformation values from reconstructed 3D surfaces of
individual primordia from live-cell imaging data. Based
on these growth measurements, we applied a multiple
feature landscape matching (LAM-M) algorithm to ensure
a reliable temporal alignment of multiple primordia.
Although the original landscape matching (LAM)
algorithm motivated our alignment approach, it
sometimes fails to properly align growth curves in the
presence of high noise/distortion. To overcome this
shortcoming, we modified the cost function to consider
the landscape of the corresponding growth features. We
also present an alternate parameter-free growth
alignment algorithm which performs as well as LAM-M for
high-quality data, but is more robust to the presence
of outliers or noise. Results on primordia and guppy
evolutionary growth data show that the proposed
alignment framework performs at least as well as the
LAM algorithm in the general case, and significantly
better in the case of increased noise.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Molloy:2013:PSE,
author = "Kevin Molloy and Sameh Saleh and Amarda Shehu",
title = "Probabilistic Search and Energy Guidance for Biased
Decoy Sampling in Ab Initio Protein Structure
Prediction",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1162--1175",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.29",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Adequate sampling of the conformational space is a
central challenge in ab initio protein structure
prediction. In the absence of a template structure, a
conformational search procedure guided by an energy
function explores the conformational space, gathering
an ensemble of low-energy decoy conformations. If the
sampling is inadequate, the native structure may be
missed altogether. Even if reproduced, a subsequent
stage that selects a subset of decoys for further
structural detail and energetic refinement may discard
near-native decoys if they are high energy or
insufficiently represented in the ensemble. Sampling
should produce a decoy ensemble that facilitates the
subsequent selection of near-native decoys. In this
paper, we investigate a robotics-inspired framework
that allows directly measuring the role of energy in
guiding sampling. Testing demonstrates that a soft
energy bias steers sampling toward a diverse decoy
ensemble less prone to exploiting energetic artifacts
and thus more likely to facilitate retainment of
near-native conformations by selection techniques. We
employ two different energy functions, the associative
memory Hamiltonian with water and Rosetta. Results show
that enhanced sampling provides a rigorous testing of
energy functions and exposes different deficiencies in
them, thus promising to guide development of more
accurate representations and energy functions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huang:2013:EEO,
author = "Yao-ming Huang and Chris Bystroff",
title = "Expanded Explorations into the Optimization of an
Energy Function for Protein Design",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1176--1187",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.113",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Nature possesses a secret formula for the energy as a
function of the structure of a protein. In protein
design, approximations are made to both the structural
representation of the molecule and to the form of the
energy equation, such that the existence of a general
energy function for proteins is by no means guaranteed.
Here, we present new insights toward the application of
machine learning to the problem of finding a general
energy function for protein design. Machine learning
requires the definition of an objective function, which
carries with it the implied definition of success in
protein design. We explored four functions, consisting
of two functional forms, each with two criteria for
success. Optimization was carried out by a Monte Carlo
search through the space of all variable parameters.
Cross-validation of the optimized energy function
against a test set gave significantly different results
depending on the choice of objective function, pointing
to relative correctness of the built-in assumptions.
Novel energy cross terms correct for the observed
nonadditivity of energy terms and an imbalance in the
distribution of predicted amino acids. This paper
expands on the work presented at the 2012 ACM-BCB.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Blumenthal:2013:IIR,
author = "Seth Blumenthal and Yisheng Tang and Wenjie Yang and
Brian Chen",
title = "Isolating Influential Regions of Electrostatic
Focusing in Protein and {DNA} Structure",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1188--1198",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.124",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Electrostatic focusing is a general phenomenon that
occurs in cavities and grooves on the molecular surface
of biomolecules. Narrow surface features can partially
shield charged atoms from the high-dielectric solvent,
enhancing electrostatic potentials inside the cavity
and projecting electric field lines outward into the
solvent. This effect has been observed in many
instances and is widely considered in the human
examination of molecular structure, but it is rarely
integrated into the digital representations used in
protein structure comparison software. To create a
computational representation of electrostatic focusing,
that is compatible with structure comparison
algorithms, this paper presents an approach that
generates three-dimensional solids that approximate
regions where focusing occurs. We verify the accuracy
of this representation against instances of focusing in
proteins and DNA. Noting that this representation also
identifies thin focusing regions on the molecular
surface that are unlikely to affect binding, we
describe a second algorithm that conservatively
isolates larger focusing regions. The resulting 3D
solids can be compared with Boolean set operations,
permitting a new range of analyses on the regions where
electrostatic focusing occurs. They also represent a
novel integration of molecular shape and electrostatic
focusing into the same structure comparison
framework.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kuksa:2013:BSC,
author = "Pavel P. Kuksa",
title = "Biological Sequence Classification with Multivariate
String Kernels",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1201--1210",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.15",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "String kernel-based machine learning methods have
yielded great success in practical tasks of
structured/sequential data analysis. They often exhibit
state-of-the-art performance on many practical tasks of
sequence analysis such as biological sequence
classification, remote homology detection, or protein
superfamily and fold prediction. However, typical
string kernel methods rely on the analysis of discrete
1D string data (e.g., DNA or amino acid sequences). In
this paper, we address the multiclass biological
sequence classification problems using multivariate
representations in the form of sequences of features
vectors (as in biological sequence profiles, or
sequences of individual amino acid physicochemical
descriptors) and a class of multivariate string kernels
that exploit these representations. On three protein
sequence classification tasks, the proposed
multivariate representations and kernels show
significant 15-20 percent improvements compared to
existing state-of-the-art sequence classification
methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bleik:2013:TCB,
author = "Said Bleik and Meenakshi Mishra and Jun Huan and Min
Song",
title = "Text Categorization of Biomedical Data Sets Using
Graph Kernels and a Controlled Vocabulary",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1211--1217",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.16",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recently, graph representations of text have been
showing improved performance over conventional
bag-of-words representations in text categorization
applications. In this paper, we present a graph-based
representation for biomedical articles and use graph
kernels to classify those articles into high-level
categories. In our representation, common biomedical
concepts and semantic relationships are identified with
the help of an existing ontology and are used to build
a rich graph structure that provides a consistent
feature set and preserves additional semantic
information that could improve a classifier's
performance. We attempt to classify the graphs using
both a set-based graph kernel that is capable of
dealing with the disconnected nature of the graphs and
a simple linear kernel. Finally, we report the results
comparing the classification performance of the kernel
classifiers to common text-based classifiers.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yan:2013:CNE,
author = "Su Yan and W. Scott Spangler and Ying Chen",
title = "Chemical Name Extraction Based on Automatic Training
Data Generation and Rich Feature Set",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1218--1233",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.101",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The automation of extracting chemical names from text
has significant value to biomedical and life science
research. A major barrier in this task is the
difficulty of getting a sizable and good quality data
to train a reliable entity extraction model. Another
difficulty is the selection of informative features of
chemical names, since comprehensive domain knowledge on
chemistry nomenclature is required. Leveraging random
text generation techniques, we explore the idea of
automatically creating training sets for the task of
chemical name extraction. Assuming the availability of
an incomplete list of chemical names, called a
dictionary, we are able to generate well-controlled,
random, yet realistic chemical-like training documents.
We statistically analyze the construction of chemical
names based on the incomplete dictionary, and propose a
series of new features, without relying on any domain
knowledge. Compared to state-of-the-art models learned
from manually labeled data and domain knowledge, our
solution shows better or comparable results in
annotating real-world data with less human effort.
Moreover, we report an interesting observation about
the language for chemical names. That is, both the
structural and semantic components of chemical names
follow a Zipfian distribution, which resembles many
natural languages.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Florea:2013:GGT,
author = "Liliana D. Florea and Steven L. Salzberg",
title = "Genome-Guided Transcriptome Assembly in the Age of
Next-Generation Sequencing",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1234--1240",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.140",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Next generation sequencing technologies provide
unprecedented power to explore the repertoire of genes
and their alternative splice variants, collectively
defining the transcriptome of a species in great
detail. However, assembling the short reads into
full-length gene and transcript models presents
significant computational challenges. We review current
algorithms for assembling transcripts and genes from
next generation sequencing reads aligned to a reference
genome, and lay out areas for future improvements.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Shakya:2013:AWL,
author = "Devendra K. Shakya and Rajiv Saxena and Sanjeev N.
Sharma",
title = "An Adaptive Window Length Strategy for Eukaryotic
{CDS} Prediction",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1241--1252",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.76",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Signal processing-based algorithms for identification
of coding sequences (CDS) in eukaryotes are non-data
driven and exploit the presence of three-base
periodicity in these regions for their detection.
Three-base periodicity is commonly detected using short
time Fourier transform (STFT) that uses a window
function of fixed length. As the length of the protein
coding and noncoding regions varies widely, the
identification accuracy of STFT-based algorithms is
poor. In this paper, a novel signal processing-based
algorithm is developed by enabling the window length
adaptation in STFT of DNA sequences for improving the
identification of three-base periodicity. The length of
the window function has been made adaptive in coding
regions to maximize the magnitude of period-3 measure,
whereas in the noncoding regions, the window length is
tailored to minimize this measure. Simulation results
on bench mark data sets demonstrate the advantage of
this algorithm when compared with other non-data-driven
methods for CDS prediction.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rosenberg:2013:CHC,
author = "Noah A. Rosenberg",
title = "Coalescent Histories for Caterpillar-Like Families",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1253--1262",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.123",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A coalescent history is an assignment of branches of a
gene tree to branches of a species tree on which
coalescences in the gene tree occur. The number of
coalescent histories for a pair consisting of a labeled
gene tree topology and a labeled species tree topology
is important in gene tree probability computations, and
more generally, in studying evolutionary possibilities
for gene trees on species trees. Defining the $ (T_r)
$-caterpillar-like family as a sequence of $ (n)
$-taxon trees constructed by replacing the $ (r)
$-taxon subtree of $ (n) $-taxon caterpillars by a
specific $ (r) $-taxon labeled topology $ (T_r) $, we
examine the number of coalescent histories for
caterpillar-like families with matching gene tree and
species tree labeled topologies. For each $ (T_r) $
with size $ (r \le 8) $, we compute the number of
coalescent histories for $ (n) $-taxon trees in the $
(T_r) $-caterpillar-like family. Next, as $ (n
\rightarrow \infty) $, we find that the limiting ratio
of the numbers of coalescent histories for the $ (T_r)
$ family and caterpillars themselves is correlated with
the number of labeled histories for $ (T_r) $. The
results support a view that large numbers of coalescent
histories occur when a tree has both a relatively
balanced subtree and a high tree depth, contributing to
deeper understanding of the combinatorics of gene trees
and species trees.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wandelt:2013:FRC,
author = "Sebastian Wandelt and Ulf Leser",
title = "{FRESCO}: Referential Compression of Highly Similar
Sequences",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1275--1288",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.122",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In many applications, sets of similar texts or
sequences are of high importance. Prominent examples
are revision histories of documents or genomic
sequences. Modern high-throughput sequencing
technologies are able to generate DNA sequences at an
ever-increasing rate. In parallel to the decreasing
experimental time and cost necessary to produce DNA
sequences, computational requirements for analysis and
storage of the sequences are steeply increasing.
Compression is a key technology to deal with this
challenge. Recently, referential compression schemes,
storing only the differences between a to-be-compressed
input and a known reference sequence, gained a lot of
interest in this field. In this paper, we propose a
general open-source framework to compress large amounts
of biological sequence data called Framework for
REferential Sequence COmpression (FRESCO). Our basic
compression algorithm is shown to be one to two orders
of magnitudes faster than comparable related work,
while achieving similar compression ratios. We also
propose several techniques to further increase
compression ratios, while still retaining the advantage
in speed: (1) selecting a good reference sequence; and
(2) rewriting a reference sequence to allow for better
compression. In addition, we propose a new way of
further boosting the compression ratios by applying
referential compression to already referentially
compressed files (second-order compression). This
technique allows for compression ratios way beyond
state of the art, for instance, 4,000:1 and higher for
human genomes. We evaluate our algorithms on a large
data set from three different species (more than 1,000
genomes, more than 3 TB) and on a collection of
versions of Wikipedia pages. Our results show that
real-time compression of highly similar sequences at
high compression ratios is possible on modern
hardware.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{AlNasr:2013:IBS,
author = "Kamal {Al Nasr} and Chunmei Liu and Mugizi Rwebangira
and Legand Burge and Jing He",
title = "Intensity-Based Skeletonization of {CryoEM} Gray-Scale
Images Using a True Segmentation-Free Algorithm",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1289--1298",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.121",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Cryo-electron microscopy is an experimental technique
that is able to produce 3D gray-scale images of protein
molecules. In contrast to other experimental
techniques, cryo-electron microscopy is capable of
visualizing large molecular complexes such as viruses
and ribosomes. At medium resolution, the positions of
the atoms are not visible and the process cannot
proceed. The medium-resolution images produced by
cryo-electron microscopy are used to derive the atomic
structure of the proteins in de novo modeling. The
skeletons of the 3D gray-scale images are used to
interpret important information that is helpful in de
novo modeling. Unfortunately, not all features of the
image can be captured using a single segmentation. In
this paper, we present a segmentation-free approach to
extract the gray-scale curve-like skeletons. The
approach relies on a novel representation of the 3D
image, where the image is modeled as a graph and a set
of volume trees. A test containing 36 synthesized maps
and one authentic map shows that our approach can
improve the performance of the two tested tools used in
de novo modeling. The improvements were 62 and 13
percent for Gorgon and DP-TOSS, respectively.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chuang:2013:OPU,
author = "Li-Yeh Chuang and Cheng-Huei Yang and Jui-Hung Tsai
and Cheng-Hong Yang",
title = "Operon Prediction Using Chaos Embedded Particle Swarm
Optimization",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1299--1309",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.63",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Operons contain valuable information for drug design
and determining protein functions. Genes within an
operon are co-transcribed to a single-strand mRNA and
must be coregulated. The identification of operons is,
thus, critical for a detailed understanding of the gene
regulations. However, currently used experimental
methods for operon detection are generally difficult to
implement and time consuming. In this paper, we propose
a chaotic binary particle swarm optimization (CBPSO) to
predict operons in bacterial genomes. The intergenic
distance, participation in the same metabolic pathway
and the cluster of orthologous groups (COG) properties
of the Escherichia coli genome are used to design a
fitness function. Furthermore, the Bacillus subtilis,
Pseudomonas aeruginosa PA01, Staphylococcus aureus and
Mycobacterium tuberculosis genomes are tested and
evaluated for accuracy, sensitivity, and specificity.
The computational results indicate that the proposed
method works effectively in terms of enhancing the
performance of the operon prediction. The proposed
method also achieved a good balance between sensitivity
and specificity when compared to methods from the
literature.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zarai:2013:EES,
author = "Yoram Zarai and Michael Margaliot and Tamir Tuller",
title = "Explicit Expression for the Steady-State Translation
Rate in the Infinite-Dimensional Homogeneous Ribosome
Flow Model",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1322--1328",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.120",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gene translation is a central stage in the
intracellular process of protein synthesis. Gene
translation proceeds in three major stages: initiation,
elongation, and termination. During the elongation
step, ribosomes (intracellular macromolecules) link
amino acids together in the order specified by
messenger RNA (mRNA) molecules. The homogeneous
ribosome flow model (HRFM) is a mathematical model of
translation-elongation under the assumption of constant
elongation rate along the mRNA sequence. The HRFM
includes $ (n) $ first-order nonlinear ordinary
differential equations, where $ (n) $ represents the
length of the mRNA sequence, and two positive
parameters: ribosomal initiation rate and the
(constant) elongation rate. Here, we analyze the HRFM
when $ (n) $ goes to infinity and derive a simple
expression for the steady-state protein synthesis rate.
We also derive bounds that show that the behavior of
the HRFM for finite, and relatively small, values of $
(n) $ is already in good agreement with the closed-form
result in the infinite-dimensional case. For example,
for $ (n = 15) $, the relative error is already less
than 4 percent. Our results can, thus, be used in
practice for analyzing the behavior of
finite-dimensional HRFMs that model translation. To
demonstrate this, we apply our approach to estimate the
mean initiation rate in M. musculus, finding it to be
around 0.17 codons per second.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bach:2013:SND,
author = "Christian Bach and Prabir Patra and Jani M. Pallis and
William Sherman and Hassan Bajwa",
title = "Strategy for Naturelike Designer Transcription Factors
with Reduced Toxicity",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1340--1343",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.107",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "For clinical applications, the biological functions of
DNA-binding proteins require that they interact with
their target binding site with high affinity and
specificity. Advances in randomized production and
target-oriented selection of engineered artificial
DNA-binding domains incited a rapidly expanding field
of designer transcription factors (TFs). Engineered
transcription factors are used in zinc-finger nuclease
(ZFN) technology that allows targeted genome editing.
Zinc-finger-binding domains fabricated by modular
assembly display an unexpectedly high failure rate
having either a lack of activity as ZFNs in human cells
or activity at ``off-target EUR' binding sites on the
human genome causing cell death. To address these
shortcomings, we created new binding domains using a
targeted modification strategy. We produced two SP1
mutants by exchanging amino acid residues in the
alpha-helical region of the transcription factor SP1.
We identified their best target binding sites and
searched the NCBI HuRef genome for matches of the
nine-base-pair consensus binding site of SP1 and the
best binding sites of its mutants. Our research
concludes that we can alter the binding preference of
existing zinc-finger domains without altering its
biological functionalities.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2013:IOA,
author = "Anonymous",
title = "{IEEE} Open Access Publishing",
journal = j-TCBB,
volume = "10",
number = "5",
pages = "1344--1344",
month = sep,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.183",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Jan 9 15:34:03 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mandoiu:2013:GEI,
author = "Ion I. Mandoiu and Jianxin Wang and Alexander
Zelikovsky",
title = "{Guest Editors}' Introduction to the Special Section
on Bioinformatics Research and Applications",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1345--1346",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.189",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2013:RTR,
author = "Xi Chen and Jianhua Xuan and Chen Wang and Ayesha N.
Shajahan and Rebecca B. Riggins and Robert Clarke",
title = "Reconstruction of Transcriptional Regulatory Networks
by Stability-Based Network Component Analysis",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1347--1358",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.146",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Reliable inference of transcription regulatory
networks is a challenging task in computational
biology. Network component analysis (NCA) has become a
powerful scheme to uncover regulatory networks behind
complex biological processes. However, the performance
of NCA is impaired by the high rate of false
connections in binding information. In this paper, we
integrate stability analysis with NCA to form a novel
scheme, namely stability-based NCA (sNCA), for
regulatory network identification. The method mainly
addresses the inconsistency between gene expression
data and binding motif information. Small perturbations
are introduced to prior regulatory network, and the
distance among multiple estimated transcript factor
(TF) activities is computed to reflect the stability
for each TF's binding network. For target gene
identification, multivariate regression and t-statistic
are used to calculate the significance for each TF-gene
connection. Simulation studies are conducted and the
experimental results show that sNCA can achieve an
improved and robust performance in TF identification as
compared to NCA. The approach for target gene
identification is also demonstrated to be suitable for
identifying true connections between TFs and their
target genes. Furthermore, we have successfully applied
sNCA to breast cancer data to uncover the role of TFs
in regulating endocrine resistance in breast cancer.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Re:2013:NBD,
author = "Matteo Re and Giorgio Valentini",
title = "Network-Based Drug Ranking and Repositioning with
Respect to {DrugBank} Therapeutic Categories",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1359--1371",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.62",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Drug repositioning is a challenging computational
problem involving the integration of heterogeneous
sources of biomolecular data and the design of label
ranking algorithms able to exploit the overall topology
of the underlying pharmacological network. In this
context, we propose a novel semisupervised drug ranking
problem: prioritizing drugs in integrated biochemical
networks according to specific DrugBank therapeutic
categories. Algorithms for drug repositioning usually
perform the inference step into an inhomogeneous
similarity space induced by the relationships existing
between drugs and a second type of entity (e.g.,
disease, target, ligand set), thus making infeasible a
drug ranking within a homogeneous pharmacological
space. To deal with this problem, we designed a general
framework based on bipartite network projections by
which homogeneous pharmacological networks can be
constructed and integrated from heterogeneous and
complementary sources of chemical, biomolecular and
clinical information. Moreover, we present a novel
algorithmic scheme based on kernelized score functions
that adopts both local and global learning strategies
to effectively rank drugs in the integrated
pharmacological space using different network
combination methods. Detailed experiments with more
than 80 DrugBank therapeutic categories involving about
1,300 FDA-approved drugs show the effectiveness of the
proposed approach.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wylie:2013:PCP,
author = "Tim Wylie and Binhai Zhu",
title = "Protein Chain Pair Simplification under the Discrete
{Fr{\'e}chet} Distance",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1372--1383",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.17",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "For protein structure alignment and comparison, a lot
of work has been done using RMSD as the distance
measure, which has drawbacks under certain
circumstances. Thus, the discrete Fr{\'e}chet distance
was recently applied to the problem of protein
(backbone) structure alignment and comparison with
promising results. For this problem, visualization is
also important because protein chain backbones can have
as many as 500-600 $ (\alpha) $-carbon atoms, which
constitute the vertices in the comparison. Even with an
excellent alignment, the similarity of two polygonal
chains can be difficult to visualize unless the chains
are nearly identical. Thus, the chain pair
simplification problem (CPS-3F) was proposed in 2008 to
simultaneously simplify both chains with respect to
each other under the discrete Fr{\'e}chet distance. The
complexity of CPS-3F is unknown, so heuristic methods
have been developed. Here, we define a variation of
CPS-3F, called the constrained CPS-3F problem ($ ({\rm
CPS \hbox {-}3F}^+) $ ), and prove that it is
polynomially solvable by presenting a dynamic
programming solution, which we then prove is a factor-2
approximation for CPS-3F. We then compare the $ ({\rm
CPS \hbox {-}3F}^+) $ solutions with previous empirical
results, and further demonstrate some of the benefits
of the simplified comparisons. Chain pair
simplification based on the Hausdorff distance (CPS-2H)
is known to be NP-complete, and here we prove that the
constrained version ($ (\rm C P S \hbox {-}2 H^+) $ )
is also NP-complete. Finally, we discuss future work
and implications along with a software library
implementation, named the Fr{\'e}chet-based Protein
Alignment {\&} Comparison Toolkit (FPACT).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bulteau:2013:IED,
author = "Laurent Bulteau and Minghui Jiang",
title = "Inapproximability of $ ((1, 2)) $-Exemplar Distance",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1384--1390",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.144",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Given two genomes possibly with duplicate genes, the
exemplar distance problem is that of removing all but
one copy of each gene in each genome, so as to minimize
the distance between the two reduced genomes according
to some measure. Let $ ((s, t)) $-exemplar distance
denote the exemplar distance problem on two genomes $
(G_1) $ and $ (G_2) $, where each gene occurs at most $
(s) $ times in $ (G_1) $ and at most $ (t) $ times in $
(G_2) $. We show that the simplest nontrivial variant
of the exemplar distance problem, $ ((1, 2)) $-Exemplar
Distance, is already hard to approximate for a wide
variety of distance measures, including both popular
genome rearrangement measures such as adjacency
disruptions, signed reversals, and signed
double-cut-and-joins, and classic string edit distance
measures such as Levenshtein and Hamming distances.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Catanzaro:2013:IPF,
author = "Daniele Catanzaro and Martine Labbe and Bjarni V.
Halldorsson",
title = "An Integer Programming Formulation of the Parsimonious
Loss of Heterozygosity Problem",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1391--1402",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.138",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A loss of heterozygosity (LOH) event occurs when, by
the laws of Mendelian inheritance, an individual should
be heterozygote at a given site but, due to a deletion
polymorphism, is not. Deletions play an important role
in human disease and their detection could provide
fundamental insights for the development of new
diagnostics and treatments. In this paper, we
investigate the parsimonious loss of heterozygosity
problem (PLOHP), i.e., the problem of partitioning
suspected polymorphisms from a set of individuals into
a minimum number of deletion areas. Specifically, we
generalize Halld{\'o}rsson et al.'s work by providing a
more general formulation of the PLOHP and by showing
how one can incorporate different recombination rates
and prior knowledge about the locations of deletions.
Moreover, we show that the PLOHP can be formulated as a
specific version of the clique partition problem in a
particular class of graphs called undirected
catch-point interval graphs and we prove its general $
({\cal NP}) $-hardness. Finally, we provide a
state-of-the-art integer programming (IP) formulation
and strengthening valid inequalities to exactly solve
real instances of the PLOHP containing up to 9,000
individuals and 3,000 SNPs. Our results give
perspectives on the mathematics of the PLOHP and
suggest new directions on the development of future
efficient exact solution approaches.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Christinat:2013:TPE,
author = "Yann Christinat and Bernard M. E. Moret",
title = "A Transcript Perspective on Evolution",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1403--1411",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.145",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Alternative splicing is now recognized as a major
mechanism for transcriptome and proteome diversity in
higher eukaryotes, yet its evolution is poorly
understood. Most studies focus on the evolution of
exons and introns at the gene level, while only few
consider the evolution of transcripts. In this paper,
we present a framework for transcript phylogenies where
ancestral transcripts evolve along the gene tree by
gains, losses, and mutation. We demonstrate the
usefulness of our method on a set of 805 genes and two
different topics. First, we improve a method for
transcriptome reconstruction from ESTs (ASPic), then we
study the evolution of function in transcripts. The use
of transcript phylogenies allows us to double the
precision of ASPic, whereas results on the functional
study reveal that conserved transcripts are more likely
to share protein domains than functional sites. These
studies validate our framework for the study of
evolution in large collections of organisms from the
perspective of transcripts; for this purpose, we
developed and provide a new tool, TrEvoR.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2013:MLI,
author = "Si Li and Kwok Pui Choi and Taoyang Wu and Louxin
Zhang",
title = "Maximum Likelihood Inference of the Evolutionary
History of a {PPI} Network from the Duplication History
of Its Proteins",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1412--1421",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.14",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Evolutionary history of protein-protein interaction
(PPI) networks provides valuable insight into molecular
mechanisms of network growth. In this paper, we study
how to infer the evolutionary history of a PPI network
from its protein duplication relationship. We show that
for a plausible evolutionary history of a PPI network,
its relative quality, measured by the so-called loss
number, is independent of the growth parameters of the
network and can be computed efficiently. This finding
leads us to propose two fast maximum likelihood
algorithms to infer the evolutionary history of a PPI
network given the duplication history of its proteins.
Simulation studies demonstrated that our approach,
which takes advantage of protein duplication
information, outperforms NetArch, the first maximum
likelihood algorithm for PPI network history
reconstruction. Using the proposed method, we studied
the topological change of the PPI networks of the
yeast, fruitfly, and worm.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Subramanian:2013:NMS,
author = "Ayshwarya Subramanian and Stanley Shackney and Russell
Schwartz",
title = "Novel Multisample Scheme for Inferring Phylogenetic
Markers from Whole Genome Tumor Profiles",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1422--1431",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.33",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Computational cancer phylogenetics seeks to enumerate
the temporal sequences of aberrations in tumor
evolution, thereby delineating the evolution of
possible tumor progression pathways, molecular
subtypes, and mechanisms of action. We previously
developed a pipeline for constructing phylogenies
describing evolution between major recurring cell types
computationally inferred from whole-genome tumor
profiles. The accuracy and detail of the phylogenies,
however, depend on the identification of accurate,
high-resolution molecular markers of progression, i.e.,
reproducible regions of aberration that robustly
differentiate different subtypes and stages of
progression. Here, we present a novel hidden Markov
model (HMM) scheme for the problem of inferring such
phylogenetically significant markers through joint
segmentation and calling of multisample tumor data. Our
method classifies sets of genome-wide DNA copy number
measurements into a partitioning of samples into normal
(diploid) or amplified at each probe. It differs from
other similar HMM methods in its design specifically
for the needs of tumor phylogenetics, by seeking to
identify robust markers of progression conserved across
a set of copy number profiles. We show an analysis of
our method in comparison to other methods on both
synthetic and real tumor data, which confirms its
effectiveness for tumor phylogeny inference and
suggests avenues for future advances.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wehe:2013:EAK,
author = "Andre Wehe and J. Gordon Burleigh and Oliver
Eulenstein",
title = "Efficient Algorithms for Knowledge-Enhanced Supertree
and Supermatrix Phylogenetic Problems",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1432--1441",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2012.162",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Phylogenetic inference is a computationally difficult
problem, and constructing high-quality phylogenies that
can build upon existing phylogenetic knowledge and
synthesize insights from new data remains a major
challenge. We introduce knowledge-enhanced phylogenetic
problems for both supertree and supermatrix
phylogenetic analyses. These problems seek an optimal
phylogenetic tree that can only be assembled from a
user-supplied set of, possibly incompatible,
phylogenetic relationships. We describe exact
polynomial time algorithms for the knowledge-enhanced
versions of the NP-hard Robinson Foulds, gene
duplication, duplication and loss, and deep coalescence
supertree problems. Further, we demonstrate that our
algorithms can rapidly improve upon results of local
search heuristics for these problems. Finally, we
introduce a knowledge-enhanced search heuristic that
can be applied to any discrete character data set using
the maximum parsimony (MP) phylogenetic problem.
Although this approach is not guaranteed to find exact
solutions, we show that it also can improve upon
solutions from commonly used MP heuristics.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Meng:2013:WAC,
author = "Tao Meng and Ahmed T. Soliman and Mei-Ling Shyu and
Yimin Yang and Shu-Ching Chen and S. S. Iyengar and
John Yordy and Puneeth Iyengar",
title = "{Wavelet} Analysis in Current Cancer Genome Research:
a Survey",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1442--14359",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.134",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "With the rapid development of next generation
sequencing technology, the amount of biological
sequence data of the cancer genome increases
exponentially, which calls for efficient and effective
algorithms that may identify patterns hidden underneath
the raw data that may distinguish cancer Achilles'
heels. From a signal processing point of view,
biological units of information, including DNA and
protein sequences, have been viewed as one-dimensional
signals. Therefore, researchers have been applying
signal processing techniques to mine the potentially
significant patterns within these sequences. More
specifically, in recent years, wavelet transforms have
become an important mathematical analysis tool, with a
wide and ever increasing range of applications. The
versatility of wavelet analytic techniques has forged
new interdisciplinary bounds by offering common
solutions to apparently diverse problems and providing
a new unifying perspective on problems of cancer genome
research. In this paper, we provide a survey of how
wavelet analysis has been applied to cancer
bioinformatics questions. Specifically, we discuss
several approaches of representing the biological
sequence data numerically and methods of using wavelet
analysis on the numerical sequences.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ji:2013:PLS,
author = "Shuiwang Ji and Wenlu Zhang and Rongjian Li",
title = "A Probabilistic Latent Semantic Analysis Model for
Coclustering the Mouse Brain Atlas",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1460--1468",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.135",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The mammalian brain contains cells of a large variety
of types. The phenotypic properties of cells of
different types are largely the results of distinct
gene expression patterns. Therefore, it is of critical
importance to characterize the gene expression patterns
in the mammalian brain. The Allen Developing Mouse
Brain Atlas provides spatiotemporal in situ
hybridization gene expression data across multiple
stages of mouse brain development. It provides a
framework to explore spatiotemporal regulation of gene
expression during development. We employ a graph
approximation formulation to cocluster the genes and
the brain voxels simultaneously for each time point. We
show that this formulation can be expressed as a
probabilistic latent semantic analysis (PLSA) model,
thereby allowing us to use the expectation-maximization
algorithm for PLSA to estimate the coclustering
parameters. To provide a quantitative comparison with
prior methods, we evaluate the coclustering method on a
set of standard synthetic data sets. Results indicate
that our method consistently outperforms prior methods.
We apply our method to cocluster the Allen Developing
Mouse Brain Atlas data. Results indicate that our
clustering of voxels is more consistent with classical
neuroanatomy than those of prior methods. Our analysis
also yields sets of genes that are co-expressed in a
subset of the brain voxels.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhu:2013:EAZ,
author = "Daming Zhu and Lusheng Wang",
title = "An Exact Algorithm for the Zero Exemplar Breakpoint
Distance Problem",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1469--1477",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.127",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The exemplar breakpoint distance problem is one of the
most important problems in genome comparison and has
been extensively studied in the literature. The
exemplar breakpoint distance problem cannot be
approximated within any factor even if each gene family
occurs at most twice in a genome. This is due to the
fact that its decision version, the zero exemplar
breakpoint distance problem where each gene family
occurs at most twice in a genome (ZEBD$ ((2, 2)) $ for
short) is NP-hard. Thus, the basic version ZEBD$ ((2,
2)) $ has attracted the attention of many scientists.
The best existing algorithm for ZEBD$ ((2, 2)) $ runs
in $ (O(n2^n)) $ time. In this paper, we propose a new
algorithm for ZEBD$ ((2, 2)) $ with running time $
(O(n^{21.86121^n})) $. We have implemented the
algorithm in Java. The software package is available
upon request.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mourad:2013:DPS,
author = "Ramy Mourad and Zaher Dawy and Faruck Morcos",
title = "Designing Pooling Systems for Noisy High-Throughput
Protein-Protein Interaction Experiments Using {Boolean}
Compressed Sensing",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1478--1490",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.129",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Group testing, also known as pooling, is a common
technique used in high-throughput experiments in
molecular biology to significantly reduce the number of
tests required to identify rare biological interactions
while correcting for experimental noise. Central to the
group testing problem are (1) a pooling design that
lays out how items are grouped together into pools for
testing and (2) a decoder that interprets the results
of the tested pools, identifying the active compounds.
In this work, we take advantage of decoder guarantees
from the field of compressed sensing (CS) to address
the problem of efficient and reliable detection of
biological interaction in noisy high-throughput
experiments. We also use efficient combinatorial
algorithms from group testing as well as established
measurement matrices from CS to create pooling designs.
First, we formulate the group testing problem in terms
of a Boolean CS framework. We then propose a
low-complexity $ (l_1) $-norm decoder to interpret
pooling test results and identify active compounds. We
demonstrate the robustness of the proposed $ (l_1)
$-norm decoder in simulated experiments with
false-positive and false-negative error rates typical
of high-throughput experiments. When benchmarked
against the current state-of-the-art methods, the
proposed $ (l_1) $-norm decoder provides superior error
correction for the majority of the cases considered
while being notably faster computationally.
Additionally, we test the performance of the $ (l_1)
$-norm decoder against a real experimental data set,
where 12,675 prey proteins were screened against 12
bait proteins. Lastly, we study the impact of different
sparse pooling design matrices on decoder performance
and show that the shifted transversal design (STD) is
the most suitable among the pooling designs surveyed
for biological applications of CS.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ahirwal:2013:EEA,
author = "M. K. Ahirwal and A. Kumar and G. K. Singh",
title = "{EEG\slash ERP} Adaptive Noise Canceller Design with
{Controlled Search Space (CSS)} Approach in Cuckoo and
Other Optimization Algorithms",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1491--1504",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.119",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper explores the migration of adaptive
filtering with swarm intelligence/evolutionary
techniques employed in the field of
electroencephalogram/event-related potential noise
cancellation or extraction. A new approach is proposed
in the form of controlled search space to stabilize the
randomness of swarm intelligence techniques especially
for the EEG signal. Swarm-based algorithms such as
Particles Swarm Optimization, Artificial Bee Colony,
and Cuckoo Optimization Algorithm with their variants
are implemented to design optimized adaptive noise
canceler. The proposed controlled search space
technique is tested on each of the swarm intelligence
techniques and is found to be more accurate and
powerful. Adaptive noise canceler with traditional
algorithms such as least-mean-square, normalized
least-mean-square, and recursive least-mean-square
algorithms are also implemented to compare the results.
ERP signals such as simulated visual evoked potential,
real visual evoked potential, and real sensorimotor
evoked potential are used, due to their physiological
importance in various EEG studies. Average
computational time and shape measures of evolutionary
techniques are observed 8.21E-01 sec and 1.73E-01,
respectively. Though, traditional algorithms take
negligible time consumption, but are unable to offer
good shape preservation of ERP, noticed as average
computational time and shape measure difference,
1.41E-02 sec and 2.60E+00, respectively.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kaddi:2013:MHS,
author = "Chanchala D. Kaddi and R. Mitchell Parry and May D.
Wang",
title = "Multivariate Hypergeometric Similarity Measure",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1505--1516",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.28",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We propose a similarity measure based on the
multivariate hypergeometric distribution for the
pairwise comparison of images and data vectors. The
formulation and performance of the proposed measure are
compared with other similarity measures using synthetic
data. A method of piecewise approximation is also
implemented to facilitate application of the proposed
measure to large samples. Example applications of the
proposed similarity measure are presented using mass
spectrometry imaging data and gene expression
microarray data. Results from synthetic and biological
data indicate that the proposed measure is capable of
providing meaningful discrimination between samples,
and that it can be a useful tool for identifying
potentially related samples in large-scale biological
data sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wong:2013:PPL,
author = "Ginny Y. Wong and Frank H. F. Leung and Sai-Ho Ling",
title = "Predicting Protein-Ligand Binding Site Using Support
Vector Machine with Protein Properties",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1517--1529",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.126",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Identification of protein-ligand binding site is an
important task in structure-based drug design and
docking algorithms. In the past two decades, different
approaches have been developed to predict the binding
site, such as the geometric, energetic, and
sequence-based methods. When scores are calculated from
these methods, the algorithm for doing classification
becomes very important and can affect the prediction
results greatly. In this paper, the support vector
machine (SVM) is used to cluster the pockets that are
most likely to bind ligands with the attributes of
geometric characteristics, interaction potential,
offset from protein, conservation score, and properties
surrounding the pockets. Our approach is compared to
LIGSITE, $ ({\rm LIGSITE}^{{\rm csc}}) $, SURFNET,
Fpocket, PocketFinder, Q-SiteFinder, ConCavity, and
MetaPocket on the data set LigASite and 198 drug-target
protein complexes. The results show that our approach
improves the success rate from 60 to 80 percent at AUC
measure and from 61 to 66 percent at top 1 prediction.
Our method also provides more comprehensive results
than the others.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Braz:2013:PMC,
author = "Fernando A. F. Braz and Jader S. Cruz and Alessandra
C. Faria-Campos and Sergio V. A. Campos",
title = "Probabilistic Model Checking Analysis of Palytoxin
Effects on Cell Energy Reactions of the {Na+\slash
K+-ATPase}",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1530--1541",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.97",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Probabilistic model checking (PMC) is a technique used
for the specification and analysis of complex systems.
It can be applied directly to biological systems which
present these characteristics, including cell transport
systems. These systems are structures responsible for
exchanging ions through the plasma membrane. Their
correct behavior is essential for animal cells, since
changes on those are responsible for diseases. In this
work, PMC is used to model and analyze the effects of
the palytoxin toxin (PTX) interactions with one of
these systems. Our model suggests that ATP could
inhibit PTX action. Therefore, individuals with ATP
deficiencies, such as in brain disorders, may be more
susceptible to the toxin. We have also used heat maps
to enhance the kinetic model, which is used to describe
the system reactions. The map reveals unexpected
situations, such as a frequent reaction between
unlikely pump states, and hot spots such as likely
states and reactions. This type of analysis provides a
better understanding on how transmembrane ionic
transport systems behave and may lead to the discovery
and development of new drugs to treat diseases
associated to their incorrect behavior.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yang:2013:CPP,
author = "Chao Yang and Zengyou He and Weichuan Yu",
title = "A Combinatorial Perspective of the Protein Inference
Problem",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1542--1547",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.110",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In a shotgun proteomics experiment, proteins are the
most biologically meaningful output. The success of
proteomics studies depends on the ability to accurately
and efficiently identify proteins. Many methods have
been proposed to facilitate the identification of
proteins from peptide identification results. However,
the relationship between protein identification and
peptide identification has not been thoroughly
explained before. In this paper, we devote ourselves to
a combinatorial perspective of the protein inference
problem. We employ combinatorial mathematics to
calculate the conditional protein probabilities
(protein probability means the probability that a
protein is correctly identified) under three
assumptions, which lead to a lower bound, an upper
bound, and an empirical estimation of protein
probabilities, respectively. The combinatorial
perspective enables us to obtain an analytical
expression for protein inference. Our method achieves
comparable results with ProteinProphet in a more
efficient manner in experiments on two data sets of
standard protein mixtures and two data sets of real
samples. Based on our model, we study the impact of
unique peptides and degenerate peptides (degenerate
peptides are peptides shared by at least two proteins)
on protein probabilities. Meanwhile, we also study the
relationship between our model and ProteinProphet. We
name our program ProteinInfer. Its Java source code,
our supplementary document and experimental results are
available at
http://bioinformatics.ust.hk/proteininfer.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Oakley:2013:PSO,
author = "Mark T. Oakley and Elizabeth Grace Richardson and
Harriet Carr and Roy L. Johnston",
title = "Protein Structure Optimization with a ``{Lamarckian}''
Ant Colony Algorithm",
journal = j-TCBB,
volume = "10",
number = "6",
pages = "1548--1552",
month = nov,
year = "2013",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.125",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Feb 28 05:26:07 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We describe the LamarckiAnt algorithm: a search
algorithm that combines the features of a
``Lamarckian'' genetic algorithm and ant colony
optimization. We have implemented this algorithm for
the optimization of BLN model proteins, which have
frustrated energy landscapes and represent a challenge
for global optimization algorithms. We demonstrate that
LamarckiAnt performs competitively with other
state-of-the-art optimization algorithms.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2014:EEC,
author = "Ying Xu",
title = "Editorial from the {Editor-in-Chief}",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "1--1",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2302365",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Eisenhaber:2014:GEI,
author = "Frank Eisenhaber and Wing-Kin Sung and Limsoon Wong",
title = "Guest editorial for the international conference on
genome informatics {(GIW 2013)}",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "5--6",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2299751",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhao:2014:CGE,
author = "Liang Zhao and Steven C. H. Hoi and Zhenhua Li and
Limsoon Wong and Hung Nguyen and Jinyan Li",
title = "Coupling graphs, efficient algorithms and {B}-cell
epitope prediction",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "7--16",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.136",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Coupling graphs are newly introduced in this paper to
meet many application needs particularly in the field
of bioinformatics. A coupling graph is a two-layer
graph complex, in which each node from one layer of the
graph complex has at least one connection with the
nodes in the other layer, and vice versa. The coupling
graph model is sufficiently powerful to capture strong
and inherent associations between subgraph pairs in
complicated applications. The focus of this paper is on
mining algorithms of frequent coupling subgraphs and
bioinformatics application. Although existing frequent
subgraph mining algorithms are competent to identify
frequent subgraphs from a graph database, they perform
poorly on frequent coupling subgraph mining because
they generate many irrelevant subgraphs. We propose a
novel graph transformation technique to transform a
coupling graph into a generic graph. Based on the
transformed coupling graphs, existing graph mining
methods are then utilized to discover frequent coupling
subgraphs. We prove that the transformation is precise
and complete and that the restoration is reversible.
Experiments carried out on a database containing 10,511
coupling graphs show that our proposed algorithm
reduces the mining time very much in comparison with
the existing subgraph mining algorithms. Moreover, we
demonstrate the usefulness of frequent coupling
subgraphs by applying our algorithm to make accurate
predictions of epitopes in antibody-antigen binding.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fan:2014:QSM,
author = "Ying Fan and Ruoshui Lu and Lusheng Wang and Massimo
Andreatta and Shuai Cheng Li",
title = "Quantifying significance of {MHC II} residues",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "17--25",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.138",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The major histocompatibility complex (MHC), a
cell-surface protein mediating immune recognition,
plays important roles in the immune response system of
all higher vertebrates. MHC molecules are highly
polymorphic and they are grouped into serotypes
according to the specificity of the response. It is a
common belief that a protein sequence determines its
three dimensional structure and function. Hence, the
protein sequence determines the serotype. Residues play
different levels of importance. In this paper, we
quantify the residue significance with the available
serotype information. Knowing the significance of the
residues will deepen our understanding of the MHC
molecules and yield us a concise representation of the
molecules. In this paper we propose a linear
programming-based approach to find significant residue
positions as well as quantifying their significance in
MHC II DR molecules. A mong all the residues in MHC II
DR molecules, 18 positions are of particular
significance, which is consistent with the literature
on MHC binding sites, and succinct pseudo-sequences
appear to be adequate to capture the whole sequence
features. When the result is used for classification of
MHC molecules with serotype assigned by WHO, a 98.4
percent prediction performance is achieved. The methods
have been implemented in java
(http://code.google.com/p/quassi/).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yoo:2014:ICM,
author = "Paul D. Yoo and Sami Muhaidat and Kamal Taha and Jamal
Bentahar and Abdallah Shami",
title = "Intelligent consensus modeling for proline cis-trans
isomerization prediction",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "26--32",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.132",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Proline cis-trans isomerization (CTI) plays a key role
in the rate-determining steps of protein folding.
Accurate prediction of proline CTI is of great
importance for the understanding of protein folding,
splicing, cell signaling, and transmembrane active
transport in both the human body and animals. Our goal
is to develop a state-of-the-art proline CTI predictor
based on a biophysically motivated intelligent
consensus modeling through the use of sequence
information only (i.e., position specific scores
generated by PSI-BLAST). The current computational
proline CTI predictors reach about 70-73 percent Q2
accuracies and about 0.40 Matthew correlation
coefficient (Mcc) through the use of sequence-based
evolutionary information as well as predicted protein
secondary structure information. However, our approach
that utilizes a novel decision tree-based consensus
model with a powerful randomized-metalearning technique
has achieved 86.58 percent Q2 accuracy and 0.74 Mcc, on
the same proline CTI data set, which is a better result
than those of any existing computational proline CTI
predictors reported in the literature.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ghoraie:2014:RSS,
author = "Laleh Soltan Ghoraie and Forbes Burkowski and Shuai
Cheng Li and Mu Zhu",
title = "Residue-specific side-chain polymorphisms via particle
belief propagation",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "33--41",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.130",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein side chains populate diverse conformational
ensembles in crystals. Despite much evidence that there
is widespread conformational polymorphism in protein
side chains, most of the X -ray crystallography data
are modeled by single conformations in the Protein Data
Bank. The ability to extract or to predict these
conformational polymorphisms is of crucial importance,
as it facilitates deeper understanding of protein
dynamics and functionality. In this paper, we describe
a computational strategy capable of predicting
side-chain polymorphisms. Our approach extends a
particular class of algorithms for side-chain
prediction by modeling the side-chain dihedral angles
more appropriately as continuous rather than discrete
variables. Employing a new inferential technique known
as particle belief propagation, we predict
residue-specific distributions that encode information
about side-chain polymorphisms. Our predicted
polymorphisms are in relatively close agreement with
results from a state-of-the-art approach based on X
-ray crystallography data, which characterizes the
conformational polymorphisms of side chains using
electron density information, and has successfully
discovered previously unmodeled conformations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liao:2014:NUB,
author = "Ruiqi Liao and Ruichang Zhang and Jihong Guan and
Shuigeng Zhou",
title = "A new unsupervised binning approach for metagenomic
sequences based on {$N$}-grams and automatic feature
weighting",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "42--54",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.137",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The rapid development of high-throughput technologies
enables researchers to sequence the whole metagenome of
a microbial community sampled directly from the
environment. The assignment of these sequence reads
into different species or taxonomical classes is a
crucial step for metagenomic analysis, which is
referred to as binning of metagenomic data. Most
traditional binning methods rely on known reference
genomes for accurate assignment of the sequence reads,
therefore cannot classify reads from unknown species
without the help of close references. To overcome this
drawback, unsupervised learning based approaches have
been proposed, which need not any known species'
reference genome for help. In this paper, we introduce
a novel unsupervised method called MCluster for binning
metagenomic sequences. This method uses N-grams to
extract sequence features and utilizes automatic
feature weighting to improve the performance of the
basic K-means clustering algorithm. We evaluate
MCluster on a variety of simulated data sets and a real
data set, and compare it with three latest binning
methods: AbundanceBin, MetaCluster 3.0, and MetaCluster
5.0. Experimental results show that MCluster achieves
obviously better overall performance ( F -measure) than
AbundanceBin and MetaCluster 3.0 on long metagenomic
reads ({$>$}=800 bp); while compared with MetaCluster
5.0, MCluster obtains a larger sensitivity, and a
comparable yet more stable F -measure on short
metagenomic reads ({$<$300} bp). This suggests that
MCluster can serve as a promising tool for effectively
binning metagenomic sequences.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hsiao:2014:GND,
author = "Jui-Chen Hsiao and Chih-Hsuan Wei and Hung-Yu Kao",
title = "Gene name disambiguation using multi-scope species
detection",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "55--62",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.139",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Species detection is an important topic in the text
mining field. According to the importance of the
research topics (e.g., species assignment to genes and
document focus species detection), some studies are
dedicated to an individual topic. However, no
researcher to date has discussed species detection as a
general problem. Therefore, we developed a multi-scope
species detection model to identify the focus species
for different scopes (i.e., gene mention, sentence,
paragraph, and global scope of the entire article).
Species assignment is one of the bottlenecks of gene
name disambiguation. In our evaluation, recognizing the
focus species of a gene mention in four different
scopes improved the gene name disambiguation. We used
the species cue words extracted from articles to
estimate the relevance between an article and a
species. The relevance score was calculated by our
proposed entities frequency-augmented invert species
frequency (EF-AISF) formula, which represents the
importance of an entity to a species. We also defined a
relation guide factor (RGF) to normalize the relevance
score. Our method not only achieved better performance
than previous methods but also can handle the articles
that do not specifically mention a species. In the DECA
corpus, we outperformed previous studies and obtained
an accuracy of 88.22 percent.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Guo:2014:RFE,
author = "Jing Guo and Ritika Jain and Peng Yang and Rui Fan and
Chee Keong Kwoh and Jie Zheng",
title = "Reliable and fast estimation of recombination rates by
convergence diagnosis and parallel {Markov Chain Monte
Carlo}",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "63--72",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.133",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genetic recombination is an essential event during the
process of meiosis resulting in an exchange of segments
between paired chromosomes. Estimating recombination
rate is crucial for understanding the process of
recombination. Experimental methods are normally
difficult and limited to small scale estimations. Thus
statistical methods using population genetics data are
important for large-scale analysis. LDhat is an
extensively used statistical method using jMCMC
algorithm to predict recombination rates. Due to the
complexity of rjMCMC scheme, LDhat may take a long time
for large SNP data sets. In addition, rjMCMC parameters
should be manually defined in the original program
which directly impact results. To address these issues,
we designed an improved algorithm based on LDhat
implementing MCMC convergence diagnostic algorithms to
automatically predict values of parameters and monitor
the mixing process. Then parallel computation methods
were employed to further accelerate the new program.
The new algorithms have been tested on ten samples from
HapMap phase 2 data set. The results were compared with
previous code and showed nearly identical output.
However, our new methods achieved significant
acceleration proving that they are more efficient and
reliable for the estimation of recombination rates. The
stand-alone package is freely available for download
http://www.ntu.edu.sg/home/zhengjie/software/CPLDhat.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gan:2014:ICR,
author = "Yanglan Gan and Jihong Guan and Shuigeng Zhou and
Weixiong Zhang",
title = "Identifying cis-regulatory elements and modules using
conditional random fields",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "73--82",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.131",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Accurate identification of cis-regulatory elements and
their correlated modules is essential for analysis of
transcriptional regulation, which is a challenging
problem in computational biology. Unsupervised learning
has the advantage of compensating for missing annotated
data, and is thus promising to be effective to identify
cis-regulatory elements and modules. We introduced a
Conditional Random Fields model, referred to as CRFEM,
to integrate sequence features and long-range
dependency of genomic sequences such as epigenetic
features to identify cis-regulatory elements and
modules at the same time. The proposed method is able
to automatically learn model parameters with no labeled
data and explicitly optimize the predictive probability
of cis-regulatory elements and modules. In comparison
with existing methods, our method is more accurate and
can be used for genome-wide studies of gene
regulation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Srihari:2014:ECC,
author = "Sriganesh Srihari and Venkatesh Raman and Hon Wai
Leong and Mark A. Ragan",
title = "Evolution and controllability of cancer networks: a
{Boolean} perspective",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "83--94",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.128",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Cancer forms a robust system capable of maintaining
stable functioning (cell sustenance and proliferation)
despite perturbations. Cancer progresses as stages over
time typically with increasing aggressiveness and
worsening prognosis. Characterizing these stages and
identifying the genes driving transitions between them
is critical to understand cancer progression and to
develop effective anti-cancer therapies. In this work,
we propose a novel model for the 'cancer system' as a
Boolean state space in which a Boolean network, built
from protein-interaction and gene-expression data from
different stages of cancer, transits between Boolean
satisfiability states by ``editing'' interactions and
``flipping'' genes. Edits reflect rewiring of the PPI
network while flipping of genes reflect activation or
silencing of genes between stages. We formulate a
minimization problem MIN FLIP to identify these genes
driving the transitions. The application of our model
(called BoolSpace) on three case studies--pancreatic
and breast tumours in human and post spinal-cord injury
(SCI) in rats--reveals valuable insights into the
phenomenon of cancer progression: (i) interactions
involved in core cell-cycle and DNA-damage repair
pathways are significantly rewired in tumours,
indicating significant impact to key genome-stabilizing
mechanisms; (ii) several of the genes flipped are
serine/threonine kinases which act as biological
switches, reflecting cellular switching mechanisms
between stages; and (iii) different sets of genes are
flipped during the initial and final stages indicating
a pattern to tumour progression. Based on these
results, we hypothesize that robustness of cancer
partly stems from ``passing of the baton'' between
genes at different stages--genes from different
biological processes and/or cellular components are
involved in different stages of tumour progression
thereby allowing tumour cells to evade targeted
therapy, and therefore an effective therapy should
target a ``cover set'' of these genes. A C/C++
implementation of BoolSpace is freely available at:
http://www.bioinformatics.org.au/tools-data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bandyopadhyay:2014:SCS,
author = "Sanghamitra Bandyopadhyay and Saurav Mallik and
Anirban Mukhopadhyay",
title = "A survey and comparative study of statistical tests
for identifying differential expression from microarray
data",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "95--115",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.147",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "DNA microarray is a powerful technology that can
simultaneously determine the levels of thousands of
transcripts (generated, for example, from genes/miRNAs)
across different experimental conditions or tissue
samples. The motto of differential expression analysis
is to identify the transcripts whose expressions change
significantly across different types of samples or
experimental conditions. A number of statistical
testing methods are available for this purpose. In this
paper, we provide a comprehensive survey on different
parametric and non-parametric testing methodologies for
identifying differential expression from microarray
data sets. The performances of the different testing
methods have been compared based on some real-life
miRNA and mRNA expression data sets. For validating the
resulting differentially expressed miRNAs, the outcomes
of each test are checked with the information available
for miRNA in the standard miRNA database PhenomiR 2.0.
Subsequently, we have prepared different simulated data
sets of different sample sizes (from 10 to 100 per
group/population) and thereafter the power of each test
have been calculated individually. The comparative
simulated study might lead to formulate robust and
comprehensive judgements about the performance of each
test in the basis of assumption of data distribution.
Finally, a list of advantages and limitations of the
different statistical tests has been provided, along
with indications of some areas where further studies
are required.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bandyopadhyay:2014:NPB,
author = "Sanghamitra Bandyopadhyay and Koushik Mallick",
title = "A new path based hybrid measure for gene ontology
similarity",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "116--127",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.149",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gene Ontology (GO) consists of a controlled vocabulary
of terms, annotating a gene or gene product, structured
in a directed acyclic graph. In the graph, semantic
relations connect the terms, that represent the
knowledge of functional description and cellular
component information of gene products. GO similarity
gives us a numerical representation of biological
relationship between a gene set, which can be used to
infer various biological facts such as protein
interaction, structural similarity, gene clustering,
etc. Here we introduce a new shortest path based hybrid
measure of ontological similarity between two terms
which combines both structure of the GO graph and
information content of the terms. Here the similarity
between two terms $ t_1 $ and $ t_2 $, referred to as
GOSimPBHM($ t_1 $, $ t_2$), has two components; one
obtained from the common ancestors of $ t_1$ and $
t_2$. The other from their remaining ancestors. The
proposed path based hybrid measure does not suffer from
the well-known shallow annotation problem. Its
superiority with respect to some other popular measures
is established for protein protein interaction
prediction, correlation with gene expression and
functional classification of genes in a biological
pathway. Finally, the proposed measure is utilized to
compute the average GO similarity score among the genes
that are experimentally validated targets of some
microRNAs. Results demonstrate that the targets of a
given miRNA have a high degree of similarity in the
biological process category of GO.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Saeed:2014:CRC,
author = "Fahad Saeed and Jason D. Hoffert and Mark A. Knepper",
title = "{CAMS--RS}: clustering algorithm for large-scale mass
spectrometry data using restricted search space and
intelligent random sampling",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "128--141",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.152",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "High-throughput mass spectrometers can produce massive
amounts of redundant data at an astonishing rate with
many of them having poor signal-to-noise (S/N) ratio.
These low S/N ratio spectra may not get interpreted
using conventional spectra-to-database matching
techniques. In this paper, we present an efficient
algorithm, CAMS-RS (Clustering Algorithm for Mass
Spectra using Restricted Space and Sampling) for
clustering of raw mass spectrometry data. CAMS-RS
utilizes a novel metric (called F-set) that exploits
the temporal and spatial patterns to accurately assess
similarity between two given spectra. The F-set
similarity metric is independent of the retention time
and allows clustering of mass spectrometry data from
independent LC-MS/MS runs. A novel restricted search
space strategy is devised to limit the comparisons of
the number of spectra. An intelligent sampling method
is executed on individual bins that allow merging of
the results to make the final clusters. Our
experiments, using experimentally generated data sets,
show that the proposed algorithm is able to cluster
spectra with high accuracy and is helpful in
interpreting low S/N ratio spectra. The CAMS-RS
algorithm is highly scalable with increasing number of
spectra and our implementation allows clustering of up
to a million spectra within minutes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Metsis:2014:DCN,
author = "Vangelis Metsis and Fillia Makedon and Dinggang Shen
and Heng Huang",
title = "{DNA} copy number selection using robust structured
sparsity-inducing norms",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "138--181",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.141",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Array comparative genomic hybridization (aCGH) is a
newly introduced method for the detection of copy
number abnormalities associated with human diseases
with special focus on cancer. Specific patterns in DNA
copy number variations (CNVs) can be associated with
certain disease types and can facilitate prognosis and
progress monitoring of the disease. Machine learning
techniques have been used to model the problem of
tissue typing as a classification problem. Feature
selection is an important part of the classification
process, because many biological features are not
related to the diseases and confuse the classification
tasks. Multiple feature selection methods have been
proposed in the different domains where classification
has been applied. In this work, we will present a new
feature selection method based on structured
sparsity-inducing norms to identify the informative
aCGH biomarkers which can help us classify different
disease subtypes. To validate the performance of the
proposed method, we experimentally compare it with
existing feature selection methods on four publicly
available aCGH data sets. In all empirical results, the
proposed sparse learning based feature selection method
consistently outperforms other related approaches. More
important, we carefully investigate the aCGH biomarkers
selected by our method, and the biological evidences in
literature strongly support our results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2014:CGT,
author = "Biing-Feng Wang and Chien-Hsin Lin and I-Tse Yang",
title = "Constructing a gene team tree in almost {$ O (n \lg n)
$} time",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "142--153",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.150",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "An important model of a conserved gene cluster is
called the gene team model, in which a chromosome is
defined to be a permutation of distinct genes and a
gene team is defined to be a set of genes that appear
in two or more species, with the distance between
adjacent genes in the team for each chromosome always
no more than a certain threshold $ \delta $. A gene
team tree is a succinct way to represent all gene teams
for every possible value of $ \delta $. The previous
fastest algorithm for constructing a gene team tree of
two chromosomes requires $ O (n \lg n \lg \lg n) $
time, which was given by Wang and Lin. Its bottleneck
is a problem called the maximum-gap problem. In this
paper, by presenting an improved algorithm for the
maximum-gap problem, we reduce the upper bound of the
gene team tree problem to $ O (n \lg n \alpha (n)) $.
Since a grows extremely slowly, this result is almost
as efficient as the current best upper bound, $ O (n
\lg n) $, for finding the gene teams of a fixed $
\delta $ value. Our new algorithm is very efficient
from both the theoretical and practical points of view.
Wang and Lin's gene-team-tree algorithm can be extended
to $k$ chromosomes with complexity $ O (k n \lg n \lg
\lg n)$. Similarly, our improved algorithm for the
maximum-gap problem reduces this running time to $ O (k
n \lg n \alpha (n))$. In addition, it also provides new
upper bounds for the gene team tree problem on general
sequences, in which multiple copies of the same gene
are allowed.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kayano:2014:DDC,
author = "Mitsunori Kayano and Motoki Shiga and Hiroshi
Mamitsuka",
title = "Detecting differentially coexpressed genes from
labeled expression data: a brief review",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "154--167",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.2297921",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We review methods for capturing differential
coexpression, which can be divided into two cases by
the size of gene sets: (1) two paired genes and (2)
multiple genes. In the first case, two genes are
positively and negatively correlated with each other
under one and the other conditions, respectively. In
the second case, multiple genes are coexpressed and
randomly expressed under one and the other conditions,
respectively. We summarize a variety of methods for the
first and second cases into four and three approaches,
respectively. We describe each of these approaches in
detail technically, being followed by thorough
comparative experiments with both synthetic and real
data sets. Our experimental results imply high
possibility of improving the efficiency of the current
methods, particularly in the case of multiple genes,
because of low performance achieved by the best methods
which are relatively simple intuitive ones.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Prabhakaran:2014:HHI,
author = "Sandhya Prabhakaran and M{\'e}lanie Rey and Osvaldo
Zagordi and Niko Beerenwinkel and Volker Roth",
title = "{HIV} haplotype inference using a propagating
{Dirichlet} process mixture model",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "182--191",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.145",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper presents a new computational technique for
the identification of HIV haplotypes. HIV tends to
generate many potentially drug-resistant mutants within
the HIV-infected patient and being able to identify
these different mutants is important for efficient drug
administration. With the view of identifying the
mutants, we aim at analyzing short deep sequencing data
called reads. From a statistical perspective, the
analysis of such data can be regarded as a nonstandard
clustering problem due to missing pairwise similarity
measures between non-overlapping reads. To overcome
this problem we propagate a Dirichlet Process Mixture
Model by sequentially updating the prior information
from successive local analyses. The model is verified
using both simulated and real sequencing data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wei:2014:IPI,
author = "Leyi Wei and Minghong Liao and Yue Gao and Rongrong Ji
and Zengyou He and Quan Zou",
title = "Improved and promising identification of human
{MicroRNAs} by incorporating a high-quality negative
set",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "192--201",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.146",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "MicroRNA (miRNA) plays an important role as a
regulator in biological processes. Identification of
(pre-) miRNAs helps in understanding regulatory
processes. Machine learning methods have been designed
for pre-miRNA identification. However, most of them
cannot provide reliable predictive performances on
independent testing data sets. We assumed this is
because the training sets, especially the negative
training sets, are not sufficiently representative. To
generate a representative negative set, we proposed a
novel negative sample selection technique, and
successfully collected negative samples with improved
quality. Two recent classifiers rebuilt with the
proposed negative set achieved an improvement of $
\approx 6 $ percent in their predictive performance,
which confirmed this assumption. Based on the proposed
negative set, we constructed a training set, and
developed an online system called miRNApre specifically
for human pre-miRNA identification. We showed that
miRNApre achieved accuracies on updated human and
nonhuman data sets that were 34.3 and 7.6 percent
higher than those achieved by current methods. The
results suggest that miRNApre is an effective tool for
pre-miRNA identification. Additionally, by integrating
miRNApre, we developed a miRNA mining tool,
mirnaDetect, which can be applied to find potential
miRNAs in genome-scale data. MirnaDetect achieved a
comparable mining performance on human chromosome 19
data as other existing methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Esfahani:2014:IBP,
author = "Mohammad Shahrokh Esfahani and Edward R. Dougherty",
title = "Incorporation of biological pathway knowledge in the
construction of priors for optimal {Bayesian}
classification",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "202--218",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.143",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Small samples are commonplace in genomic/proteomic
classification, the result being inadequate classifier
design and poor error estimation. The problem has
recently been addressed by utilizing prior knowledge in
the form of a prior distribution on an uncertainty
class of feature-label distributions. A critical issue
remains: how to incorporate biological knowledge into
the prior distribution. For genomics/proteomics, the
most common kind of knowledge is in the form of
signaling pathways. Thus, it behooves us to find
methods of transforming pathway knowledge into
knowledge of the feature-label distribution governing
the classification problem. In this paper, we address
the problem of prior probability construction by
proposing a series of optimization paradigms that
utilize the incomplete prior information contained in
pathways (both topological and regulatory). The
optimization paradigms employ the marginal
log-likelihood, established using a small number of
feature-label realizations (sample points) regularized
with the prior pathway information about the variables.
In the special case of a Normal-Wishart prior
distribution on the mean and inverse covariance matrix
(precision matrix) of a Gaussian distribution, these
optimization problems become convex. Companion website:
gsp.tamu.edu/ Publications/supplementary/shahrokh13a.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Amit:2014:LEP,
author = "Mika Amit and Rolf Backofen and Steffen Heyne and Gad
M. Landau and Mathias M{\"o}hl and Christina Otto and
Sebastian Will",
title = "Local exact pattern matching for non-fixed {RNA}
structures",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "219--230",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.2297113",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Detecting local common sequence-structure regions of
RNAs is a biologically important problem. Detecting
such regions allows biologists to identify functionally
relevant similarities between the inspected molecules.
We developed dynamic programming algorithms for finding
common structure-sequence patterns between two RNAs.
The RNAs are given by their sequence and a set of
potential base pairs with associated probabilities. In
contrast to prior work on local pattern matching of
RNAs, we support the breaking of arcs. This allows us
to add flexibility over matching only fixed structures;
potentially matching only a similar subset of specified
base pairs. We present an $ O(n^3) $ algorithm for
local exact pattern matching between two nested RNAs,
and an $ O(n^3 \log n) $ algorithm for one nested RNA
and one bounded-unlimited RNA. In addition, an
algorithm for approximate pattern matching is
introduced that for two given nested RNAs and a number
$k$, finds the maximal local pattern matching score
between the two RNAs with at most $k$ mismatches in $
O(n^3 k^2)$ time. Finally, we present an $ O(n^3)$
algorithm for finding the most similar subforest
between two nested RNAs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gorecki:2014:MDC,
author = "Pawel G{\'o}recki and Oliver Eulenstein",
title = "Maximizing deep coalescence cost",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "231--242",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.144",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The minimizing deep coalescence (MDC) problem seeks a
species tree that reconciles the given gene trees with
the minimum number of deep coalescence events, called
deep coalescence (DC) cost. To better assess MDC
species trees we investigate into a basic mathematical
property of the DC cost, called the diameter. Given a
gene tree, a species tree, and a leaf labeling function
that assigns leaf-genes of the gene tree to a
leaf-species in the species tree from which they were
sampled, the DC cost describes the discordance between
the trees caused by deep coalescence events. The
diameter of a gene tree and a species tree is the
maximum DC cost across all leaf labelings for these
trees. We prove fundamental mathematical properties
describing precisely these diameters for bijective and
general leaf labelings, and present efficient
algorithms to compute the diameters and their
corresponding leaf labelings. In particular, we
describe an optimal, i.e., linear time, algorithm for
the bijective case. Finally, in an experimental study
we demonstrate that the average diameters between a
gene tree and a species tree grow significantly slower
than their naive upper bounds, suggesting that our
exact bounds can significantly improve on assessing DC
costs when using diameters.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sun:2014:MSA,
author = "Jun Sun and Vasile Palade and Xiaojun Wu and Wei
Fang",
title = "Multiple sequence alignment with hidden {Markov}
models learned by random drift particle swarm
optimization",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "243--257",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.148",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Hidden Markov Models (HMMs) are powerful tools for
multiple sequence alignment (MSA), which is known to be
an NP-complete and important problem in bioinformatics.
Learning HMMs is a difficult task, and many
meta-heuristic methods, including particle swarm
optimization (PSO), have been used for that. In this
paper, a new variant of PSO, called the random drift
particle swarm optimization (RDPSO) algorithm, is
proposed to be used for HMM learning tasks in MSA
problems. The proposed RDPSO algorithm, inspired by the
free electron model in metal conductors in an external
electric field, employs a novel set of evolution
equations that can enhance the global search ability of
the algorithm. Moreover, in order to further enhance
the algorithmic performance of the RDPSO, we
incorporate a diversity control method into the
algorithm and, thus, propose an RDPSO with
diversity-guided search (RDPSODGS). The performances of
the RDPSO, RDPSO-DGS and other algorithms are tested
and compared by learning HMMs for MSA on two well-known
benchmark data sets. The experimental results show that
the HMMs learned by the RDPSO and RDPSO-DGS are able to
generate better alignments for the benchmark data sets
than other most commonly used HMM learning methods,
such as the Baum-Welch and other PSO algorithms. The
performance comparison with well-known MSA programs,
such as ClustalW and MAFFT, also shows that the
proposed methods have advantages in multiple sequence
alignment.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Healy:2014:AKM,
author = "John Healy and Desmond Chambers",
title = "Approximate $k$-mer matching using fuzzy hash maps",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "258--264",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2309609",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/hash.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present a fuzzy technique for approximate $k$-mer
matching that combines the speed of hashing with the
sensitivity of dynamic programming. Our approach
exploits the collision detection mechanism used by hash
maps, unifying the two phases of ``seed and extend''
into a single operation that executes in close to $
O(1)$ average time.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2014:EPF,
author = "Guoxian Yu and Huzefa Rangwala and Carlotta Domeniconi
and Guoji Zhang and Zhiwen Yu",
title = "Erratum to {``Protein function prediction using
multilabel ensemble classification''}",
journal = j-TCBB,
volume = "11",
number = "1",
pages = "265--265",
month = jan,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2299736",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:12 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kim:2014:MLS,
author = "Chang-Kug Kim and Jin-A Kim and Ji-Weon Choi and
In-Seon Jeong and Yi-Seul Moon and Dong-Suk Park and
Young-Joo Seol and Yong-Kab Kim and Yong-Hwan Kim and
Yeon-Ki Kim",
title = "A multi-layered screening method to identify plant
regulatory genes",
journal = j-TCBB,
volume = "11",
number = "2",
pages = "293--303",
month = mar,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.2296308",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:18 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We used a seven-step process to identify genes
involved in glucosinolate biosynthesis and metabolism
in the Chinese cabbage (Brassica rapa). We constructed
an annotated data set with 34,570 unigenes from B. rapa
and predicted 11,526 glucosinolate-related candidate
genes using expression profiles generated across nine
stages of development on a 47k-gene microarray. Using
our multi-layered screening method, we screened 392
transcription factors, 843 pathway genes, and 4,162
ortholog genes associated with glucosinolate-related
biosynthesis. Finally, we identified five genes by
comparison of the pathway-network genes including the
transcription-factor genes and the ortholog-ontology
genes. The five genes were anchored to the chromosomes
of B. rapa to characterize their genetic-map positions,
and phylogenetic reconstruction with homologous genes
was performed. These anchored genes were verified by
reverse-transcription polymerase chain reaction. While
the five genes identified by our multi-layered screen
require further characterization and validation, our
study demonstrates the power of multi-layered screening
after initial identification of genes on microarrays.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nye:2014:ACP,
author = "Tom M. W. Nye",
title = "An algorithm for constructing principal geodesics in
phylogenetic treespace",
journal = j-TCBB,
volume = "11",
number = "2",
pages = "304--315",
month = mar,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2309599",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:18 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Most phylogenetic analyses result in a sample of
trees, but summarizing and visualizing these samples
can be challenging. Consensus trees often provide
limited information about a sample, and so methods such
as consensus networks, clustering and multidimensional
scaling have been developed and applied to tree
samples. This paper describes a stochastic algorithm
for constructing a principal geodesic or line through
treespace which is analogous to the first principal
component in standard principal components analysis. A
principal geodesic summarizes the most variable
features of a sample of trees, in terms of both tree
topology and branch lengths, and it can be visualized
as an animation of smoothly changing trees. The
algorithm performs a stochastic search through
parameter space for a geodesic which minimizes the sum
of squared projected distances of the data points. This
procedure aims to identify the globally optimal
principal geodesic, though convergence to locally
optimal geodesics is possible. The methodology is
illustrated by constructing principal geodesics for
experimental and simulated data sets, demonstrating the
insight into samples of trees that can be gained and
how the method improves on a previously published
approach. A java package called GeoPhytter for
constructing and visualizing principal geodesics is
freely available from www.ncl.ac.uk/ntmwn/geophytter.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Eshra:2014:OPC,
author = "Abeer Eshra and Ayman El-Sayed",
title = "An odd parity checker prototype using {DNAzyme} finite
state machine",
journal = j-TCBB,
volume = "11",
number = "2",
pages = "316--324",
month = mar,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.2295803",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:18 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A finite-state machine (FSM) is an abstract
mathematical model of computation used to design both
computer programs and sequential logic circuits.
Considered as an abstract model of computation, FSM is
weak; it has less computational power than some other
models of computation such as the Turing machine. This
paper discusses the finite-state automata based on
Deoxyribonucleic Acid (DNA) and different
implementations of DNA FSMs. Moreover, a comparison was
made to clarify the advantages and disadvantages of
each kind of presented DNA FSMS. Since it is a major
goal for nanoscince, nanotechnology and super molecular
chemistry is to design synthetic molecular devices that
are programmable and run autonomously. Programmable
means that the behavior of the device can be modified
without redesigning the whole structure. Autonomous
means that it runs without externally mediated change
to the work cycle. In this paper we present an odd
Parity Checker Prototype Using DNAzyme FSM. Our paper
makes use of a known design for a DNA nanorobotic
device due to Reif and Sahu [1] for executing FSM
computations using DNAzymes. The main contribution of
our paper is a description of how to program that
device to do a FSM computation known as odd parity
checking. We describe in detail finite state automaton
built on 10-23 DNAzyme, and give its procedure of
design and computation. The design procedure has two
major phases: designing the language potential alphabet
DNA strands, and depending on the first phase to design
the DNAzyme possible transitions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Davidson:2014:DBP,
author = "Ruth Davidson and Seth Sullivant",
title = "Distance-based phylogenetic methods around a
polytomy",
journal = j-TCBB,
volume = "11",
number = "2",
pages = "325--335",
month = mar,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2309592",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:18 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Distance-based phylogenetic algorithms attempt to
solve the NP-hard least-squares phylogeny problem by
mapping an arbitrary dissimilarity map representing
biological data to a tree metric. The set of all
dissimilarity maps is a Euclidean space properly
containing the space of all tree metrics as a
polyhedral fan. Outputs of distance-based tree
reconstruction algorithms such as UPGMA and
neighbor-joining are points in the maximal cones in the
fan. Tree metrics with polytomies lie at the
intersections of maximal cones. A phylogenetic
algorithm divides the space of all dissimilarity maps
into regions based upon which combinatorial tree is
reconstructed by the algorithm. Comparison of
phylogenetic methods can be done by comparing the
geometry of these regions. We use polyhedral geometry
to compare the local nature of the subdivisions induced
by least-squares phylogeny, UPGMA, and neighbor-joining
when the true tree has a single polytomy with exactly
four neighbors. Our results suggest that in some
circumstances, UPGMA and neighbor-joining poorly match
least-squares phylogeny.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Patton:2014:HPI,
author = "Kristopher L. Patton and David J. John and James L.
Norris and Daniel R. Lewis and Gloria K. Muday",
title = "Hierarchical probabilistic interaction modeling for
multiple gene expression replicates",
journal = j-TCBB,
volume = "11",
number = "2",
pages = "336--346",
month = mar,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2299804",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:18 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Microarray technology allows for the collection of
multiple replicates of gene expression time course data
for hundreds of genes at a handful of time points.
Developing hypotheses about a gene transcriptional
network, based on time course gene expression data is
an important and very challenging problem. In many
situations there are similarities which suggest a
hierarchical structure between the replicates. This
paper develops posterior probabilities for network
features based on multiple hierarchical replications.
Through Bayesian inference, in conjunction with the
Metropolis--Hastings algorithm and model averaging, a
hierarchical multiple replicate algorithm is applied to
seven sets of simulated data and to a set of
Arabidopsis thaliana gene expression data. The models
of the simulated data suggest high posterior
probabilities for pairs of genes which have at least
moderate signal partial correlation. For the
Arabidopsis model, many of the highest posterior
probability edges agree with the literature.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{GaneshKumar:2014:HAB,
author = "Pugalendhi GaneshKumar and Chellasamy Rani and
Durairaj Devaraj and T. Aruldoss Albert Victoire",
title = "Hybrid ant bee algorithm for fuzzy expert system based
sample classification",
journal = j-TCBB,
volume = "11",
number = "2",
pages = "347--360",
month = mar,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2307325",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:18 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Accuracy maximization and complexity minimization are
the two main goals of a fuzzy expert system based
microarray data classification. Our previous Genetic
Swarm Algorithm (GSA) approach has improved the
classification accuracy of the fuzzy expert system at
the cost of their interpretability. The if-then rules
produced by the GSA are lengthy and complex which is
difficult for the physician to understand. To address
this interpretability-accuracy tradeoff, the rule set
is represented using integer numbers and the task of
rule generation is treated as a combinatorial
optimization task. Ant colony optimization (ACO) with
local and global pheromone updations are applied to
find out the fuzzy partition based on the gene
expression values for generating simpler rule set. In
order to address the formless and continuous expression
values of a gene, this paper employs artificial bee
colony (ABC) algorithm to evolve the points of
membership function. Mutual Information is used for
identification of informative genes. The performance of
the proposed hybrid Ant Bee Algorithm (ABA) is
evaluated using six gene expression data sets. From the
simulation study, it is found that the proposed
approach generated an accurate fuzzy system with highly
interpretable and compact rules for all the data sets
when compared with other approaches.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tanaka:2014:IEE,
author = "Shunji Tanaka",
title = "Improved exact enumerative algorithms for the planted
$ (l, d)$-motif search problem",
journal = j-TCBB,
volume = "11",
number = "2",
pages = "361--374",
month = mar,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2306842",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:18 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper efficient exact algorithms are proposed
for the planted $ (l, d)$-motif search problem. This
problem is to find all motifs of length $l$ that are
planted in each input string with at most $d$
mismatches. The ``quorum'' version of this problem is
also treated in this paper to find motifs planted not
in all input strings but in at least $q$ input strings.
The proposed algorithms are based on the previous
algorithms called qPMSPruneI and qPMS7 that traverse a
search tree starting from a $l$-length substring of an
input string. To improve these previous algorithms,
several techniques are introduced, which contribute to
reducing the computation time for the traversal. In
computational experiments, it will be shown that the
proposed algorithms outperform the previous
algorithms.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Siren:2014:IGP,
author = "Jouni Sir{\'e}n and Niko V{\"a}lim{\"a}ki and Veli
M{\"a}kinen",
title = "Indexing graphs for path queries with applications in
genome research",
journal = j-TCBB,
volume = "11",
number = "2",
pages = "375--388",
month = mar,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.2297101",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:18 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We propose a generic approach to replace the canonical
sequence representation of genomes with graph
representations, and study several applications of such
extensions. We extend the Burrows--Wheeler transform
(BWT) of strings to acyclic directed labeled graphs, to
support path queries as an extension to substring
searching. We develop, apply, and tailor this technique
to (a) read alignment on an extended BWT index of a
graph representing pan-genome, i.e., reference genome
and known variants of it; and (b) split-read alignment
on an extended BWT index of a splicing graph. Other
possible applications include probe/primer design,
alignments to assembly graphs, and alignments to
phylogenetic tree of partial-order graphs. We report
several experiments on the feasibility and
applicability of the approach. Especially on
highly-polymorphic genome regions our pan-genome index
is making a significant improvement in alignment
accuracy.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Labarre:2014:MPL,
author = "Anthony Labarre and Sicco Verwer",
title = "Merging partially labelled trees: hardness and a
declarative programming solution",
journal = j-TCBB,
volume = "11",
number = "2",
pages = "389--397",
month = mar,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2307200",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:18 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Intraspecific studies often make use of haplotype
networks instead of gene genealogies to represent the
evolution of a set of genes. Cassens et al. [3]
proposed one such network reconstruction method, based
on the global maximum parsimony principle, which was
later recast by the first author of the present work as
the problem of finding a minimum common supergraph of a
set of $t$ partially labelled trees. Although
algorithms have been proposed for solving that problem
on two graphs, the complexity of the general problem on
trees remains unknown. In this paper, we show that the
corresponding decision problem is NP-complete for $ t =
3$. We then propose a declarative programming approach
to solving the problem to optimality in practice, as
well as a heuristic approach, both based on the IDP
system, and assess the performance of both methods on
randomly generated data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2014:OSM,
author = "Weiming Li and Bin Ma and Kaizhong Zhang",
title = "Optimizing spaced $k$-mer neighbors for efficient
filtration in protein similarity search",
journal = j-TCBB,
volume = "11",
number = "2",
pages = "398--406",
month = mar,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2306831",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:18 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Large-scale comparison or similarity search of genomic
DNA and protein sequence is of fundamental importance
in modern molecular biology. To perform DNA and protein
sequence similarity search efficiently, seeding (or
filtration) method has been widely used where only
sequences sharing a common pattern or ``seed'' are
subject to detailed comparison. Therefore these methods
trade search sensitivity with search speed. In this
paper, we introduce a new seeding method, called spaced
$k$-mer neighbors, which provides a better tradeoff
between the sensitivity and speed in protein sequence
similarity search. With the method of spaced $k$-mer
neighbors, for each spaced $k$-mer, a set of spaced
$k$-mers is selected as its neighbors. These
pre-selected spaced $k$-mer neighbors are then used to
detect hits between query sequence and database
sequences. We propose an efficient heuristic algorithm
for the spaced neighbor selection. Our computational
experimental results demonstrate that the method of
spaced $k$-mer neighbors can improve the overall
tradeoff efficiency over existing seeding methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tang:2014:PEP,
author = "Xiwei Tang and Jianxin Wang and Jiancheng Zhong and Yi
Pan",
title = "Predicting essential proteins based on weighted degree
centrality",
journal = j-TCBB,
volume = "11",
number = "2",
pages = "407--418",
month = mar,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.2295318",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:18 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Essential proteins are vital for an organism's
viability under a variety of conditions. There are many
experimental and computational methods developed to
identify essential proteins. Computational prediction
of essential proteins based on the global
protein-protein interaction (PPI) network is severely
restricted because of the insufficiency of the PPI
data, but fortunately the gene expression profiles help
to make up the deficiency. In this work, Pearson
correlation coefficient (PCC) is used to bridge the gap
between PPI and gene expression data. Based on PCC and
edge clustering coefficient (ECC), a new centrality
measure, i.e., the weighted degree centrality (WDC), is
developed to achieve the reliable prediction of
essential proteins. WDC is employed to identify
essential proteins in the yeast PPI and e-Coli networks
in order to estimate its performance. For comparison,
other prediction technologies are also performed to
identify essential proteins. Some evaluation methods
are used to analyze the results from various prediction
approaches. The prediction results and comparative
analyses are shown in the paper. Furthermore, the
parameter $ \lambda $ in the method WDC will be
analyzed in detail and an optimal $ \lambda $ value
will be found. Based on the optimal $ \lambda $ value,
the differentiation of WDC and another prediction
method PeC is discussed. The analyses prove that WDC
outperforms other methods including DC, BC, CC, SC, EC,
IC, NC, and PeC. At the same time, the analyses also
mean that it is an effective way to predict essential
proteins by means of integrating different data
sources.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nasr:2014:SSS,
author = "Kamal {Al Nasr} and Desh Ranjan and Mohammad Zubair
and Lin Chen and Jing He",
title = "Solving the secondary structure matching problem in
cryo-{EM} de novo modeling using a constrained
{$K$}-shortest path graph algorithm",
journal = j-TCBB,
volume = "11",
number = "2",
pages = "419--430",
month = mar,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2302803",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:18 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Electron cryomicroscopy is becoming a major
experimental technique in solving the structures of
large molecular assemblies. More and more
three-dimensional images have been obtained at the
medium resolutions between 5 and 10{\AA}. At this
resolution range, major $ \alpha $-helices can be
detected as cylindrical sticks and \beta -sheets can be
detected as plain-like regions. A critical question in
de novo modeling from cryo-EM images is to determine
the match between the detected secondary structures
from the image and those on the protein sequence. We
formulate this matching problem into a constrained
graph problem and present an $ O(\Delta^2 N^2 2^N)$
algorithm to this NP-Hard problem. The algorithm
incorporates the dynamic programming approach into a
constrained $k$-shortest path algorithm. Our method,
DP-TOSS, has been tested using $ \alpha $-proteins with
maximum 33 helices and $ \alpha $--$ \beta $ proteins
up to five helices and 12 \beta -strands. The correct
match was ranked within the top 35 for 19 of the 20 $
\alpha $-proteins and all nine $ \alpha $--$ \beta $
proteins tested. The results demonstrate that DP-TOSS
improves accuracy, time and memory space in deriving
the topologies of the secondary structure elements for
proteins with a large number of secondary structures
and a complex skeleton.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Moskon:2014:SAC,
author = "Miha Moskon and Miha Mraz",
title = "Systematic approach to computational design of gene
regulatory networks with information processing
capabilities",
journal = j-TCBB,
volume = "11",
number = "2",
pages = "431--440",
month = mar,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.2295792",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:18 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present several measures that can be used in de
novo computational design of biological systems with
information processing capabilities. Their main purpose
is to objectively evaluate the behavior and identify
the biological information processing structures with
the best dynamical properties. They can be used to
define constraints that allow one to simplify the
design of more complex biological systems. These
measures can be applied to existent computational
design approaches in synthetic biology, i.e., rational
and automatic design approaches. We demonstrate their
use on (a) the computational models of several basic
information processing structures implemented with gene
regulatory networks and (b) on a modular design of a
synchronous toggle switch.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tulpan:2014:TPP,
author = "Dan Tulpan and Derek H. Smith and Roberto Montemanni",
title = "Thermodynamic post-processing versus {GC}-content
pre-processing for {DNA} codes satisfying the {Hamming}
distance and reverse-complement constraints",
journal = j-TCBB,
volume = "11",
number = "2",
pages = "441--452",
month = mar,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2299815",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:18 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Stochastic, meta-heuristic and linear construction
algorithms for the design of DNA strands satisfying
Hamming distance and reverse-complement constraints
often use a GC-content constraint to pre-process the
DNA strands. Since GC-content is a poor predictor of
DNA strand hybridization strength the strands can be
filtered by post-processing using thermodynamic
calculations. An alternative approach is considered
here, where the algorithms are modified to remove
consideration of GC-content and rely on post-processing
alone to obtain large sets of DNA strands with
satisfactory melting temperatures. The two approaches
(pre-processing GC-content and post-processing melting
temperatures) are compared and are shown to be
complementary when large DNA sets are desired. In
particular, the second approach can give significant
improvements when linear constructions are used.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cai:2014:GEI,
author = "Zhipeng Cai and Oliver Eulenstein and Cynthia Gibas",
title = "Guest editors introduction to the special section on
bioinformatics research and applications",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "453--454",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2306114",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huffner:2014:PBN,
author = "Falk H{\"u}ffner and Christian Komusiewicz and Adrian
Liebtrau and Rolf Niedermeier",
title = "Partitioning biological networks into highly connected
clusters with maximum edge coverage",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "455--467",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.177",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A popular clustering algorithm for biological networks
which was proposed by Hartuv and Shamir [5] identifies
nonoverlapping highly connected components. We extend
the approach taken by this algorithm by introducing the
combinatorial optimization problem HIGHLY CONNECTED
DELETION, which asks for removing as few edges as
possible from a graph such that the resulting graph
consists of highly connected components. We show that
HIGHLY CONNECTED DELETION is NP-hard and provide a
fixed-parameter algorithm and a kernelization. We
propose exact and heuristic solution strategies, based
on polynomial-time data reduction rules and integer
linear programming with column generation. The data
reduction typically identifies 75 percent of the edges
that are deleted for an optimal solution; the column
generation method can then optimally solve protein
interaction networks with up to 6,000 vertices and
13,500 edges within five hours. Additionally, we
present a new heuristic that finds more clusters than
the method by Hartuv and Shamir.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Song:2014:MSS,
author = "Xuebo Song and Lin Li and Pradip K. Srimani and Philip
S. Yu and James Z. Wang",
title = "Measure the semantic similarity of {GO} terms using
aggregate information content",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "468--476",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.176",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The rapid development of gene ontology (GO) and huge
amount of biomedical data annotated by GO terms
necessitate computation of semantic similarity of GO
terms and, in turn, measurement of functional
similarity of genes based on their annotations. In this
paper we propose a novel and efficient method to
measure the semantic similarity of GO terms. The
proposed method addresses the limitations in existing
GO term similarity measurement techniques; it computes
the semantic content of a GO term by considering the
information content of all of its ancestor terms in the
graph. The aggregate information content (AIC) of all
ancestor terms of a GO term implicitly reflects the GO
term's location in the GO graph and also represents how
human beings use this GO term and all its ancestor
terms to annotate genes. We show that semantic
similarity of GO terms obtained by our method closely
matches the human perception. Extensive experimental
studies show that this novel method also outperforms
all existing methods in terms of the correlation with
gene expression data. We have developed web services
for measuring semantic similarity of GO terms and
functional similarity of genes using the proposed AIC
method and other popular methods. These web services
are available at http://
bioinformatics.clemson.edu/G-SESAME.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zheng:2014:EIL,
author = "Yu Zheng and Louxin Zhang",
title = "Effect of incomplete lineage sorting on
tree-reconciliation-based inference of gene
duplication",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "477--485",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.2297913",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In the tree reconciliation approach to infer the
duplication history of a gene family, the gene (family)
tree is compared to the corresponding species tree.
Incomplete lineage sorting (ILS) gives rise to
stochastic variation in the topology of a gene tree and
hence likely introduces false duplication events when a
tree reconciliation method is used. We quantify the
effect of ILS on gene duplication inference in a
species tree in terms of the expected number of false
duplication events inferred from reconciling a random
gene tree, which occurs with a probability predicted in
coalescent theory, and the species tree. We
computationally examine the relationship between the
effect of ILS on duplication inference in a species
tree and its topological parameters. Our findings
suggest that ILS may cause non-negligible bias on
duplication inference, particularly on an asymmetric
species tree. Hence, when gene duplication is inferred
via tree reconciliation or any other approach that
takes gene tree topology into account, the ILS-induced
bias should be examined cautiously.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhao:2014:DPC,
author = "Bihai Zhao and Jianxin Wang and Min Li and Fang-Xiang
Wu and Yi Pan",
title = "Detecting protein complexes based on uncertain graph
model",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "486--497",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.2297915",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Advanced biological technologies are producing
large-scale protein-protein interaction (PPI) data at
an ever increasing pace, which enable us to identify
protein complexes from PPI networks. Pair-wise protein
interactions can be modeled as a graph, where vertices
represent proteins and edges represent PPIs. However
most of current algorithms detect protein complexes
based on deterministic graphs, whose edges are either
present or absent. Neighboring information is neglected
in these methods. Based on the uncertain graph model,
we propose the concept of expected density to assess
the density degree of a subgraph, the concept of
relative degree to describe the relationship between a
protein and a subgraph in a PPI network. We develop an
algorithm called DCU (detecting complex based on
uncertain graph model) to detect complexes from PPI
networks. In our method, the expected density combined
with the relative degree is used to determine whether a
subgraph represents a complex with high cohesion and
low coupling. We apply our method and the existing
competing algorithms to two yeast PPI networks.
Experimental results indicate that our method performs
significantly better than the state-of-the-art methods
and the proposed model can provide more insights for
future study in PPI networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Marchiori:2014:GEP,
author = "Elena Marchiori and Alioune Ngom and Raj Acharya",
title = "Guest editorial: pattern recognition in
bioinformatics",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "498--499",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2315668",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Comin:2014:FEP,
author = "Matteo Comin and Morris Antonello",
title = "Fast entropic profiler: an information theoretic
approach for the discovery of patterns in genomes",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "500--509",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.2297924",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Information theory has been used for quite some time
in the area of computational biology. In this paper we
present a pattern discovery method, named Fast Entropic
Profiler, that is based on a local entropy function
that captures the importance of a region with respect
to the whole genome. The local entropy function has
been introduced by Vinga and Almeida in [29], here we
discuss and improve the original formulation. We
provide a linear time and linear space algorithm called
Fast Entropic Profiler (FastEP), as opposed to the
original quadratic implementation. Moreover we propose
an alternative normalization that can be also
efficiently implemented. We show that FastEP is
suitable for large genomes and for the discovery of
patterns with unbounded length. FastEP is available at
http://www.dei.unipd.it/~ciompin/main/FastEP.html.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dehzangi:2014:SBM,
author = "Abdollah Dehzangi and Kuldip Paliwal and James Lyons
and Alok Sharma and Abdul Sattar",
title = "A segmentation-based method to extract structural and
evolutionary features for protein fold recognition",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "510--519",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.2296317",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein fold recognition (PFR) is considered as an
important step towards the protein structure prediction
problem. Despite all the efforts that have been made so
far, finding an accurate and fast computational
approach to solve the PFR still remains a challenging
problem for bioinformatics and computational biology.
In this study, we propose the concept of
segmented-based feature extraction technique to provide
local evolutionary information embedded in position
specific scoring matrix (PSSM) and structural
information embedded in the predicted secondary
structure of proteins using SPINE-X. We also employ the
concept of occurrence feature to extract global
discriminatory information from PSSM and SPINE-X. By
applying a support vector machine (SVM) to our
extracted features, we enhance the protein fold
prediction accuracy for 7.4 percent over the best
results reported in the literature. We also report 73.8
percent prediction accuracy for a data set consisting
of proteins with less than 25 percent sequence
similarity rates and 80.7 percent prediction accuracy
for a data set with proteins belonging to 110 folds
with less than 40 percent sequence similarity rates. We
also investigate the relation between the number of
folds and the number of features being used and show
that the number of features should be increased to get
better protein fold prediction results when the number
of folds is relatively large.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ochs:2014:OAT,
author = "Michael F. Ochs and Jason E. Farrar and Michael
Considine and Yingying Wei and Soheil Meshinchi and
Robert J. Arceci",
title = "Outlier analysis and top scoring pair for integrated
data analysis and biomarker discovery",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "520--532",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.153",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Pathway deregulation has been identified as a key
driver of carcinogenesis, with proteins in signaling
pathways serving as primary targets for drug
development. Deregulation can be driven by a number of
molecular events, including gene mutation, epigenetic
changes in gene promoters, overexpression, and gene
amplifications or deletions. We demonstrate a novel
approach that identifies pathways of interest by
integrating outlier analysis within and across
molecular data types with gene set analysis. We use the
results to seed the top-scoring pair algorithm to
identify robust biomarkers associated with pathway
deregulation. We demonstrate this methodology on
pediatric acute myeloid leukemia (AML) data. We develop
a biomarker in primary AML tumors, demonstrate
robustness with an independent primary tumor data set,
and show that the identified biomarkers also function
well in relapsed pediatric AML tumors.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jauhari:2014:MGE,
author = "Shaurya Jauhari and S. A. M. Rizvi",
title = "Mining gene expression data focusing cancer
therapeutics: a digest",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "533--547",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2312002",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "An understanding towards genetics and epigenetics is
essential to cope up with the paradigm shift which is
underway. Personalized medicine and gene therapy will
confluence the days to come. This review highlights
traditional approaches as well as current advancements
in the analysis of the gene expression data from cancer
perspective. Due to improvements in biometric
instrumentation and automation, it has become easier to
collect a lot of experimental data in molecular
biology. Analysis of such data is extremely important
as it leads to knowledge discovery that can be
validated by experiments. Previously, the diagnosis of
complex genetic diseases has conventionally been done
based on the non-molecular characteristics like kind of
tumor tissue, pathological characteristics, and
clinical phase. The microarray data can be well
accounted for high dimensional space and noise. Same
were the reasons for ineffective and imprecise results.
Several machine learning and data mining techniques are
presently applied for identifying cancer using gene
expression data. While differences in efficiency do
exist, none of the well-established approaches is
uniformly superior to others. The quality of algorithm
is important, but is not in itself a guarantee of the
quality of a specific data analysis.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wong:2014:ACP,
author = "Andrew K. C. Wong and En-Shiun Annie Lee",
title = "Aligning and clustering patterns to reveal the protein
functionality of sequences",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "548--560",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2306840",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Discovering sequence patterns with variations unveils
significant functions of a protein family. Existing
combinatorial methods of discovering patterns with
variations are computationally expensive, and
probabilistic methods require more elaborate
probabilistic representation of the amino acid
associations. To overcome these shortcomings, this
paper presents a new computationally efficient method
for representing patterns with variations in a compact
representation called Aligned Pattern Cluster (AP
Cluster). To tackle the runtime, our method discovers a
shortened list of non-redundant statistically
significant sequence associations based on our previous
work. To address the representation of protein
functional regions, our pattern alignment and
clustering step, presented in this paper captures the
conservations and variations of the aligned patterns.
We further refine our solution to allow more coverage
of sequences via extending the AP Clusters containing
only statistically significant patterns to Weak and
Conserved AP Clusters. When applied to the cytochrome
c, the ubiquitin, and the triosephosphate isomerase
protein families, our algorithm identifies the binding
segments as well as the binding residues. When compared
to other methods, ours discovers all binding sites in
the AP Clusters with superior entropy and coverage. The
identification of patterns with variations help
biologists to avoid time-consuming simulations and
experimentations. (Software available upon request).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mina:2014:IRL,
author = "Marco Mina and Pietro Hiram Guzzi",
title = "Improving the robustness of local network alignment:
design and extensive assessment of a {Markov}
clustering-based approach",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "561--572",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2318707",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The analysis of protein behavior at the network level
had been applied to elucidate the mechanisms of protein
interaction that are similar in different species.
Published network alignment algorithms proved to be
able to recapitulate known conserved modules and
protein complexes, and infer new conserved interactions
confirmed by wet lab experiments. In the meantime,
however, a plethora of continuously evolving
protein-protein interaction (PPI) data sets have been
developed, each featuring different levels of
completeness and reliability. For instance, algorithms
performance may vary significantly when changing the
data set used in their assessment. Moreover, existing
papers did not deeply investigate the robustness of
alignment algorithms. For instance, some algorithms
performances vary significantly when changing the data
set used in their assessment. In this work, we design
an extensive assessment of current algorithms
discussing the robustness of the results on the basis
of input networks. We also present AlignMCL, a local
network alignment algorithm based on an improved model
of alignment graph and Markov Clustering. AlignMCL
performs better than other state-of-the-art local
alignment algorithms over different updated data sets.
In addition, AlignMCL features high levels of
robustness, producing similar results regardless the
selected data set.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Shen:2014:LKS,
author = "Qingliang Shen and Hong Tian and Daoqi Tang and
Wenbing Yao and Xiangdong Gao",
title = "Ligand-{K*} sequence elimination: a novel algorithm
for ensemble-based redesign of receptor-ligand
binding",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "573--578",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2302795",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "K* is rotamerically ensemble-based approach to compute
the binding constant. However, its time-consuming
feature limited its application. We present a novel
algorithm that not only computes the partition function
efficiently, but also avoids the exponential growth of
execution time by iteratively pruning the sequence
space until the sequence with highest affinity is
identified.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2014:PFP,
author = "Guoxian Yu and Huzefa Rangwala and Carlotta Domeniconi
and Guoji Zhang and Zhiwen Yu",
title = "Protein function prediction with incomplete
annotations",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "579--591",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.142",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Automated protein function prediction is one of the
grand challenges in computational biology. Multi-label
learning is widely used to predict functions of
proteins. Most of multi-label learning methods make
prediction for unlabeled proteins under the assumption
that the labeled proteins are completely annotated,
i.e., without any missing functions. However, in
practice, we may have a subset of the ground-truth
functions for a protein, and whether the protein has
other functions is unknown. To predict protein
functions with incomplete annotations, we propose a
Protein Function Prediction method with Weak-label
Learning (ProWL) and its variant ProWL-IF. Both ProWL
and ProWL-IF can replenish the missing functions of
proteins. In addition, ProWL-IF makes use of the
knowledge that a protein cannot have certain functions,
which can further boost the performance of protein
function prediction. Our experimental results on
protein-protein interaction networks and gene
expression benchmarks validate the effectiveness of
both ProWL and ProWL-IF.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lopez-Caamal:2014:SQC,
author = "Fernando L{\'o}pez-Caamal and Diego A. Oyarz{\'u}n and
Richard H. Middleton and M{\'\i}riam R. Garc{\'\i}a",
title = "Spatial quantification of cytosolic {Ca$ {2+} $}
accumulation in nonexcitable cells: an analytical
study",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "592--603",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2316010",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Calcium ions act as messengers in a broad range of
processes such as learning, apoptosis, and muscular
movement. The transient profile and the temporal
accumulation of calcium signals have been suggested as
the two main characteristics in which calcium cues
encode messages to be forwarded to downstream pathways.
We address the analytical quantification of calcium
temporal-accumulation in a long, thin section of a
nonexcitable cell by solving a boundary value problem.
In these expressions we note that the cytosolic
Ca$^{2+}$ accumulation is independent of every
intracellular calcium flux and depends on the Ca$^{2+}$
exchange across the membrane, cytosolic calcium
diffusion, geometry of the cell, extracellular calcium
perturbation, and initial concentrations. In
particular, we analyse the time-integrated response of
cytosolic calcium due to (i) a localised initial
concentration of cytosolic calcium and (ii) transient
extracellular perturbation of calcium. In these
scenarios, we conclude that (i) the range of calcium
progression is confined to the vicinity of the initial
concentration, thereby creating calcium microdomains;
and (ii) we observe a low-pass filtering effect in the
response driven by extracellular Ca$^{2+}$
perturbations. Additionally, we note that our
methodology can be used to analyse a broader range of
stimuli and scenarios.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Guan:2014:BDC,
author = "Benjamin X. Guan and Bir Bhanu and Prue Talbot and
Sabrina Lin",
title = "Bio-driven cell region detection in human embryonic
stem cell assay",
journal = j-TCBB,
volume = "11",
number = "3",
pages = "604--611",
month = may,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2306836",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:22 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper proposes a bio-driven algorithm that
detects cell regions automatically in the human
embryonic stem cell (hESC) images obtained using a
phase contrast microscope. The algorithm uses both
statistical intensity distributions of foreground/hESCs
and background/substrate as well as cell property for
cell region detection. The intensity distributions of
foreground/hESCs and background/substrate are modeled
as a mixture of two Gaussians. The cell property is
translated into local spatial information. The
algorithm is optimized by parameters of the modeled
distributions and cell regions evolve with the local
cell property. The paper validates the method with
various videos acquired using different microscope
objectives. In comparison with the state-of-the-art
methods, the proposed method is able to detect the
entire cell region instead of fragmented cell regions.
It also yields high marks on measures such as Jacard
similarity, Dice coefficient, sensitivity and
specificity. Automated detection by the proposed method
has the potential to enable fast quantifiable analysis
of hESCs using large data sets which are needed to
understand dynamic cell behaviors.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2014:RAE,
author = "Ying Xu",
title = "Reviewer appreciation editorial",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "613--613",
month = jul,
year = "2014",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhou:2014:GEA,
author = "Shuigeng Zhou and Yi-Ping Phoebe Chen",
title = "Guest editorial for the {12th Asia Pacific
Bioinformatics Conference}",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "614--615",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2327487",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2014:FIN,
author = "Bin Xu and Jihong Guan",
title = "From function to interaction: a new paradigm for
accurately predicting protein complexes based on
protein-to-protein interaction networks",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "616--627",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2306825",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Identification of protein complexes is critical to
understand complex formation and protein functions.
Recent advances in high-throughput experiments have
provided large data sets of protein-protein
interactions (PPIs). Many approaches, based on the
assumption that complexes are dense subgraphs of PPI
networks (PINs in short), have been proposed to predict
complexes using graph clustering methods. In this
paper, we introduce a novel
from-function-to-interaction paradigm for protein
complex detection. As proteins perform biological
functions by forming complexes, we first cluster
proteins using biology process (BP) annotations from
gene ontology (GO). Then, we map the resulting protein
clusters onto a PPI network (PIN in short), extract
connected subgraphs consisting of clustered proteins
from the PPI network and expand each connected subgraph
with protein nodes that have rich links to the proteins
in the subgraph. Such expanded subgraphs are taken as
predicted complexes. We apply the proposed method
(called CPredictor) to two PPI data sets of S.
cerevisiae for predicting protein complexes.
Experimental results show that CPredictor outperforms
the existing methods. The outstanding precision of
CPredictor proves that the from-function-to-interaction
paradigm provides a new and effective way to
computational detection of protein complexes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Comin:2014:BFR,
author = "Matteo Comin and Davide Verzotto",
title = "Beyond fixed-resolution alignment-free measures for
mammalian enhancers sequence comparison",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "628--637",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2306830",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The cell-type diversity is to a large degree driven by
transcription regulation, i.e., enhancers. It has been
recently shown that in high-level eukaryotes enhancers
rarely work alone, instead they collaborate by forming
clusters of cis-regulatory modules (CRMs). Even if the
binding of transcription factors is sequence-specific,
the identification of functionally similar enhancers is
very difficult. A similarity measure to detect related
regulatory sequences is crucial to understand
functional correlation between two enhancers. This will
allow large-scale analyses, clustering and genome-wide
classifications. In this paper we present Under 2, a
parameter-free alignment-free statistic based on
variable-length words. As opposed to traditional
alignment-free methods, which are based on fixed-length
patterns or, in other words, tied to a fixed
resolution, our statistic is built upon variable-length
words, and thus multiple resolutions are allowed. This
will capture the great variability of lengths of CRMs.
We evaluate several alignment-free statistics on
simulated data and real ChIP-seq sequences. The new
statistic is highly successful in discriminating
functionally related enhancers and, in almost all
experiments, it outperforms fixed-resolution methods.
Finally, experiments on mouse enhancers show that
Under2 can separate enhancers active in different
tissues. Availability:
http://www.dei.unipd.it/~ciompin/main/UnderIICRMS.html",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gan:2014:NWB,
author = "Yanglan Gan and Guobing Zou and Jihong Guan and
Guangwei Xu",
title = "A novel wavelet-based approach for predicting
nucleosome positions using {DNA} structural
information",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "638--647",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2306837",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Nucleosomes are basic elements of chromatin structure.
The positioning of nucleosomes along a genome is very
important to dictate eukaryotic DNA compaction and
access. Current computational methods have focused on
the analysis of nucleosome occupancy and the
positioning of well-positioned nucleosomes. However,
fuzzy nucleosomes require more complex configurations
and are more difficult to predict their positions. We
analyzed the positioning of well-positioned and fuzzy
nucleosomes from a novel structural perspective, and
proposed WaveNuc, a computational approach for
inferring their positions based on continuous wavelet
transformation. The comparative analysis demonstrates
that these two kinds of nucleosomes exhibit different
propeller twist structural characteristics.
Well-positioned nucleosomes tend to locate at sharp
peaks of the propeller twist profile, whereas fuzzy
nucleosomes correspond to broader peaks. The sharpness
of these peaks shows that the propeller twist profile
may contain nucleosome positioning information.
Exploiting this knowledge, we applied WaveNuc to detect
the two different kinds of peaks of the propeller twist
profile along the genome. We compared the performance
of our method with existing methods on real data sets.
The results show that the proposed method can
accurately resolve complex configurations of fuzzy
nucleosomes, which leads to better performance of
nucleosome positioning prediction on the whole
genome.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lo:2014:SCR,
author = "Christine Lo and Boyko Kakaradov and Daniel Lokshtanov
and Christina Boucher",
title = "{SeeSite}: characterizing relationships between splice
junctions and splicing enhancers",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "648--656",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2304294",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "RNA splicing is a cellular process driven by the
interaction between numerous regulatory sequences and
binding sites, however, such interactions have been
primarily explored by laboratory methods since
computational tools largely ignore the relationship
between different splicing elements. Current
computational methods identify either splice sites or
other regulatory sequences, such as enhancers and
silencers. We present a novel approach for
characterizing co-occurring relationships between
splice site motifs and splicing enhancers. Our approach
relies on an efficient algorithm for approximately
solving Consensus Sequence with Outliers, an
NP-complete string clustering problem. In particular,
we give an algorithm for this problem that outputs
near-optimal solutions in polynomial time. To our
knowledge, this is the first formulation and
computational attempt for detecting co-occurring
sequence elements in RNA sequence data. Further, we
demonstrate that SeeSite is capable of showing that
certain ESEs are preferentially associated with weaker
splice sites, and that there exists a co-occurrence
relationship with splice site motifs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2014:IEL,
author = "Hualong Yu and Jun Ni",
title = "An improved ensemble learning method for classifying
high-dimensional and imbalanced biomedicine data",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "657--666",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2306838",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Training classifiers on skewed data can be technically
challenging tasks, especially if the data is
high-dimensional simultaneously, the tasks can become
more difficult. In biomedicine field, skewed data type
often appears. In this study, we try to deal with this
problem by combining asymmetric bagging ensemble
classifier (as Bagging) that has been presented in
previous work and an improved random subspace (RS)
generation strategy that is called feature subspace
(FSS). Specifically, FSS is a novel method to promote
the balance level between accuracy and diversity of
base classifiers in as Bagging. In view of the strong
generalization capability of support vector machine
(SVM), we adopt it to be base classifier. Extensive
experiments on four benchmark biomedicine data sets
indicate that the proposed ensemble learning method
outperforms many baseline approaches in terms of
Accuracy, F-measure, G-mean and AUC evaluation
criterions, thus it can be regarded as an effective and
efficient tool to deal with high-dimensional and
imbalanced biomedical data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hu:2014:PRA,
author = "Fei Hu and Jun Zhou and Lingxi Zhou and Jijun Tang",
title = "Probabilistic reconstruction of ancestral gene orders
with insertions and deletions",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "667--672",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2309602",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Changes of gene orderings have been extensively used
as a signal to reconstruct phylogenies and ancestral
genomes. Inferring the gene order of an extinct species
has a wide range of applications, including the
potential to reveal more detailed evolutionary
histories, to determine gene content and ordering, and
to understand the consequences of structural changes
for organismal function and species divergence. In this
study, we propose a new adjacency-based method, PMAG+,
to infer ancestral genomes under a more general model
of gene evolution involving gene insertions and
deletions (indels), in addition to gene rearrangements.
PMAG+ improves on our previous method PMAG by
developing a new approach to infer ancestral gene
contents and reducing the adjacency assembly problem to
an instance of TSP. We designed a series of experiments
to extensively validate PMAG+ and compared the results
with the most recent and comparable method GapAdj.
According to the results, ancestral gene contents
predicted by PMAG+ coincides highly with the actual
contents with error rates less than 1 percent. Under
various degrees of indels, PMAG+ consistently achieves
more accurate prediction of ancestral gene orders and
at the same time, produces contigs very close to the
actual chromosomes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Thomas:2014:MLE,
author = "Minta Thomas and Anneleen Daemen and Bart {De Moor}",
title = "Maximum likelihood estimation of {GEVD}: applications
in bioinformatics",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "673--680",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2304292",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We propose a method, maximum likelihood estimation of
generalized eigenvalue decomposition (MLGEVD) that
employs a well known technique relying on the
generalization of singular value decomposition (SVD).
The main aim of the work is to show the tight
equivalence between MLGEVD and generalized ridge
regression. This relationship reveals an important
mathematical property of GEVD in which the second
argument act as prior information in the model. Thus we
show that MLGEVD allows the incorporation of external
knowledge about the quantities of interest into the
estimation problem. We illustrate the importance of
prior knowledge in clinical decision making/identifying
differentially expressed genes with case studies for
which microarray data sets with corresponding
clinical/literature information are available. On all
of these three case studies, MLGEVD outperformed GEVD
on prediction in terms of test area under the ROC curve
(test AUC). MLGEVD results in significantly improved
diagnosis, prognosis and prediction of therapy
response.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ichikawa:2014:SPH,
author = "Kazuki Ichikawa and Shinichi Morishita",
title = "A simple but powerful heuristic method for
accelerating $k$-means clustering of large-scale data
in life science",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "681--692",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2306200",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "K -means clustering has been widely used to gain
insight into biological systems from large-scale life
science data. To quantify the similarities among
biological data sets, Pearson correlation distance and
standardized Euclidean distance are used most
frequently; however, optimization methods have been
largely unexplored. These two distance measurements are
equivalent in the sense that they yield the same
$k$-means clustering result for identical sets of $k$
initial centroids. Thus, an efficient algorithm used
for one is applicable to the other. Several
optimization methods are available for the Euclidean
distance and can be used for processing the
standardized Euclidean distance; however, they are not
customized for this context. We instead approached the
problem by studying the properties of the Pearson
correlation distance, and we invented a simple but
powerful heuristic method for markedly pruning
unnecessary computation while retaining the final
solution. Tests using real biological data sets with
50--60K vectors of dimensions 10--2001 ($ \approx
400$MB in size) demonstrated marked reduction in
computation time for $ k = 10$--$ 500$ in comparison
with other state-of-the-art pruning methods such as
Elkan's and Hamerly's algorithms. The BoostKCP software
is available at http://mlab.cb.k.u-tokyo.ac.jp/
ichikawa/boostKCP/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gao:2014:HTZ,
author = "Yuan Gao and Rosa H. M. Chan and Tommy W. S. Chow and
Liyun Zhang and Sylvia Bonilla and Chi-Pui Pang and
Mingzhi Zhang and Yuk Fai Leung",
title = "A high-throughput zebrafish screening method for
visual mutants by light-induced locomotor response",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "693--701",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2306829",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Normal and visually-impaired zebrafish larvae have
differentiable light-induced locomotor response (LLR),
which is composed of visual and non-visual components.
It is recently demonstrated that differences in the
acute phase of the LLR, also known as the visual motor
response (VMR), can be utilized to evaluate new eye
drugs. However, most of the previous studies focused on
the average LLR activity of a particular genotype,
which left information that could address differences
in individual zebrafish development unattended. In this
study, machine learning techniques were employed to
distinguish not only zebrafish larvae of different
genotypes, but also different batches, based on their
response to light stimuli. This approach allows us to
perform efficient high-throughput zebrafish screening
with relatively simple preparations. Following the
general machine learning framework, some discriminative
features were first extracted from the behavioral data.
Both unsupervised and supervised learning algorithms
were implemented for the classification of zebrafish of
different genotypes and batches. The accuracy of the
classification in genotype was over 80 percent and
could achieve up to 95 percent in some cases. The
results obtained shed light on the potential of using
machine learning techniques for analyzing behavioral
data of zebrafish, which may enhance the reliability of
high-throughput drug screening.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chuang:2014:NSG,
author = "Chia-Hua Chuang and Chun-Liang Lin",
title = "A novel synthesizing genetic logic circuit: frequency
multiplier",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "702--713",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2316814",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper presents a novel synthesizing genetic logic
circuit design based on an existing synthetic genetic
oscillator, which provides a function of frequency
multiplier to synthesize a clock signal whose frequency
is a multiple of that of the genetic oscillator. In the
renowned literature, the synthetic genetic oscillator,
known as a repressilator, has been successfully built
in Escherichia coli to generate a periodic oscillating
phenomenon through three repressive genes repress each
other in a chain. On the basis of this fact, our
proposed genetic frequency multiplier circuit utilizes
genetic Buffers in series with a waveform-shaping
circuit to reshape the genetic oscillation signal into
a crisp logic clock signal. By regulating different
threshold levels in the Buffer, the time length of
logic high/low levels in a fundamental sinusoidal wave
can be engineered to pulse-width-modulated (PWM)
signals with various duty cycles. Integrating some of
genetic logic XOR gates and PWM signals from the output
of the Buffers, a genetic frequency multiplier circuit
can be created and the clock signal with the
integer-fold of frequency of the genetic oscillator is
generated. The synthesized signal can be used in
triggering the downstream digital genetic logic
circuits. Simulation results show the applicability of
the proposed idea.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Su:2014:AMD,
author = "Hai Su and Fuyong Xing and Jonah D. Lee and Charlotte
A. Peterson and Lin Yang",
title = "Automatic myonuclear detection in isolated single
muscle fibers using robust ellipse fitting and sparse
representation",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "714--726",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2013.151",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Accurate and robust detection of myonuclei in isolated
single muscle fibers is required to calculate
myonuclear domain size. However, this task is
challenging because: (1) shape and size variations of
the nuclei, (2) overlapping nuclear clumps, and (3)
multiple $z$-stack images with out-of-focus regions. In
this paper, we have proposed a novel automatic
detection algorithm to robustly quantify myonuclei in
isolated single skeletal muscle fibers. The original
$z$-stack images are first converted into one
all-in-focus image using multi-focus image fusion. A
sufficient number of ellipse fitting hypotheses are
then generated from the myonuclei contour segments
using heteroscedastic errors-in-variables (HEIV)
regression. A set of representative training samples
and a set of discriminative features are selected by a
two-stage sparse model. The selected samples with
representative features are utilized to train a
classifier to select the best candidates. A modified
inner geodesic distance based mean-shift clustering
algorithm is used to produce the final nuclei detection
results. The proposed method was extensively tested
using 42 sets of $z$-stack images containing over 1,500
myonuclei. The method demonstrates excellent results
that are better than current state-of-the-art
approaches.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2014:DSB,
author = "Zhiwen Yu and Hongsheng Chen and Jane You and Hau-San
Wong and Jiming Liu and Le Li and Guoqiang Han",
title = "Double selection based semi-supervised clustering
ensemble for tumor clustering from gene expression
profiles",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "727--740",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2315996",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Tumor clustering is one of the important techniques
for tumor discovery from cancer gene expression
profiles, which is useful for the diagnosis and
treatment of cancer. While different algorithms have
been proposed for tumor clustering, few make use of the
expert's knowledge to better the performance of tumor
discovery. In this paper, we first view the expert's
knowledge as constraints in the process of clustering,
and propose a feature selection based semi-supervised
cluster ensemble framework (FS-SSCE) for tumor
clustering from bio-molecular data. Compared with
traditional tumor clustering approaches, the proposed
framework FS-SSCE is featured by two properties: (1)
The adoption of feature selection techniques to dispel
the effect of noisy genes. (2) The employment of the
binate constraint based K-means algorithm to take into
account the effect of experts' knowledge. Then, a
double selection based semi-supervised cluster ensemble
framework (DS-SSCE) which not only applies the feature
selection technique to perform gene selection on the
gene dimension, but also selects an optimal subset of
representative clustering solutions in the ensemble and
improve the performance of tumor clustering using the
normalized cut algorithm. DS-SSCE also introduces a
confidence factor into the process of constructing the
consensus matrix by considering the prior knowledge of
the data set. Finally, we design a modified double
selection based semi-supervised cluster ensemble
framework (MDS-SSCE) which adopts multiple clustering
solution selection strategies and an aggregated
solution selection function to choose an optimal subset
of clustering solutions. The results in the experiments
on cancer gene expression profiles show that (i)
FS-SSCE, DS-SSCE and MDS-SSCE are suitable for
performing tumor clustering from bio-molecular data.
(ii) MDS-SSCE outperforms a number of state-of-the-art
tumor clustering approaches on most of the data sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fa:2014:NRG,
author = "Rui Fa and Asoke K. Nandi",
title = "Noise resistant generalized parametric validity index
of clustering for gene expression data",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "741--752",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2312006",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Validity indices have been investigated for decades.
However, since there is no study of noise-resistance
performance of these indices in the literature, there
is no guideline for determining the best clustering in
noisy data sets, especially microarray data sets. In
this paper, we propose a generalized parametric
validity (GPV) index which employs two tunable
parameters $ \alpha $ and $ \beta $ to control the
proportions of objects being considered to calculate
the dissimilarities. The greatest advantage of the
proposed GPV index is its noise-resistance ability,
which results from the flexibility of tuning the
parameters. Several rules are set to guide the
selection of parameter values. To illustrate the
noise-resistance performance of the proposed index, we
evaluate the GPV index for assessing five clustering
algorithms in two gene expression data simulation
models with different noise levels and compare the
ability of determining the number of clusters with
eight existing indices. We also test the GPV in three
groups of real gene expression data sets. The
experimental results suggest that the proposed GPV
index has superior noise-resistance ability and
provides fairly accurate judgements.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Garcia-Jimenez:2014:PPR,
author = "Beatriz Garc{\'\i}a-Jim{\'e}nez and Tirso Pons and
Araceli Sanchis and Alfonso Valencia",
title = "Predicting protein relationships to human pathways
through a relational learning approach based on simple
sequence features",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "753--765",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2318730",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Biological pathways are important elements of systems
biology and in the past decade, an increasing number of
pathway databases have been set up to document the
growing understanding of complex cellular processes.
Although more genome-sequence data are becoming
available, a large fraction of it remains functionally
uncharacterized. Thus, it is important to be able to
predict the mapping of poorly annotated proteins to
original pathway models. Results: We have developed a
Relational Learning-based Extension (RLE) system to
investigate pathway membership through a function
prediction approach that mainly relies on combinations
of simple properties attributed to each protein. RLE
searches for proteins with molecular similarities to
specific pathway components. Using RLE, we associated
383 uncharacterized proteins to 28 pre-defined human
Reactome pathways, demonstrating relative confidence
after proper evaluation. Indeed, in specific cases
manual inspection of the database annotations and the
related literature supported the proposed
classifications. Examples of possible additional
components of the Electron transport system, Telomere
maintenance and Integrin cell surface interactions
pathways are discussed in detail. Availability: All the
human predicted proteins in the 2009 and 2012 releases
30 and 40 of Reactome are available at
http://rle.bioinfo.cnio.es.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kong:2014:BSD,
author = "Ao Kong and Chinmaya Gupta and Mauro Ferrari and Marco
Agostini and Chiara Bedin and Ali Bouamrani and Ennio
Tasciotti and Robert Azencott",
title = "Biomarker signature discovery from mass spectrometry
data",
journal = j-TCBB,
volume = "11",
number = "4",
pages = "766--772",
month = jul,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2318718",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 6 16:13:27 MST 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Mass spectrometry based high throughput proteomics are
used for protein analysis and clinical diagnosis. Many
machine learning methods have been used to construct
classifiers based on mass spectrometry data, for
discrimination between cancer stages. However, the
classifiers generated by machine learning such as SVM
techniques typically lack biological interpretability.
We present an innovative technique for automated
discovery of signatures optimized to characterize
various cancer stages. We validate our signature
discovery algorithm on one new colorectal cancer
MALDI-TOF data set, and two well-known ovarian cancer
SELDI-TOF data sets. In all of these cases, our
signature based classifiers performed either better or
at least as well as four benchmark machine learning
algorithms including SVM and KNN. Moreover, our
optimized signatures automatically select smaller sets
of key biomarkers than the black-boxes generated by
machine learning, and are much easier to interpret.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pandey:2014:GES,
author = "Gaurav Pandey and Huzefa Rangwala",
title = "Guest editorial for special section on {BIOKDD2013}",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "773--774",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2348731",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fakhraei:2014:NBD,
author = "Shobeir Fakhraei and Bert Huang and Louiqa Raschid and
Lise Getoor",
title = "Network-based drug-target interaction prediction with
probabilistic soft logic",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "775--787",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2325031",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Drug-target interaction studies are important because
they can predict drugs' unexpected therapeutic or
adverse side effects. In silico predictions of
potential interactions are valuable and can focus
effort on in vitro experiments. We propose a prediction
framework that represents the problem using a bipartite
graph of drug-target interactions augmented with
drug-drug and target-target similarity measures and
makes predictions using probabilistic soft logic (PSL).
Using probabilistic rules in PSL, we predict
interactions with models based on triad and tetrad
structures. We apply (blocking) techniques that make
link prediction in PSL more efficient for drug-target
interaction prediction. We then perform extensive
experimental studies to highlight different aspects of
the model and the domain, first comparing the models
with different structures and then measuring the effect
of the proposed blocking on the prediction performance
and efficiency. We demonstrate the importance of rule
weight learning in the proposed PSL model and then show
that PSL can effectively make use of a variety of
similarity measures. We perform an experiment to
validate the importance of collective inference and
using multiple similarity measures for accurate
predictions in contrast to non-collective and single
similarity assumptions. Finally, we illustrate that our
PSL model achieves state-of-the-art performance with
simple, interpretable rules and evaluate our novel
predictions using online data sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Seetan:2014:RRH,
author = "Raed I. Seetan and Anne M. Denton and Omar Al-Azzam
and Ajay Kumar and M. Javed Iqbal and Shahryar F.
Kianian",
title = "Reliable radiation hybrid maps: an efficient scalable
clustering-based approach",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "788--800",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2329310",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The process of mapping markers from radiation hybrid
mapping (RHM) experiments is equivalent to the
traveling salesman problem and, thereby, has
combinatorial complexity. As an additional problem,
experiments typically result in some unreliable markers
that reduce the overall quality of the map. We propose
a clustering approach for addressing both problems
efficiently by eliminating unreliable markers without
the need for mapping the complete set of markers.
Traditional approaches for eliminating markers use
resampling of the full data set, which has an even
higher computational complexity than the original
mapping problem. In contrast, the proposed approach
uses a divide-and-conquer strategy to construct
framework maps based on clusters that exclude
unreliable markers. Clusters are ordered using parallel
processing and are then combined to form the complete
map. We present three algorithms that explore the
trade-off between the number of markers included in the
map and placement accuracy. Using an RHM data set of
the human genome, we compare the framework maps from
our proposed approaches with published physical maps
and with the results of using the Carthagene tool.
Overall, our approaches have a very low computational
complexity and produce solid framework maps with good
chromosome coverage and high agreement with the
physical map marker order.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Goncalves:2014:LEH,
author = "Joana P. Gon{\c{c}}alves and Sara C. Madeira",
title = "{LateBiclustering}: efficient heuristic algorithm for
time-lagged bicluster identification",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "801--813",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2312007",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Identifying patterns in temporal data is key to
uncover meaningful relationships in diverse domains,
from stock trading to social interactions. Also of
great interest are clinical and biological
applications, namely monitoring patient response to
treatment or characterizing activity at the molecular
level. In biology, researchers seek to gain insight
into gene functions and dynamics of biological
processes, as well as potential perturbations of these
leading to disease, through the study of patterns
emerging from gene expression time series. Clustering
can group genes exhibiting similar expression profiles,
but focuses on global patterns denoting rather broad,
unspecific responses. Biclustering reveals local
patterns, which more naturally capture the intricate
collaboration between biological players, particularly
under a temporal setting. Despite the general
biclustering formulation being NP-hard, considering
specific properties of time series has led to efficient
solutions for the discovery of temporally aligned
patterns. Notably, the identification of biclusters
with time-lagged patterns, suggestive of
transcriptional cascades, remains a challenge due to
the combinatorial explosion of delayed occurrences.
Herein, we propose LateBiclustering, a sensible
heuristic algorithm enabling a polynomial rather than
exponential time solution for the problem. We show that
it identifies meaningful time-lagged biclusters
relevant to the response of Saccharomyces cerevisiae to
heat stress.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhou:2014:DSC,
author = "Cheng Zhou and Pieter Meysman and Boris Cule and Kris
Laukens and Bart Goethals",
title = "Discovery of spatially cohesive itemsets in
three-dimensional protein structures",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "814--825",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2311795",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper we present a cohesive structural itemset
miner aiming to discover interesting patterns in a set
of data objects within a multidimensional spatial
structure by combining the cohesion and the support of
the pattern. We propose two ways to build the itemset
miner, VertexOne and VertexAll, in an attempt to find a
balance between accuracy and run-times. The experiments
show that VertexOne performs better, and finds almost
the same itemsets as VertexAll in a much shorter time.
The usefulness of the method is demonstrated by
applying it to find interesting patterns of amino acids
in spatial proximity within a set of proteins based on
their atomic coordinates in the protein molecular
structure. Several patterns found by the cohesive
structural itemset miner contain amino acids that
frequently co-occur in the spatial structure, even if
they are distant in the primary protein sequence and
only brought together by protein folding. Further
various indications were found that some of the
discovered patterns seem to represent common underlying
support structures within the proteins.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zeller:2014:GAP,
author = "Michael Zeller and Christophe N. Magnan and Vishal R.
Patel and Paul Rigor and Leonard Sender and Pierre
Baldi",
title = "A genomic analysis pipeline and its application to
pediatric cancers",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "826--839",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2330616",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present a cancer genomic analysis pipeline which
takes as input sequencing reads for both germline and
tumor genomes and outputs filtered lists of all genetic
mutations in the form of short ranked list of the most
affected genes in the tumor, using either the Complete
Genomics or Illumina platforms. A novel reporting and
ranking system has been developed that makes use of
publicly available datasets and literature specific to
each patient, including new methods for using publicly
available expression data in the absence of proper
control data. Previously implicated small and large
variations (including gene fusions) are reported in
addition to probable driver mutations. Relationships
between cancer and the sequenced tumor genome are
highlighted using a network-based approach that
integrates known and predicted protein-protein,
protein-TF, and protein-drug interaction data. By using
an integrative approach, effects of genetic variations
on gene expression are used to provide further evidence
of driver mutations. This pipeline has been developed
with the aim to be used in assisting in the analysis of
pediatric tumors, as an unbiased and automated method
for interpreting sequencing results along with
identifying potentially therapeutic drugs and their
targets. We present results that agree with previous
literature and highlight specific findings in a few
patients.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2014:ANG,
author = "Peng Chen and Chao Wang and Xi Li and Xuehai Zhou",
title = "Accelerating the next generation long read mapping
with the {FPGA}-based system",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "840--852",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2326876",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "To compare the newly determined sequences against the
subject sequences stored in the databases is a critical
job in the bioinformatics. Fortunately, recent survey
reports that the state-of-the-art aligners are already
fast enough to handle the ultra amount of short
sequence reads in the reasonable time. However, for
aligning the long sequence reads ({$>$400} bp)
generated by the next generation sequencing (NGS)
technology, it is still quite inefficient with present
aligners. Furthermore, the challenge becomes more and
more serious as the lengths and the amounts of the
sequence reads are both keeping increasing with the
improvement of the sequencing technology. Thus, it is
extremely urgent for the researchers to enhance the
performance of the long read alignment. In this paper,
we propose a novel FPGA-based system to improve the
efficiency of the long read mapping. Compared to the
state-of-the-art long read aligner BWA-SW, our
accelerating platform could achieve a high performance
with almost the same sensitivity. Experiments
demonstrate that, for reads with lengths ranging from
512 up to 4,096 base pairs, the described system
obtains a 10 $ \times $ -48$ \times $ speedup for the
bottleneck of the software. As to the whole mapping
procedure, the FPGA-based platform could achieve a 1:8$
\times $ -3:3$ \times $ speedup versus the BWA-SW
aligner, reducing the alignment cycles from weeks to
days.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Meira:2014:AMA,
author = "Luis A. A. Meira and Vin{\'\i}cius R. M{\'a}ximo and
{\'A}lvaro L. Fazenda and Arlindo F. {Da
Concei{\c{c}}{\~a}o}",
title = "{Acc-Motif}: accelerated network motif detection",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "853--862",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2321150",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Network motif algorithms have been a topic of research
mainly after the 2002-seminal paper from Milo et al.
[1], which provided motifs as a way to uncover the
basic building blocks of most networks. Motifs have
been mainly applied in Bioinformatics, regarding gene
regulation networks. Motif detection is based on
induced subgraph counting. This paper proposes an
algorithm to count subgraphs of size k + 2 based on the
set of induced subgraphs of size k. The general
technique was applied to detect 3, 4 and 5-sized motifs
in directed graphs. Such algorithms have time
complexity O(a(G)m), O(m$^2$ ) and O(nm$^2$ ),
respectively, where a(G) is the arboricity of G(V,E).
The computational experiments in public data sets show
that the proposed technique was one order of magnitude
faster than Kavosh and FANMOD. When compared to
NetMODE, acc-Motif had a slightly improved
performance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Messaoudi:2014:BSS,
author = "Imen Messaoudi and Afef Elloumi-Oueslati and Zied
Lachiri",
title = "Building specific signals from frequency chaos game
and revealing periodicities using a smoothed {Fourier}
analysis",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "863--877",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2315991",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Investigating the roles and functions of DNA within
genomes is becoming a primary focus of genomic
research. Thus, the research works are moving towards
cooperation between different scientific disciplines
which aims at facilitating the interpretation of
genetic information. In order to characterize the DNA
of living organisms, signal processing tools appear to
be very suitable for such study. However, a DNA
sequence must be converted into a numerical sequence
before processing; which defines the concept of DNA
coding. In line with this, we propose a new one
dimensional model based on the chaos game
representation theory called Frequency Chaos Game
Signal: FCGS. Then, we perform a Smoothed Fourier
Transform to enhance hidden periodicities in the
C.elegans DNA sequences. Through this study, we
demonstrate the performance of our coding approach in
highlighting characteristic periodicities. Indeed,
several periodicities are shown to be involved in the
1D spectra and the 2D spectrograms of FCGSs. To
investigate further about the contribution of our
method in the enhancement of characteristic spectral
attributes, a comparison with a range of binary
indicators is established.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Krotzky:2014:EGB,
author = "Timo Krotzky and Thomas Fober and Eyke H{\"u}llermeier
and Gerhard Klebe",
title = "Extended graph-based models for enhanced similarity
search in {Cavbase}",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "878--890",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2325020",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "To calculate similarities between molecular
structures, measures based on the maximum common
subgraph are frequently applied. For the comparison of
protein binding sites, these measures are not fully
appropriate since graphs representing binding sites on
a detailed atomic level tend to get very large. In
combination with an NP-hard problem, a large graph
leads to a computationally demanding task. Therefore,
for the comparison of binding sites, a less detailed
coarse graph model is used building upon so-called
pseudocenters. Consistently, a loss of structural data
is caused since many atoms are discarded and no
information about the shape of the binding site is
considered. This is usually resolved by performing
subsequent calculations based on additional
information. These steps are usually quite expensive,
making the whole approach very slow. The main drawback
of a graph-based model solely based on pseudocenters,
however, is the loss of information about the shape of
the protein surface. In this study, we propose a novel
and efficient modeling formalism that does not increase
the size of the graph model compared to the original
approach, but leads to graphs containing considerably
more information assigned to the nodes. More
specifically, additional descriptors considering
surface characteristics are extracted from the local
surface and attributed to the pseudocenters stored in
Cavbase. These properties are evaluated as additional
node labels, which lead to a gain of information and
allow for much faster but still very accurate
comparisons between different structures.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wu:2014:GWP,
author = "Jian-Sheng Wu and Sheng-Jun Huang and Zhi-Hua Zhou",
title = "Genome-wide protein function prediction through
multi-instance multi-label learning",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "891--902",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2323058",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Automated annotation of protein function is
challenging. As the number of sequenced genomes rapidly
grows, the vast majority of proteins can only be
annotated computationally. Nature often brings several
domains together to form multidomain and
multi-functional proteins with a vast number of
possibilities, and each domain may fulfill its own
function independently or in a concerted manner with
its neighbors. Thus, it is evident that the protein
function prediction problem is naturally and inherently
Multi-Instance Multi-Label (MIML) learning tasks. Based
on the state-of-the-art MIML algorithm MIMLNN, we
propose a novel ensemble MIML learning framework
EnMIMLNN and design three algorithms for this task by
combining the advantage of three kinds of Hausdorff
distance metrics. Experiments on seven real-world
organisms covering the biological three-domain system,
i.e., archaea, bacteria, and eukaryote, show that the
EnMIMLNN algorithms are superior to most
state-of-the-art MIML and Multi-Label learning
algorithms.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Limpiti:2014:IIN,
author = "Tulaya Limpiti and Chainarong Amornbunchornvej and
Apichart Intarapanich and Anunchai Assawamakin and
Sissades Tongsima",
title = "{iNJclust}: iterative neighbor-joining tree clustering
framework for inferring population structure",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "903--914",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2322372",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Understanding genetic differences among populations is
one of the most important issues in population
genetics. Genetic variations, e.g., single nucleotide
polymorphisms, are used to characterize commonality and
difference of individuals from various populations.
This paper presents an efficient graph-based clustering
framework which operates iteratively on the
Neighbor-Joining (NJ) tree called the iNJclust
algorithm. The framework uses well-known genetic
measurements, namely the allele-sharing distance, the
neighbor-joining tree, and the fixation index. The
behavior of the fixation index is utilized in the
algorithm's stopping criterion. The algorithm provides
an estimated number of populations, individual
assignments, and relationships between populations as
outputs. The clustering result is reported in the form
of a binary tree, whose terminal nodes represent the
final inferred populations and the tree structure
preserves the genetic relationships among them. The
clustering performance and the robustness of the
proposed algorithm are tested extensively using
simulated and real data sets from bovine, sheep, and
human populations. The result indicates that the number
of populations within each data set is reasonably
estimated, the individual assignment is robust, and the
structure of the inferred population tree corresponds
to the intrinsic relationships among populations within
the data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2014:MCP,
author = "Xiaoqing Liu and Jun Wu and Haipeng Gong and Shengchun
Deng and Zengyou He",
title = "Mining conditional phosphorylation motifs",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "915--927",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2321400",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Phosphorylation motifs represent position-specific
amino acid patterns around the phosphorylation sites in
the set of phosphopeptides. Several algorithms have
been proposed to uncover phosphorylation motifs,
whereas the problem of efficiently discovering a set of
significant motifs with sufficiently high coverage and
non-redundancy still remains unsolved. Here we present
a novel notion called conditional phosphorylation
motifs. Through this new concept, the motifs whose
over-expressiveness mainly benefits from its
constituting parts can be filtered out effectively. To
discover conditional phosphorylation motifs, we propose
an algorithm called C-Motif for a non-redundant
identification of significant phosphorylation motifs.
C-Motif is implemented under the Apriori framework, and
it tests the statistical significance together with the
frequency of candidate motifs in a single stage.
Experiments demonstrate that C-Motif outperforms some
current algorithms such as MMFPh and Motif-All in terms
of coverage and non-redundancy of the results and
efficiency of the execution. The source code of C-Motif
is available at: https://sourceforge.
net/projects/cmotif/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kirkpatrick:2014:PPP,
author = "Bonnie Kirkpatrick and Kristian Stevens",
title = "Perfect phylogeny problems with missing values",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "928--941",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2316005",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The perfect phylogeny problem is of central importance
to both evolutionary biology and population genetics.
Missing values are a common occurrence in both sequence
and genotype data, but they make the problem of finding
a perfect phylogeny NP-hard even for binary characters.
We introduce new and efficient perfect phylogeny
algorithms for broad classes of binary and multistate
data with missing values. Specifically, we address
binary missing data consistent with the rich data
hypothesis (RDH) introduced by Halperin and Karp and
give an efficient algorithm for enumerating
phylogenies. This algorithm is useful for computing the
probability of data with missing values under the
coalescent model. In addition, we use the partition
intersection (PI) graph and chordal graph theory to
generalize the RDH to multi-state characters with
missing values. For a bounded number of states, we
provide a fixed parameter tractable algorithm for the
perfect phylogeny problem with missing data. Utilizing
the PI graph, we are able to show that under multiple
biologically motivated models for character data, our
generalized RDH holds with high probability, and we
evaluate our results with extensive empirical
analysis.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Horta:2014:SMC,
author = "Danilo Horta and Ricardo J. G. B. Campello",
title = "Similarity measures for comparing biclusterings",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "942--954",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2325016",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The comparison of ordinary partitions of a set of
objects is well established in the clustering
literature, which comprehends several studies on the
analysis of the properties of similarity measures for
comparing partitions. However, similarity measures for
clusterings are not readily applicable to
biclusterings, since each bicluster is a tuple of two
sets (of rows and columns), whereas a cluster is only a
single set (of rows). Some biclustering similarity
measures have been defined as minor contributions in
papers which primarily report on proposals and
evaluation of biclustering algorithms or comparative
analyses of biclustering algorithms. The consequence is
that some desirable properties of such measures have
been overlooked in the literature. We review 14
biclustering similarity measures. We define eight
desirable properties of a biclustering measure, discuss
their importance, and prove which properties each of
the reviewed measures has. We show examples drawn and
inspired from important studies in which several
biclustering measures convey misleading evaluations due
to the absence of one or more of the discussed
properties. We also advocate the use of a more general
comparison approach that is based on the idea of
transforming the original problem of comparing
biclusterings into an equivalent problem of comparing
clustering partitions with overlapping clusters.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Suvorova:2014:SPC,
author = "Yulia M. Suvorova and Maria A. Korotkova and Eugene V.
Korotkov",
title = "Study of the paired change points in bacterial genes",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "955--964",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2321154",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "It is known that nucleotide sequences are not totally
homogeneous and this heterogeneity could not be due to
random fluctuations only. Such heterogeneity poses a
problem of making sequence segmentation into a set of
homogeneous parts divided by the points called ``change
points''. In this work we investigated a special case
of change points--paired change points (PCP). We used a
well-known property of coding sequences--triplet
periodicity (TP). The sequences that we are especially
interested in consist of three successive parts: the
first and the last parts have similar TP while the
middle part has different TP type. We aimed to find the
genes with PCP and provide explanation for this
phenomenon. We developed a mathematical method for the
PCP detection based on the new measure of similarity
between TP matrices. We investigated 66,936 bacterial
genes from 17 bacterial genomes and revealed 2,700
genes with PCP and 6,459 genes with single change point
(SCP). We developed a mathematical approach to
visualize the PCP cases. We suppose that PCP could be
associated with double fusion or insertion events. The
results of investigating the sequences with artificial
insertions/fusions and distribution of TP inside the
genome support the idea that the real number of genes
formed by insertion/ fusion events could be 5-7 times
greater than the number of genes revealed in the
present work.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2014:SBN,
author = "Hao Zhang and Xingyuan Wang and Xiaohui Lin",
title = "Synchronization of {Boolean} networks with different
update schemes",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "965--972",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2338313",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, the synchronizations of Boolean
networks with different update schemes (synchronized
Boolean networks and asynchronous Boolean networks) are
investigated. All nodes in Boolean network are
represented in terms of semi-tensor product. First, we
give the concept of inner synchronization and observe
that all nodes in a Boolean network are synchronized
with each other. Second, we investigate the outer
synchronization between a driving Boolean network and a
corresponding response Boolean network. We provide not
only the concept of traditional complete
synchronization, but also the anti-synchronization and
get the anti-synchronization in simulation. Third, we
extend the outer synchronization to asynchronous
Boolean network and get the complete synchronization
between an asynchronous Boolean network and a response
Boolean network. Consequently, theorems for
synchronization of Boolean networks and asynchronous
Boolean networks are derived. Examples are provided to
show the correctness of our theorems.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2014:RSA,
author = "Jing Li and Jun Hu and Matthew Newman and Kejun Liu
and Huanying Ge",
title = "{RNA}-seq analysis pipeline based on {Oshell}
environment",
journal = j-TCBB,
volume = "11",
number = "5",
pages = "973--978",
month = sep,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2321156",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:35 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Advances in transcriptome sequencing (RNA-Seq) have
revolutionized the way to characterize and quantify
transcripts. The breakthroughs in RNA-Seq technologies
give rise to the ever-increasing volumes of data,
making data processing the bottleneck of transcriptome
research. It becomes crucial to develop an efficient
analysis pipeline to automate RNA-Seq data analysis.
Based on Oshell environment, we present here an
ultra-fast and powerful RNA-Seq analysis pipeline for
quality control, sequence alignment, variation
detection, expression quantification and junction
discovery. The pipeline runs on both Linux and Windows
operating systems, with either stand-alone or cluster
computing environment. Parallel computing is also
supported for improved processing speed. Oshell is free
for non-commercial use at http://omicsoft.com/oshell.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huang:2014:SAI,
author = "Yufei Huang and Yidong Chen and Xiaoning Qian",
title = "Selected articles from the {2012 IEEE International
Workshop on Genomic Signal Processing and Statistics
(GENSIPS 2012)}",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "981--983",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2353218",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gregory:2014:LFD,
author = "Karl B. Gregory and Amin A. Momin and Kevin R. Coombes
and Veerabhadran Baladandayuthapani",
title = "Latent feature decompositions for integrative analysis
of multi-platform genomic data",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "984--994",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2325035",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Increased availability of multi-platform genomics data
on matched samples has sparked research efforts to
discover how diverse molecular features interact both
within and between platforms. In addition, simultaneous
measurements of genetic and epigenetic characteristics
illuminate the roles their complex relationships play
in disease progression and outcomes. However,
integrative methods for diverse genomics data are faced
with the challenges of ultra-high dimensionality and
the existence of complex interactions both within and
between platforms. We propose a novel modeling
framework for integrative analysis based on
decompositions of the large number of platform-specific
features into a smaller number of latent features.
Subsequently we build a predictive model for clinical
outcomes accounting for both within --- and
between-platform interactions based on Bayesian model
averaging procedures. Principal components, partial
least squares and non-negative matrix factorization as
well as sparse counterparts of each are used to define
the latent features, and the performance of these
decompositions is compared both on real and simulated
data. The latent feature interactions are shown to
preserve interactions between the original features and
not only aid prediction but also allow explicit
selection of outcome-related features. The methods are
motivated by and applied to a glioblastoma multiforme
data set from The Cancer Genome Atlas to predict
patient survival times integrating gene expression,
microRNA, copy number and methylation data. For the
glioblastoma data, we find a high concordance between
our selected prognostic genes and genes with known
associations with glioblastoma. In addition, our model
discovers several relevant cross-platform interactions
such as copy number variation associated gene dosing
and epigenetic regulation through promoter methylation.
On simulated data, we show that our proposed method
successfully incorporates interactions within and
between genomic platforms to aid accurate prediction
and variable selection. Our methods perform best when
principal components are used to define the latent
features.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Berlow:2014:IAA,
author = "Noah Berlow and Saad Haider and Qian Wan and Mathew
Geltzeiler and Lara E. Davis and Charles Keller and
Ranadip Pal",
title = "An integrated approach to anti-cancer drug sensitivity
prediction",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "995--1008",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2321138",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A framework for design of personalized cancer therapy
requires the ability to predict the sensitivity of a
tumor to anticancer drugs. The predictive modeling of
tumor sensitivity to anti-cancer drugs has primarily
focused on generating functions that map gene
expressions and genetic mutation profiles to drug
sensitivity. In this paper, we present a new approach
for drug sensitivity prediction and combination therapy
design based on integrated functional and genomic
characterizations. The modeling approach when applied
to data from the Cancer Cell Line Encyclopedia shows a
significant gain in prediction accuracy as compared to
elastic net and random forest techniques based on
genomic characterizations. Utilizing a Mouse Embryonal
Rhabdomyosarcoma cell culture and a drug screen of 60
targeted drugs, we show that predictive modeling based
on functional data alone can also produce high accuracy
predictions. The framework also allows us to generate
personalized tumor proliferation circuits to gain
further insights on the individualized biological
pathway.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tian:2014:INB,
author = "Ye Tian and Sean S. Wang and Zhen Zhang and Olga C.
Rodriguez and Emanuel Petricoin and Ie-Ming Shih and
Daniel Chan and Maria Avantaggiati and Guoqiang Yu and
Shaozhen Ye and Robert Clarke and Chao Wang and Bai
Zhang and Yue Wang and Chris Albanese",
title = "Integration of network biology and imaging to study
cancer phenotypes and responses",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1009--1019",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2338304",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Ever growing ``omics'' data and continuously
accumulated biological knowledge provide an
unprecedented opportunity to identify molecular
biomarkers and their interactions that are responsible
for cancer phenotypes that can be accurately defined by
clinical measurements such as in vivo imaging. Since
signaling or regulatory networks are dynamic and
context-specific, systematic efforts to characterize
such structural alterations must effectively
distinguish significant network rewiring from random
background fluctuations. Here we introduced a novel
integration of network biology and imaging to study
cancer phenotypes and responses to treatments at the
molecular systems level. Specifically, Differential
Dependence Network (DDN) analysis was used to detect
statistically significant topological rewiring in
molecular networks between two phenotypic conditions,
and in vivo Magnetic Resonance Imaging (MRI) was used
to more accurately define phenotypic sample groups for
such differential analysis. We applied DDN to analyze
two distinct phenotypic groups of breast cancer and
study how genomic instability affects the molecular
network topologies in high-grade ovarian cancer.
Further, FDA-approved arsenic trioxide (ATO) and the
ND2-SmoA1 mouse model of Medulloblastoma (MB) were used
to extend our analyses of combined MRI and Reverse
Phase Protein Microarray (RPMA) data to assess tumor
responses to ATO and to uncover the complexity of
therapeutic molecular biology.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lu:2014:LPC,
author = "Meng Lu and Hye-Seung Lee and David Hadley and Jianhua
Z. Huang and Xiaoning Qian",
title = "Logistic principal component analysis for rare
variants in gene-environment interaction analysis",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1020--1028",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2322371",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The characteristics of low minor allele frequency
(MAF) and weak individual effects make genome-wide
association studies (GWAS) for rare variant single
nucleotide polymorphisms (SNPs) more difficult when
using conventional statistical methods. By aggregating
the rare variant effects belonging to the same gene,
collapsing is the most common way to enhance the
detection of rare variant effects for association
analyses with a given trait. In this paper, we propose
a novel framework of MAF-based logistic principal
component analysis (MLPCA) to derive aggregated
statistics by explicitly modeling the correlation
between rare variant SNP data, which is categorical.
The derived aggregated statistics by MLPCA can then be
tested as a surrogate variable in regression models to
detect the gene-environment interaction from rare
variants. In addition, MLPCA searches for the optimal
linear combination from the best subset of rare
variants according to MAF that has the maximum
association with the given trait. We compared the power
of our MLPCA-based methods with four existing
collapsing methods in gene-environment interaction
association analysis using both our simulation data set
and Genetic Analysis Workshop 17 (GAW17) data. Our
experimental results have demonstrated that MLPCA on
two forms of genotype data representations achieves
higher statistical power than those existing methods
and can be further improved by introducing the
appropriate sparsity penalty. The performance
improvement by our MLPCA-based methods result from the
derived aggregated statistics by explicitly modeling
categorical SNP data and searching for the maximum
associated subset of SNPs for collapsing, which helps
better capture the combined effect from individual rare
variants and the interaction with environmental
factors.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sajjadi:2014:NBM,
author = "Seyed Javad Sajjadi and Xiaoning Qian and Bo Zeng and
Amin Ahmadi Adl",
title = "Network-based methods to identify highly
discriminating subsets of biomarkers",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1029--1037",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2325014",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Complex diseases such as various types of cancer and
diabetes are conjectured to be triggered and influenced
by a combination of genetic and environmental factors.
To integrate potential effects from interplay among
underlying candidate factors, we propose a new
network-based framework to identify effective
biomarkers by searching for groups of synergistic risk
factors with high predictive power to disease outcome.
An interaction network is constructed with node weights
representing individual predictive power of candidate
factors and edge weights capturing pairwise synergistic
interactions among factors. We then formulate this
network-based biomarker identification problem as a
novel graph optimization model to search for multiple
cliques with maximum overall weight, which we denote as
the Maximum Weighted Multiple Clique Problem (MWMCP).
To achieve optimal or near optimal solutions, both an
analytical algorithm based on column generation method
and a fast heuristic for large-scale networks have been
derived. Our algorithms for MWMCP have been implemented
to analyze two biomedical data sets: a Type 1 Diabetes
(T1D) data set from the Diabetes Prevention Trial-Type
1 (DPT-1) study, and a breast cancer genomics data set
for metastasis prognosis. The results demonstrate that
our network-based methods can identify important
biomarkers with better prediction accuracy compared to
the conventional feature selection that only considers
individual effects.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2014:BSB,
author = "Yanxun Xu and Xiaofeng Zheng and Yuan Yuan and Marcos
R. Estecio and Jean-Pierre Issa and Peng Qiu and Yuan
Ji and Shoudan Liang",
title = "{BM}-{SNP}: a {Bayesian} model for {SNP} calling using
high throughput sequencing data",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1038--1044",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2321407",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A single-nucleotide polymorphism (SNP) is a sole base
change in the DNA sequence and is the most common
polymorphism. Detection and annotation of SNPs are
among the central topics in biomedical research as SNPs
are believed to play important roles on the
manifestation of phenotypic events, such as disease
susceptibility. To take full advantage of the
next-generation sequencing (NGS) technology, we propose
a Bayesian approach, BM-SNP, to identify SNPs based on
the posterior inference using NGS data. In particular,
BM-SNP computes the posterior probability of nucleotide
variation at each covered genomic position using the
contents and frequency of the mapped short reads. The
position with a high posterior probability of
nucleotide variation is flagged as a potential SNP. We
apply BM-SNP to two cell-line NGS data, and the results
show a high ratio of overlap ({$>$95} percent) with the
dbSNP database. Compared with MAQ, BM-SNP identifies
more SNPs that are in dbSNP, with higher quality. The
SNPs that are called only by BM-SNP but not in dbSNP
may serve as new discoveries. The proposed BM-SNP
method integrates information from multiple aspects of
NGS data, and therefore achieves high detection power.
BM-SNP is fast, capable of processing whole genome data
at 20-fold average coverage in a short amount of
time.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Qiu:2014:UHD,
author = "Peng Qiu",
title = "Unfold high-dimensional clouds for exhaustive gating
of flow cytometry data",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1045--1051",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2321403",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Flow cytometry is able to measure the expressions of
multiple proteins simultaneously at the single-cell
level. A flow cytometry experiment on one biological
sample provides measurements of several protein markers
on or inside a large number of individual cells in that
sample. Analysis of such data often aims to identify
subpopulations of cells with distinct phenotypes.
Currently, the most widely used analytical approach in
the flow cytometry community is manual gating on a
sequence of nested biaxial plots, which is highly
subjective, labor intensive, and not exhaustive. To
address those issues, a number of methods have been
developed to automate the gating analysis by clustering
algorithms. However, completely removing the
subjectivity can be quite challenging. This paper
describes an alternative approach. Instead of
automating the analysis, we develop novel
visualizations to facilitate manual gating. The
proposed method views single-cell data of one
biological sample as a high-dimensional point cloud of
cells, derives the skeleton of the cloud, and unfolds
the skeleton to generate 2D visualizations. We
demonstrate the utility of the proposed visualization
using real data, and provide quantitative comparison to
visualizations generated from principal component
analysis and multidimensional scaling.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bitar:2014:BPC,
author = "Main{\'a} Bitar and Gl{\'o}ria Regina Franco",
title = "A basic protein comparative three-dimensional modeling
methodological workflow theory and practice",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1052--1065",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2325018",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "When working with proteins and studying its
properties, it is crucial to have access to the
three-dimensional structure of the molecule. If
experimentally solved structures are not available,
comparative modeling techniques can be used to generate
useful protein models to subsidize structure-based
research projects. In recent years, with Bioinformatics
becoming the basis for the study of protein structures,
there is a crescent need for the exposure of details
about the algorithms behind the software and servers,
as well as a need for protocols to guide in silico
predictive experiments. In this article, we explore
different steps of the comparative modeling technique,
such as template identification, sequence alignment,
generation of candidate structures and quality
assessment, its peculiarities and theoretical
description. We then present a practical step-by-step
workflow, to support the Biologist on the in silico
generation of protein structures. Finally, we explore
further steps on comparative modeling, presenting
perspectives to the study of protein structures through
Bioinformatics. We trust that this is a thorough guide
for beginners that wish to work on the comparative
modeling of proteins.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhan:2014:PEM,
author = "Choujun Zhan and Wuchao Situ and Lam Fat Yeung and
Peter Wai-Ming Tsang and Genke Yang",
title = "A parameter estimation method for biological systems
modelled by {ODE\slash DDE} models using spline
approximation and differential evolution algorithm",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1066--1076",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2322360",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The inverse problem of identifying unknown parameters
of known structure dynamical biological systems, which
are modelled by ordinary differential equations or
delay differential equations, from experimental data is
treated in this paper. A two stage approach is adopted:
first, combine spline theory and Nonlinear Programming
(NLP), the parameter estimation problem is formulated
as an optimization problem with only algebraic
constraints; then, a new differential evolution (DE)
algorithm is proposed to find a feasible solution. The
approach is designed to handle problem of realistic
size with noisy observation data. Three cases are
studied to evaluate the performance of the proposed
algorithm: two are based on benchmark models with
priori-determined structure and parameters; the other
one is a particular biological system with unknown
model structure. In the last case, only a set of
observation data available and in this case a nominal
model is adopted for the identification. All the test
systems were successfully identified by using a
reasonable amount of experimental data within an
acceptable computation time. Experimental evaluation
reveals that the proposed method is capable of fast
estimation on the unknown parameters with good
precision.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Shiraishi:2014:EVA,
author = "Fumihide Shiraishi and Erika Yoshida and Eberhard O.
Voit",
title = "An efficient and very accurate method for calculating
steady-state sensitivities in metabolic reaction
systems",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1077--1086",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2338311",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Stability and sensitivity analyses of biological
systems require the ad hoc writing of computer code,
which is highly dependent on the particular model and
burdensome for large systems. We propose a very
accurate strategy to overcome this challenge. Its core
concept is the conversion of the model into the format
of biochemical systems theory (BST), which greatly
facilitates the computation of sensitivities. First,
the steady state of interest is determined by
integrating the model equations toward the steady state
and then using a Newton--Raphson method to fine-tune
the result. The second step of conversion into the BST
format requires several instances of numerical
differentiation. The accuracy of this task is ensured
by the use of a complex-variable Taylor scheme for all
differentiation steps. The proposed strategy is
implemented in a new software program, COSMOS, which
automates the stability and sensitivity analysis of
essentially arbitrary ODE models in a quick, yet highly
accurate manner. The methods underlying the process are
theoretically analyzed and illustrated with four
representative examples: a simple metabolic reaction
model; a model of aspartate-derived amino acid
biosynthesis; a TCA-cycle model; and a modified
TCA-cycle model. COSMOS has been deposited to
https://github.com/BioprocessdesignLab/COSMOS.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Charuvaka:2014:CPS,
author = "Anveshi Charuvaka and Huzefa Rangwala",
title = "Classifying protein sequences using regularized
multi-task learning",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1087--1098",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2338303",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Classification problems in which several learning
tasks are organized hierarchically pose a special
challenge because the hierarchical structure of the
problems needs to be considered. Multi-task learning
(MTL) provides a framework for dealing with such
interrelated learning tasks. When two different
hierarchical sources organize similar information, in
principle, this combined knowledge can be exploited to
further improve classification performance. We have
studied this problem in the context of protein
structure classification by integrating the learning
process for two hierarchical protein structure
classification database, SCOP and CATH. Our goal is to
accurately predict whether a given protein belongs to a
particular class in these hierarchies using only the
amino acid sequences. We have utilized the recent
developments in multi-task learning to solve the
interrelated classification problems. We have also
evaluated how the various relationships between tasks
affect the classification performance. Our evaluations
show that learning schemes in which both the
classification databases are used outperform the
schemes which utilize only one of them.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{David:2014:CEF,
author = "Laszlo David and Alexander Bockmayr",
title = "Computing elementary flux modes involving a set of
target reactions",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1099--1107",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2343964",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Elementary flux mode (EM) computation is an important
tool in the constraint-based analysis of genome-scale
metabolic networks. Due to the combinatorial complexity
of these networks, as well as the advances in the level
of detail to which they can be reconstructed, an
exhaustive enumeration of all EMs is often not
practical. Therefore, in recent years interest has
shifted towards searching EMs with specific properties.
We present a novel method that allows computing EMs
containing a given set of target reactions. This
generalizes previous algorithms where the set of target
reactions consists of a single reaction. In the
one-reaction case, our method compares favorably to the
previous approaches. In addition, we present several
applications of our algorithm for computing EMs
containing two target reactions in genome-scale
metabolic networks. A software tool implementing the
algorithms described in this paper is available at
https://sourceforge.net/projects/caefm.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rizvi:2014:DRO,
author = "Ahsan Z. Rizvi and C. Bhattacharya",
title = "Detection of replication origin sites in herpesvirus
genomes by clustering and scoring of palindromes with
quadratic entropy measures",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1108--1118",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2330622",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Replication in herpesvirus genomes is a major concern
of public health as they multiply rapidly during the
lytic phase of infection that cause maximum damage to
the host cells. Earlier research has established that
sites of replication origin are dominated by high
concentration of rare palindrome sequences of DNA.
Computational methods are devised based on scoring to
determine the concentration of palindromes. In this
paper, we propose both extraction and localization of
rare palindromes in an automated manner. Discrete
Cosine Transform (DCT-II), a widely recognized image
compression algorithm is utilized here to extract
palindromic sequences based on their reverse
complimentary symmetry property of existence. We
formulate a novel approach to localize the rare
palindrome clusters by devising a Minimum Quadratic
Entropy (MQE) measure based on the Renyi's Quadratic
Entropy (RQE) function. Experimental results over a
large number of herpesvirus genomes show that the RQE
based scoring of rare palindromes have higher order of
sensitivity, and lesser false alarm in detecting
concentration of rare palindromes and thereby sites of
replication origin.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Taha:2014:DSR,
author = "Kamal Taha",
title = "Determining semantically related significant genes",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1119--1130",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2344668",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "GO relation embodies some aspects of existence
dependency. If GO term x is existence-dependent on GO
term y, the presence of y implies the presence of x.
Therefore, the genes annotated with the function of the
GO term y are usually functionally and semantically
related to the genes annotated with the function of the
GO term x. A large number of gene set enrichment
analysis methods have been developed in recent years
for analyzing gene sets enrichment. However, most of
these methods overlook the structural dependencies
between GO terms in GO graph by not considering the
concept of existence dependency. We propose in this
paper a biological search engine called RSGSearch that
identifies enriched sets of genes annotated with
different functions using the concept of existence
dependency. We observe that GO term x cannot be
existence-dependent on GO term y, if x and y have the
same specificity (biological characteristics). After
encoding into a numeric format the contributions of GO
terms annotating target genes to the semantics of their
lowest common ancestors (LCAs), RSGSearch uses
microarray experiment to identify the most significant
LCA that annotates the result genes. We evaluated
RSGSearch experimentally and compared it with five gene
set enrichment systems. Results showed marked
improvement.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rathore:2014:GGE,
author = "Saima Rathore and Mutawarra Hussain and Asifullah
Khan",
title = "{GECC}: gene expression based ensemble classification
of colon samples",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1131--1145",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2344655",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gene expression deviates from its normal composition
in case a patient has cancer. This variation can be
used as an effective tool to find cancer. In this
study, we propose a novel gene expressions based colon
classification scheme (GECC) that exploits the
variations in gene expressions for classifying colon
gene samples into normal and malignant classes. Novelty
of GECC is in two complementary ways. First, to cater
overwhelmingly larger size of gene based data sets,
various feature extraction strategies, like,
chi-square, F-Score, principal component analysis (PCA)
and minimum redundancy and maximum relevancy (mRMR)
have been employed, which select discriminative genes
amongst a set of genes. Second, a majority voting based
ensemble of support vector machine (SVM) has been
proposed to classify the given gene based samples.
Previously, individual SVM models have been used for
colon classification, however, their performance is
limited. In this research study, we propose an
SVM-ensemble based new approach for gene based
classification of colon, wherein the individual SVM
models are constructed through the learning of
different SVM kernels, like, linear, polynomial, radial
basis function (RBF), and sigmoid. The predicted
results of individual models are combined through
majority voting. In this way, the combined decision
space becomes more discriminative. The proposed
technique has been tested on four colon, and several
other binary-class gene expression data sets, and
improved performance has been achieved compared to
previously reported gene based colon cancer detection
techniques. The computational time required for the
training and testing of 208 $ \times $ 5,851 data set
has been 591.01 and 0.019 s, respectively.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liao:2014:GSU,
author = "Bo Liao and Yan Jiang and Wei Liang and Wen Zhu and
Lijun Cai and Zhi Cao",
title = "Gene selection using locality sensitive {Laplacian}
score",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1146--1156",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2328334",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gene selection based on microarray data, is highly
important for classifying tumors accurately. Existing
gene selection schemes are mainly based on ranking
statistics. From manifold learning standpoint, local
geometrical structure is more essential to characterize
features compared with global information. In this
study, we propose a supervised gene selection method
called locality sensitive Laplacian score (LSLS), which
incorporates discriminative information into local
geometrical structure, by minimizing local within-class
information and maximizing local between-class
information simultaneously. In addition, variance
information is considered in our algorithm framework.
Eventually, to find more superior gene subsets, which
is significant for biomarker discovery, a two-stage
feature selection method that combines the LSLS and
wrapper method (sequential forward selection or
sequential backward selection) is presented.
Experimental results of six publicly available gene
expression profile data sets demonstrate the
effectiveness of the proposed approach compared with a
number of state-of-the-art gene selection methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Stock:2014:IFR,
author = "Michiel Stock and Thomas Fober and Eyke
H{\"u}llermeier and Serghei Glinca and Gerhard Klebe
and Tapio Pahikkala and Antti Airola and Bernard {De
Baets} and Willem Waegeman",
title = "Identification of functionally related enzymes by
learning-to-rank methods",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1157--1169",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2338308",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Enzyme sequences and structures are routinely used in
the biological sciences as queries to search for
functionally related enzymes in online databases. To
this end, one usually departs from some notion of
similarity, comparing two enzymes by looking for
correspondences in their sequences, structures or
surfaces. For a given query, the search operation
results in a ranking of the enzymes in the database,
from very similar to dissimilar enzymes, while
information about the biological function of annotated
database enzymes is ignored. In this work, we show that
rankings of that kind can be substantially improved by
applying kernel-based learning algorithms. This
approach enables the detection of statistical
dependencies between similarities of the active cleft
and the biological function of annotated enzymes. This
is in contrast to search-based approaches, which do not
take annotated training data into account. Similarity
measures based on the active cleft are known to
outperform sequence-based or structure-based measures
under certain conditions. We consider the Enzyme
Commission (EC) classification hierarchy for obtaining
annotated enzymes during the training phase. The
results of a set of sizeable experiments indicate a
consistent and significant improvement for a set of
similarity measures that exploit information about
small cavities in the surface of enzymes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mukhopadhyay:2014:INR,
author = "Anirban Mukhopadhyay and Monalisa Mandal",
title = "Identifying non-redundant gene markers from microarray
data: a multiobjective variable length {PSO}-based
approach",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1170--1183",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2323065",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Identifying relevant genes which are responsible for
various types of cancer is an important problem. In
this context, important genes refer to the marker genes
which change their expression level in correlation with
the risk or progression of a disease, or with the
susceptibility of the disease to a given treatment.
Gene expression profiling by microarray technology has
been successfully applied to classification and
diagnostic prediction of cancers. However, extracting
these marker genes from a huge set of genes contained
by the microarray data set is a major problem. Most of
the existing methods for identifying marker genes find
a set of genes which may be redundant in nature.
Motivated by this, a multiobjective optimization method
has been proposed which can find a small set of
non-redundant disease related genes providing high
sensitivity and specificity simultaneously. In this
article, the optimization problem has been modeled as a
multiobjective one which is based on the framework of
variable length particle swarm optimization. Using some
real-life data sets, the performance of the proposed
algorithm has been compared with that of other
state-of-the-art techniques.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zarai:2014:MPT,
author = "Yoram Zarai and Michael Margaliot and Tamir Tuller",
title = "Maximizing protein translation rate in the ribosome
flow model: the homogeneous case",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1184--1195",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2330621",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gene translation is the process in which intracellular
macro-molecules, called ribosomes, decode genetic
information in the mRNA chain into the corresponding
proteins. Gene translation includes several steps.
During the elongation step, ribosomes move along the
mRNA in a sequential manner and link amino-acids
together in the corresponding order to produce the
proteins. The homogeneous ribosome flow model (HRFM) is
a deterministic computational model for
translation-elongation under the assumption of constant
elongation rates along the mRNA chain. The HRFM is
described by a set of n first-order nonlinear ordinary
differential equations, where n represents the number
of sites along the mRNA chain. The HRFM also includes
two positive parameters: ribosomal initiation rate and
the (constant) elongation rate. In this paper, we show
that the steady-state translation rate in the HRFM is a
concave function of its parameters. This means that the
problem of determining the parameter values that
maximize the translation rate is relatively simple. Our
results may contribute to a better understanding of the
mechanisms and evolution of translation-elongation. We
demonstrate this by using the theoretical results to
estimate the initiation rate in M. musculus embryonic
stem cell. The underlying assumption is that evolution
optimized the translation mechanism. For the
infinite-dimensional HRFM, we derive a closed-form
solution to the problem of determining the initiation
and transition rates that maximize the protein
translation rate. We show that these expressions
provide good approximations for the optimal values in
the n -dimensional HRFM already for relatively small
values of n. These results may have applications for
synthetic biology where an important problem is to
re-engineer genomic systems in order to maximize the
protein production rate.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kalathiya:2014:MME,
author = "Umesh Kalathiya and Monikaben Padariya and Maciej
Baginski",
title = "Molecular modeling and evaluation of novel
dibenzopyrrole derivatives as telomerase inhibitors and
potential drug for cancer therapy",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1196--1207",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2326860",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "During previous years, many studies on synthesis, as
well as on anti-tumor, anti-inflammatory and
anti-bacterial activities of the pyrazole derivatives
have been described. Certain pyrazole derivatives
exhibit important pharmacological activities and have
proved to be useful template in drug research.
Considering importance of pyrazole template, in current
work the series of novel inhibitors were designed by
replacing central ring of acridine with pyrazole ring.
These heterocyclic compounds were proposed as a new
potential base for telomerase inhibitors. Obtained
dibenzopyrrole structure was used as a novel scaffold
structure and extension of inhibitors was done by
different functional groups. Docking of newly designed
compounds in the telomerase active site (telomerase
catalytic subunit TERT) was carried out. All
dibenzopyrrole derivatives were evaluated by three
docking programs: CDOCKER, Ligandfit docking (Scoring
Functions) and AutoDock. Compound C\_9g, C\_9k and
C\_9l performed best in comparison to all designed
inhibitors during the docking in all methods and in
interaction analysis. Introduction of pyrazole and
extension of dibenzopyrrole in compounds confirm that
such compound may act as potential telomerase
inhibitors.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mirzal:2014:NTR,
author = "Andri Mirzal",
title = "Nonparametric {Tikhonov} regularized {NMF} and its
application in cancer clustering",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1208--1217",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2328342",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The Tikhonov regularized nonnegative matrix
factorization (TNMF) is an NMF objective function that
enforces smoothness on the computed solutions, and has
been successfully applied to many problem domains
including text mining, spectral data analysis, and
cancer clustering. There is, however, an issue that is
still insufficiently addressed in the development of
TNMF algorithms, i.e., how to develop mechanisms that
can learn the regularization parameters directly from
the data sets. The common approach is to use fixed
values based on a priori knowledge about the problem
domains. However, from the linear inverse problems
study it is known that the quality of the solutions of
the Tikhonov regularized least square problems depends
heavily on the choosing of appropriate regularization
parameters. Since least squares are the building blocks
of the NMF, it can be expected that similar situation
also applies to the NMF. In this paper, we propose two
formulas to automatically learn the regularization
parameters from the data set based on the L-curve
approach. We also develop a convergent algorithm for
the TNMF based on the additive update rules. Finally,
we demonstrate the use of the proposed algorithm in
cancer clustering tasks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kasarapu:2014:RPF,
author = "Parthan Kasarapu and Maria {Garcia De La Banda} and
Arun S. Konagurthu",
title = "On representing protein folding patterns using
non-linear parametric curves",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1218--1228",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2338319",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Proteins fold into complex three-dimensional shapes.
Simplified representations of their shapes are central
to rationalise, compare, classify, and interpret
protein structures. Traditional methods to abstract
protein folding patterns rely on representing their
standard secondary structural elements (helices and
strands of sheet) using line segments. This results in
ignoring a significant proportion of structural
information. The motivation of this research is to
derive mathematically rigorous and biologically
meaningful abstractions of protein folding patterns
that maximize the economy of structural description and
minimize the loss of structural information. We report
on a novel method to describe a protein as a
non-overlapping set of parametric three dimensional
curves of varying length and complexity. Our approach
to this problem is supported by information theory and
uses the statistical framework of minimum message
length (MML) inference. We demonstrate the
effectiveness of our non-linear abstraction to support
efficient and effective comparison of protein folding
patterns on a large scale.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Disanto:2014:NRS,
author = "Filippo Disanto and Noah A. Rosenberg",
title = "On the number of ranked species trees producing
anomalous ranked gene trees",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1229--1238",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2343977",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Analysis of probability distributions conditional on
species trees has demonstrated the existence of
anomalous ranked gene trees (ARGTs), ranked gene trees
that are more probable than the ranked gene tree that
accords with the ranked species tree. Here, to improve
the characterization of ARGTs, we study enumerative and
probabilistic properties of two classes of ranked
labeled species trees, focusing on the presence or
avoidance of certain subtree patterns associated with
the production of ARGTs. We provide exact enumerations
and asymptotic estimates for cardinalities of these
sets of trees, showing that as the number of species
increases without bound, the fraction of all ranked
labeled species trees that are ARGT-producing
approaches 1. This result extends beyond earlier
existence results to provide a probabilistic claim
about the frequency of ARGTs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ahmed:2014:SSC,
author = "Hasin Afzal Ahmed and Priyakshi Mahanta and Dhruba
Kumar Bhattacharyya and Jugal Kumar Kalita",
title = "Shifting-and-scaling correlation based biclustering
algorithm",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1239--1252",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2323054",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The existence of various types of correlations among
the expressions of a group of biologically significant
genes poses challenges in developing effective methods
of gene expression data analysis. The initial focus of
computational biologists was to work with only absolute
and shifting correlations. However, researchers have
found that the ability to handle shifting-and-scaling
correlation enables them to extract more biologically
relevant and interesting patterns from gene microarray
data. In this paper, we introduce an effective
shifting-and-scaling correlation measure named Shifting
and Scaling Similarity (SSSim), which can detect highly
correlated gene pairs in any gene expression data. We
also introduce a technique named Intensive Correlation
Search (ICS) biclustering algorithm, which uses SSSim
to extract biologically significant biclusters from a
gene expression data set. The technique performs
satisfactorily with a number of benchmarked gene
expression data sets when evaluated in terms of
functional categories in Gene Ontology database.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kobayashi:2014:ISB,
author = "Koichi Kobayashi and Kunihiko Hiraishi",
title = "{ILP\slash SMT}-based method for design of {Boolean}
networks based on singleton attractors",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1253--1259",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2325011",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Attractors in gene regulatory networks represent cell
types or states of cells. In system biology and
synthetic biology, it is important to generate gene
regulatory networks with desired attractors. In this
paper, we focus on a singleton attractor, which is also
called a fixed point. Using a Boolean network (BN)
model, we consider the problem of finding Boolean
functions such that the system has desired singleton
attractors and has no undesired singleton attractors.
To solve this problem, we propose a matrix-based
representation of BNs. Using this representation, the
problem of finding Boolean functions can be rewritten
as an Integer Linear Programming (ILP) problem and a
Satisfiability Modulo Theories (SMT) problem.
Furthermore, the effectiveness of the proposed method
is shown by a numerical example on a WNT5A network,
which is related to melanoma. The proposed method
provides us a basic method for design of gene
regulatory networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Loohuis:2014:IDD,
author = "Loes Olde Loohuis and Andreas Witzel and Bud Mishra",
title = "Improving detection of driver genes: power-law null
model of copy number variation in cancer",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1260--1263",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2351805",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, we study Copy Number Variation (CNV)
data. The underlying process generating CNV segments is
generally assumed to be memory-less, giving rise to an
exponential distribution of segment lengths. In this
paper, we provide evidence from cancer patient data,
which suggests that this generative model is too
simplistic, and that segment lengths follow a power-law
distribution instead. We conjecture a simple
preferential attachment generative model that provides
the basis for the observed power-law distribution. We
then show how an existing statistical method for
detecting cancer driver genes can be improved by
incorporating the power-law distribution in the null
model.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2014:OMI,
author = "Haiying Wang and Huiru Zheng",
title = "Organized modularity in the interactome: evidence from
the analysis of dynamic organization in the cell
cycle",
journal = j-TCBB,
volume = "11",
number = "6",
pages = "1264--1270",
month = nov,
year = "2014",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2318715",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Feb 14 10:45:39 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The organization of global protein interaction
networks (PINs) has been extensively studied and
heatedly debated. We revisited this issue in the
context of the analysis of dynamic organization of a
PIN in the yeast cell cycle. Statistically significant
bimodality was observed when analyzing the distribution
of the differences in expression peak between
periodically expressed partners. A close look at their
behavior revealed that date and party hubs derived from
this analysis have some distinct features. There are no
significant differences between them in terms of
protein essentiality, expression correlation and
semantic similarity derived from gene ontology (GO)
biological process hierarchy. However, date hubs
exhibit significantly greater values than party hubs in
terms of semantic similarity derived from both GO
molecular function and cellular component hierarchies.
Relating to three-dimensional structures, we found that
both single and multi-interface proteins could become
date hubs coordinating multiple functions performed at
different times while party hubs are mainly
multiinterface proteins. Furthermore, we constructed
and analyzed a PPI network specific to the human cell
cycle and highlighted that the dynamic organization in
human interactome is far more complex than the
dichotomy of hubs observed in the yeast cell cycle.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2015:EEC,
author = "Ying Xu",
title = "Editorial from the {Editor-in-Chief}",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "1--1",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2394592",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Aluru:2015:GEI,
author = "Srinivas Aluru and Donna K. Slonim",
title = "{Guest Editors}' introduction: selected papers from
{ACM-BCB 2013}",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "2--3",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2389551",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Daniels:2015:MRH,
author = "Noah M. Daniels and Andrew Gallant and Norman Ramsey
and Lenore J. Cowen",
title = "{MRFy}: remote homology detection for beta-structural
proteins using {Markov} random fields and stochastic
search",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "4--16",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2344682",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We introduce MRFy, a tool for protein remote homology
detection that captures beta-strand dependencies in the
Markov random field. Over a set of 11 SCOP
beta-structural superfamilies, MRFy shows a 14 percent
improvement in mean Area Under the Curve for the motif
recognition problem as compared to HMMER, 25 percent
improvement as compared to RAPTOR, 14 percent
improvement as compared to HHPred, and a 18 percent
improvement as compared to CNFPred and RaptorX. MRFy
was implemented in the Haskell functional programming
language, and parallelizes well on multi-core systems.
MRFy is available, as source code as well as an
executable, from http://mrfy.cs.tufts.edu/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Torii:2015:RPG,
author = "Manabu Torii and Cecilia N. Arighi and Gang Li and
Qinghua Wang and Cathy H. Wu and K. Vijay-Shanker",
title = "{RLIMS-P 2.0}: a generalizable rule-based information
extraction system for literature mining of protein
phosphorylation information",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "17--29",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2372765",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We introduce RLIMS-P version 2.0, an enhanced
rule-based information extraction (IE) system for
mining kinase, substrate, and phosphorylation site
information from scientific literature. Consisting of
natural language processing and IE modules, the system
has integrated several new features, including the
capability of processing full-text articles and
generalizability towards different post-translational
modifications (PTMs). To evaluate the system, sets of
abstracts and full-text articles, containing a variety
of textual expressions, were annotated. On the abstract
corpus, the system achieved F-scores of 0.91, 0.92, and
0.95 for kinases, substrates, and sites, respectively.
The corresponding scores on the full-text corpus were
0.88, 0.91, and 0.92. It was additionally evaluated on
the corpus of the 2013 BioNLP-ST GE task, and achieved
an F-score of 0.87 for the phosphorylation core task,
improving upon the results previously reported on the
corpus. Full-scale processing of all abstracts in
MEDLINE and all articles in PubMed Central Open Access
Subset has demonstrated scalability for mining rich
information in literature, enabling its adoption for
biocuration and for knowledge discovery. The new system
is generalizable and it will be adapted to tackle other
major PTM types. RLIMS-P 2.0 online system is available
online (http://proteininformationresource.org/rlimsp/)
and the developed corpora are available from iProLINK
(http://proteininformationresource.org/iprolink/).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2015:PDC,
author = "Kun Wang and Avinash Das and Zheng-Mei Xiong and Kan
Cao and Sridhar Hannenhalli",
title = "Phenotype-dependent coexpression gene clusters:
application to normal and premature ageing",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "30--39",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2359446",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Hutchinson Gilford progeria syndrome (HGPS) is a rare
genetic disease with symptoms of aging at a very early
age. Its molecular basis is not entirely clear,
although profound gene expression changes have been
reported, and there are some known and other presumed
overlaps with normal aging process. Identification of
genes with aging --- or HGPS-associated expression
changes is thus an important problem. However, standard
regression approaches are currently unsuitable for this
task due to limited sample sizes, thus motivating
development of alternative approaches. Here, we report
a novel iterative multiple regression approach that
leverages co-expressed gene clusters to identify gene
clusters whose expression co-varies with age and/or
HGPS. We have applied our approach to novel RNA-seq
profiles in fibroblast cell cultures at three different
cellular ages, both from HGPS patients and normal
samples. After establishing the robustness of our
approach, we perform a comparative investigation of
biological processes underlying normal aging and HGPS.
Our results recapitulate previously known processes
underlying aging as well as suggest numerous unique
processes underlying aging and HGPS. The approach could
also be useful in detecting phenotype-dependent
co-expression gene clusters in other contexts with
limited sample sizes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Faisal:2015:GNA,
author = "Fazle Elahi Faisal and Han Zhao and Tijana
Milenkovi{\'c}",
title = "Global network alignment in the context of aging",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "40--52",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2326862",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Analogous to sequence alignment, network alignment
(NA) can be used to transfer biological knowledge
across species between conserved network regions. NA
faces two algorithmic challenges: (1) Which cost
function to use to capture ``similarities'' between
nodes in different networks? (2) Which alignment
strategy to use to rapidly identify ``high-scoring''
alignments from all possible alignments? We ``break
down'' existing state-of-the-art methods that use both
different cost functions and different alignment
strategies to evaluate each combination of their cost
functions and alignment strategies. We find that a
combination of the cost function of one method and the
alignment strategy of another method beats the existing
methods. Hence, we propose this combination as a novel
superior NA method. Then, since human aging is hard to
study experimentally due to long lifespan, we use NA to
transfer aging-related knowledge from well annotated
model species to poorly annotated human. By doing so,
we produce novel human aging-related knowledge, which
complements currently available knowledge about aging
that has been obtained mainly by sequence alignment. We
demonstrate significant similarity between topological
and functional properties of our novel predictions and
those of known aging-related genes. We are the first to
use NA to learn more about aging.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gabr:2015:RAP,
author = "Haitham Gabr and Andrei Todor and Alin Dobra and Tamer
Kahveci",
title = "Reachability analysis in probabilistic biological
networks",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "53--66",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2343967",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Extra-cellular molecules trigger a response inside the
cell by initiating a signal at special membrane
receptors (i.e., sources), which is then transmitted to
reporters (i.e., targets) through various chains of
interactions among proteins. Understanding whether such
a signal can reach from membrane receptors to reporters
is essential in studying the cell response to
extracellular events. This problem is drastically
complicated due to the unreliability of the interaction
data. In this paper, we develop a novel method, called
PReach (Probabilistic Reachability), that precisely
computes the probability that a signal can reach from a
given collection of receptors to a given collection of
reporters when the underlying signaling network is
uncertain. This is a very difficult computational
problem with no known polynomial-time solution. PReach
represents each uncertain interaction as a bi-variate
polynomial. It transforms the reachability problem to a
polynomial multiplication problem. We introduce novel
polynomial collapsing operators that associate
polynomial terms with possible paths between sources
and targets as well as the cuts that separate sources
from targets. These operators significantly shrink the
number of polynomial terms and thus the running time.
PReach has much better time complexity than the recent
solutions for this problem. Our experimental results on
real data sets demonstrate that this improvement leads
to orders of magnitude of reduction in the running time
over the most recent methods. Availability: All the
data sets used, the software implemented and the
alignments found in this paper are available at
http://bioinformatics.cise.ufl.edu/PReach/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ye:2015:GAM,
author = "Yongtao Ye and David Wai-lok Cheung and Yadong Wang
and Siu-Ming Yiu and Qing Zhang and Tak-Wah Lam and
Hing-Fung Ting",
title = "{GLProbs}: aligning multiple sequences adaptively",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "67--78",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2316820",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper introduces a simple and effective approach
to improve the accuracy of multiple sequence alignment.
We use a natural measure to estimate the similarity of
the input sequences, and based on this measure, we
align the input sequences differently. For example, for
inputs with high similarity, we consider the whole
sequences and align them globally, while for those with
moderately low similarity, we may ignore the flank
regions and align them locally. To test the
effectiveness of this approach, we have implemented a
multiple sequence alignment tool called GLProbs and
compared its performance with about one dozen leading
alignment tools on three benchmark alignment databases,
and GLProbs's alignments have the best scores in almost
all testings. We have also evaluated the practicability
of the alignments of GLProbs by applying the tool to
three biological applications, namely phylogenetic
trees construction, protein secondary structure
prediction and the detection of high risk members for
cervical cancer in the HPV-E6 family, and the results
are very encouraging.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2015:PIR,
author = "Hui Li and Xiaoyi Li and Murali Ramanathan and Aidong
Zhang",
title = "Prediction and informative risk factor selection of
bone diseases",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "79--91",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2330579",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "With the booming of healthcare industry and the
overwhelming amount of electronic health records (EHRs)
shared by healthcare institutions and practitioners, we
take advantage of EHR data to develop an effective
disease risk management model that not only models the
progression of the disease, but also predicts the risk
of the disease for early disease control or prevention.
Existing models for answering these questions usually
fall into two categories: the expert knowledge based
model or the handcrafted feature set based model. To
fully utilize the whole EHR data, we will build a
framework to construct an integrated representation of
features from all available risk factors in the EHR
data and use these integrated features to effectively
predict osteoporosis and bone fractures. We will also
develop a framework for informative risk factor
selection of bone diseases. A pair of models for two
contrast cohorts (e.g., diseased patients versus
non-diseased patients) will be established to
discriminate their characteristics and find the most
informative risk factors. Several empirical results on
a real bone disease data set show that the proposed
framework can successfully predict bone diseases and
select informative risk factors that are beneficial and
useful to guide clinical decisions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{DeJesus:2015:CUM,
author = "Michael A. DeJesus and Thomas R. Ioerger",
title = "Capturing uncertainty by modeling local transposon
insertion frequencies improves discrimination of
essential genes",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "92--102",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2326857",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Transposon mutagenesis experiments enable the
identification of essential genes in bacteria.
Deep-sequencing of mutant libraries provides a large
amount of high-resolution data on essentiality.
Statistical methods developed to analyze this data have
traditionally assumed that the probability of observing
a transposon insertion is the same across the genome.
This assumption, however, is inconsistent with the
observed insertion frequencies from transposon mutant
libraries of M. tuberculosis. We propose a modified
Binomial model of essentiality that can characterize
the insertion probability of individual genes in which
we allow local variation in the background insertion
frequency in different non-essential regions of the
genome. Using the Metropolis--Hastings algorithm,
samples of the posterior insertion probabilities were
obtained for each gene, and the probability of each
gene being essential is estimated. We compared our
predictions to those of previous methods and show that,
by taking into consideration local insertion
frequencies, our method is capable of making more
conservative predictions that better match what is
experimentally known about essential and non-essential
genes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Spencer:2015:DLN,
author = "Matt Spencer and Jesse Eickholt and Jianlin Cheng",
title = "A deep learning network approach to ab initio protein
secondary structure prediction",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "103--112",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2343960",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Ab initio protein secondary structure (SS) predictions
are utilized to generate tertiary structure
predictions, which are increasingly demanded due to the
rapid discovery of proteins. Although recent
developments have slightly exceeded previous methods of
SS prediction, accuracy has stagnated around 80 percent
and many wonder if prediction cannot be advanced beyond
this ceiling. Disciplines that have traditionally
employed neural networks are experimenting with novel
deep learning techniques in attempts to stimulate
progress. Since neural networks have historically
played an important role in SS prediction, we wanted to
determine whether deep learning could contribute to the
advancement of this field as well. We developed an SS
predictor that makes use of the position-specific
scoring matrix generated by PSI-BLAST and deep learning
network architectures, which we call DNSS. Graphical
processing units and CUDA software optimize the deep
network architecture and efficiently train the deep
networks. Optimal parameters for the training process
were determined, and a workflow comprising three
separately trained deep networks was constructed in
order to make refined predictions. This deep learning
network approach was used to predict SS for a fully
independent test dataset of 198 proteins, achieving a
Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2
percent.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liao:2015:HCM,
author = "Bo Liao and Xiong Li and Lijun Cai and Zhi Cao and
Haowen Chen",
title = "A hierarchical clustering method of selecting kernel
{SNP} to unify informative {SNP} and tag {SNP}",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "113--122",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2351797",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Various strategies can be used to select
representative single nucleotide polymorphisms (SNPs)
from a large number of SNPs, such as tag SNP for
haplotype coverage and informative SNP for haplotype
reconstruction, respectively. Representative SNPs are
not only instrumental in reducing the cost of
genotyping, but also serve an important function in
narrowing the combinatorial space in epistasis
analysis. The capacity of kernel SNPs to unify
informative SNP and tag SNP is explored, and
inconsistencies are minimized in further studies. The
correlation between multiple SNPs is formalized using
multi-information measures. In extending the
correlation, a distance formula for measuring the
similarity between clusters is first designed to
conduct hierarchical clustering. Hierarchical
clustering consists of both information gain and
haplotype diversity, so that the proposed approach can
achieve unification. The kernel SNPs are then selected
from every cluster through the top rank or backward
elimination scheme. Using these kernel SNPs, extensive
experimental comparisons are conducted between
informative SNPs on haplotype reconstruction accuracy
and tag SNPs on haplotype coverage. Results indicate
that the kernel SNP can practically unify informative
SNP and tag SNP and is therefore adaptable to various
applications.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chan:2015:MPP,
author = "Shing-Chow Chan and Li Zhang and Ho-Chun Wu and
Kai-Man Tsui",
title = "A maximum a posteriori probability and time-varying
approach for inferring gene regulatory networks from
time course gene microarray data",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "123--135",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2343951",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Unlike most conventional techniques with static model
assumption, this paper aims to estimate the
time-varying model parameters and identify significant
genes involved at different timepoints from time course
gene microarray data. We first formulate the parameter
identification problem as a new maximum a posteriori
probability estimation problem so that prior
information can be incorporated as regularization terms
to reduce the large estimation variance of the high
dimensional estimation problem. Under this framework,
sparsity and temporal consistency of the model
parameters are imposed using L 1-regularization and
novel continuity constraints, respectively. The
resulting problem is solved using the L-BFGS method
with the initial guess obtained from the partial least
squares method. A novel forward validation measure is
also proposed for the selection of regularization
parameters, based on both forward and current
prediction errors. The proposed method is evaluated
using a synthetic benchmark testing data and a publicly
available yeast Saccharomyces cerevisiae cell cycle
microarray data. For the latter particularly, a number
of significant genes identified at different timepoints
are found to be biological significant according to
previous findings in biological experiments. These
suggest that the proposed approach may serve as a
valuable tool for inferring time-varying gene
regulatory networks in biological studies.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fan:2015:AMD,
author = "Yetian Fan and Wei Wu and Jie Yang and Wenyu Yang and
Rongrong Liu",
title = "An algorithm for motif discovery with iteration on
lengths of motifs",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "136--141",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2351793",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Analysis of DNA sequence motifs is becoming
increasingly important in the study of gene regulation,
and the identification of motif in DNA sequences is a
complex problem in computational biology. Motif
discovery has attracted the attention of more and more
researchers, and varieties of algorithms have been
proposed. Most existing motif discovery algorithms fix
the motif's length as one of the input parameters. In
this paper, a novel method is proposed to identify the
optimal length of the motif and the optimal motif with
that length, through an iteration process on increasing
length numbers. For each fixed length, a modified
genetic algorithm (GA) is used for finding the optimal
motif with that length. Three operators are used in the
modified GA: Mutation that is similar to the one used
in usual GA but is modified to avoid local optimum in
our case, and Addition and Deletion that are proposed
by us for the problem. A criterion is given for
singling out the optimal length in the increasing
motif's lengths. We call this method AMDILM (an
algorithm for motif discovery with iteration on lengths
of motifs). The experiments on simulated data and real
biological data show that AMDILM can accurately
identify the optimal motif length. Meanwhile, the
optimal motifs discovered by AMDILM are consistent with
the real ones and are similar with the motifs obtained
by the three well-known methods: Gibbs Sampler, MEME
and Weeder.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wong:2015:DBC,
author = "Man-Hon Wong and Ho-Yin Sze-To and Leung-Yau Lo and
Tak-Ming Chan and Kwong-Sak Leung",
title = "Discovering binding cores in {Protein--DNA} binding
using association rule mining with statistical
measures",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "142--154",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2343952",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Understanding binding cores is of fundamental
importance in deciphering Protein--DNA (TF-TFBS)
binding and for the deep understanding of gene
regulation. Traditionally, binding cores are identified
in resolved high-resolution 3D structures. However, it
is expensive, labor-intensive and time-consuming to
obtain these structures. Hence, it is promising to
discover binding cores computationally on a large
scale. Previous studies successfully applied
association rule mining to discover binding cores from
TF-TFBS binding sequence data only. Despite the
successful results, there are limitations such as the
use of tight support and confidence thresholds, the
distortion by statistical bias in counting pattern
occurrences, and the lack of a unified scheme to rank
TF-TFBS associated patterns. In this study, we proposed
an association rule mining algorithm incorporating
statistical measures and ranking to address these
limitations. Experimental results demonstrated that,
even when the threshold on support was lowered to
one-tenth of the value used in previous studies, a
satisfactory verification ratio was consistently
observed under different confidence levels. Moreover,
we proposed a novel ranking scheme for TF-TFBS
associated patterns based on p-values and co-support
values. By comparing with other discovery approaches,
the effectiveness of our algorithm was demonstrated.
Eighty-four binding cores with PDB support are uniquely
identified.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gorecki:2015:GTD,
author = "Pawe G{\'o}recki and Oliver Eulenstein",
title = "Gene tree diameter for deep coalescence",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "155--165",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2351795",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The deep coalescence cost accounts for discord caused
by deep coalescence between a gene tree and a species
tree. It is a major concern that the diameter of a gene
tree (the tree's maximum deep coalescence cost across
all species trees) depends on its topology, which can
largely obfuscate phylogenetic studies. While this bias
can be compensated by normalizing the deep coalescence
cost using diameters, obtaining them efficiently has
been posed as an open problem by Than and Rosenberg
[33]. Here, we resolve this problem by describing a
linear time algorithm to compute the diameter of a gene
tree. In addition, we provide a complete classification
of the species trees yielding this diameter to guide
phylogenetic analyses.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2015:HCF,
author = "Chao Wang and Xi Li and Peng Chen and Aili Wang and
Xuehai Zhou and Hong Yu",
title = "Heterogeneous cloud framework for big data genome
sequencing",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "166--178",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2351800",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The next generation genome sequencing problem with
short (long) reads is an emerging field in numerous
scientific and big data research domains. However, data
sizes and ease of access for scientific researchers are
growing and most current methodologies rely on one
acceleration approach and so cannot meet the
requirements imposed by explosive data scales and
complexities. In this paper, we propose a novel
FPGA-based acceleration solution with MapReduce
framework on multiple hardware accelerators. The
combination of hardware acceleration and MapReduce
execution flow could greatly accelerate the task of
aligning short length reads to a known reference
genome. To evaluate the performance and other metrics,
we conducted a theoretical speedup analysis on a
MapReduce programming platform, which demonstrates that
our proposed architecture have efficient potential to
improve the speedup for large scale genome sequencing
applications. Also, as a practical study, we have built
a hardware prototype on the real Xilinx FPGA chip.
Significant metrics on speedup, sensitivity, mapping
quality, error rate, and hardware cost are evaluated,
respectively. Experimental results demonstrate that the
proposed platform could efficiently accelerate the next
generation sequencing problem with satisfactory
accuracy and acceptable hardware cost.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Peng:2015:IPC,
author = "Wei Peng and Jianxin Wang and Bihai Zhao and Lusheng
Wang",
title = "Identification of protein complexes using weighted
{PageRank--Nibble} algorithm and core-attachment
structure",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "179--192",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2343954",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein complexes play a significant role in
understanding the underlying mechanism of most cellular
functions. Recently, many researchers have explored
computational methods to identify protein complexes
from protein-protein interaction (PPI) networks. One
group of researchers focus on detecting local dense
subgraphs which correspond to protein complexes by
considering local neighbors. The drawback of this kind
of approach is that the global information of the
networks is ignored. Some methods such as Markov
Clustering algorithm (MCL), PageRank--Nibble are
proposed to find protein complexes based on random walk
technique which can exploit the global structure of
networks. However, these methods ignore the inherent
core-attachment structure of protein complexes and
treat adjacent node equally. In this paper, we design a
weighted PageRank--Nibble algorithm which assigns each
adjacent node with different probability, and propose a
novel method named WPNCA to detect protein complex from
PPI networks by using weighted PageRank--Nibble
algorithm and core-attachment structure. Firstly, WPNCA
partitions the PPI networks into multiple dense
clusters by using weighted PageRank--Nibble algorithm.
Then the cores of these clusters are detected and the
rest of proteins in the clusters will be selected as
attachments to form the final predicted protein
complexes. The experiments on yeast data show that
WPNCA outperforms the existing methods in terms of both
accuracy and p-value. The software for WPNCA is
available at
``http://netlab.csu.edu.cn/bioinfomatics/weipeng/WPNCA/download.html''",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Du:2015:IAC,
author = "Nan Du and Marc R. Knecht and Mark T. Swihart and
Zhenghua Tang and Tiffany R. Walsh and Aidong Zhang",
title = "Identifying affinity classes of inorganic materials
binding sequences via a graph-based model",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "193--204",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2321158",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Rapid advances in bionanotechnology have recently
generated growing interest in identifying peptides that
bind to inorganic materials and classifying them based
on their inorganic material affinities. However, there
are some distinct characteristics of inorganic
materials binding sequence data that limit the
performance of many widely-used classification methods
when applied to this problem. In this paper, we propose
a novel framework to predict the affinity classes of
peptide sequences with respect to an associated
inorganic material. We first generate a large set of
simulated peptide sequences based on an amino acid
transition matrix tailored for the specific inorganic
material. Then the probability of test sequences
belonging to a specific affinity class is calculated by
minimizing an objective function. In addition, the
objective function is minimized through iterative
propagation of probability estimates among sequences
and sequence clusters. Results of computational
experiments on two real inorganic material binding
sequence data sets show that the proposed framework is
highly effective for identifying the affinity classes
of inorganic material binding sequences. Moreover, the
experiments on the structural classification of
proteins (SCOP) data set shows that the proposed
framework is general and can be applied to traditional
protein sequences.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhu:2015:PIM,
author = "Xiangyuan Zhu and Kenli Li and Ahmad Salah and Lin Shi
and Keqin Li",
title = "Parallel implementation of {MAFFT} on {CUDA}-enabled
graphics hardware",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "205--218",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2351801",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Multiple sequence alignment (MSA) constitutes an
extremely powerful tool for many biological
applications including phylogenetic tree estimation,
secondary structure prediction, and critical residue
identification. However, aligning large biological
sequences with popular tools such as MAFFT requires
long runtimes on sequential architectures. Due to the
ever increasing sizes of sequence databases, there is
increasing demand to accelerate this task. In this
paper, we demonstrate how graphic processing units
(GPUs), powered by the compute unified device
architecture (CUDA), can be used as an efficient
computational platform to accelerate the MAFFT
algorithm. To fully exploit the GPU's capabilities for
accelerating MAFFT, we have optimized the sequence data
organization to eliminate the bandwidth bottleneck of
memory access, designed a memory allocation and reuse
strategy to make full use of limited memory of GPUs,
proposed a new modified-run-length encoding (MRLE)
scheme to reduce memory consumption, and used
high-performance shared memory to speed up I/O
operations. Our implementation tested in three NVIDIA
GPUs achieves speedup up to 11.28 on a Tesla K20m GPU
compared to the sequential MAFFT 7.015.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2015:PPF,
author = "Guoxian Yu and Huzefa Rangwala and Carlotta Domeniconi
and Guoji Zhang and Zili Zhang",
title = "Predicting protein function using multiple kernels",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "219--233",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2351821",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "High-throughput experimental techniques provide a wide
variety of heterogeneous proteomic data sources. To
exploit the information spread across multiple sources
for protein function prediction, these data sources are
transformed into kernels and then integrated into a
composite kernel. Several methods first optimize the
weights on these kernels to produce a composite kernel,
and then train a classifier on the composite kernel. As
such, these approaches result in an optimal composite
kernel, but not necessarily in an optimal classifier.
On the other hand, some approaches optimize the loss of
binary classifiers and learn weights for the different
kernels iteratively. For multi-class or multi-label
data, these methods have to solve the problem of
optimizing weights on these kernels for each of the
labels, which are computationally expensive and ignore
the correlation among labels. In this paper, we propose
a method called Predicting Protein Function using
Multiple Kernels (ProMK). ProMK iteratively optimizes
the phases of learning optimal weights and reduces the
empirical loss of multi-label classifier for each of
the labels simultaneously. ProMK can integrate kernels
selectively and downgrade the weights on noisy kernels.
We investigate the performance of ProMK on several
publicly available protein function prediction
benchmarks and synthetic datasets. We show that the
proposed approach performs better than previously
proposed protein function prediction approaches that
integrate multiple data sources and multi-label
multiple kernel learning methods. The codes of our
proposed method are available at
https://sites.google.com/site/guoxian85/promk.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Keijsper:2015:TCB,
author = "Judith Keijsper and Tim Oosterwijk",
title = "Tractable cases of $ (*, 2)$-bounded parsimony
haplotyping",
journal = j-TCBB,
volume = "12",
number = "1",
pages = "234--247",
month = jan,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2352031",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Parsimony haplotyping is the problem of finding a set
of haplotypes of minimum cardinality that explains a
given set of genotypes, where a genotype is explained
by two haplotypes if it can be obtained as a
combination of the two. This problem is NP-complete in
the general case, but polynomially solvable for ( k, l
)-bounded instances for certain k and l. Here, k
denotes the maximum number of ambiguous sites in any
genotype, and l is the maximum number of genotypes that
are ambiguous at the same site. Only the complexity of
the (*, 2)-bounded problem is still unknown, where *
denotes no restriction. It has been proved that (*,
2)-bounded instances have compatibility graphs that can
be constructed from cliques and circuits by pasting
along an edge. In this paper, we give a constructive
proof of the fact that (*, 2)-bounded instances are
polynomially solvable if the compatibility graph is
constructed by pasting cliques, trees and circuits
along a bounded number of edges. We obtain this proof
by solving a slightly generalized problem on circuits,
trees and cliques respectively, and arguing that all
possible combinations of optimal solutions for these
graphs that are pasted along a bounded number of edges
can be enumerated efficiently.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Luo:2015:GES,
author = "Feng Luo and Xintao Wu",
title = "Guest editorial for special section on {BIBM 2013}",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "252--253",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2410132",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jiang:2015:PMI,
author = "Xingpeng Jiang and Xiaohua Hu and Weiwei Xu and E. K.
Park",
title = "Predicting microbial interactions using vector
autoregressive model with graph regularization",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "254--261",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2338298",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Microbial interactions play important roles on the
structure and function of complex microbial
communities. With the rapid accumulation of
high-throughput metagenomic or 16S rRNA sequencing
data, it is possible to infer complex microbial
interactions. Co-occurrence patterns of microbial
species among multiple samples are often utilized to
infer interactions. There are few methods to consider
the temporally interacting patterns among microbial
species. In this paper, we present a Graph-regularized
Vector Autoregressive (GVAR) model to infer causal
relationships among microbial entities. The new model
has advantage comparing to the original vector
autoregressive (VAR) model. Specifically, GVAR can
incorporate similarity information for microbial
interaction inference --- i.e., GVAR assumed that if
two species are similar in the previous stage, they
tend to have similar influence on the other species in
the next stage. We apply the model on a time series
dataset of human gut microbiome which was treated with
repeated antibiotics. The experimental results indicate
that the new approach has better performance than
several other VAR-based models and demonstrate its
capability of extracting relevant microbial
interactions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wan:2015:PPL,
author = "Cen Wan and Alex A. Freitas and Jo{\~a}o Pedro {De
Magalh{\~a}es}",
title = "Predicting the pro-longevity or anti-longevity effect
of model organism genes with new hierarchical feature
selection methods",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "262--275",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2355218",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Ageing is a highly complex biological process that is
still poorly understood. With the growing amount of
ageing-related data available on the web, in particular
concerning the genetics of ageing, it is timely to
apply data mining methods to that data, in order to try
to discover novel patterns that may assist ageing
research. In this work, we introduce new hierarchical
feature selection methods for the classification task
of data mining and apply them to ageing-related data
from four model organisms: Caenorhabditis elegans
(worm), Saccharomyces cerevisiae (yeast), Drosophila
melanogaster (fly), and Mus musculus (mouse). The main
novel aspect of the proposed feature selection methods
is that they exploit hierarchical relationships in the
set of features (Gene Ontology terms) in order to
improve the predictive accuracy of the Na{\"\i}ive
Bayes and 1-Nearest Neighbour (1-NN) classifiers, which
are used to classify model organisms' genes into
pro-longevity or anti-longevity genes. The results show
that our hierarchical feature selection methods, when
used together with Na{\"\i}ive Bayes and 1-NN
classifiers, obtain higher predictive accuracy than the
standard (without feature selection) Na{\"\i}ive Bayes
and 1-NN classifiers, respectively. We also discuss the
biological relevance of a number of Gene Ontology terms
very frequently selected by our algorithms in our
datasets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Peng:2015:UAI,
author = "Wei Peng and Jianxin Wang and Yingjiao Cheng and Yu Lu
and Fangxiang Wu and Yi Pan",
title = "{UDoNC}: an algorithm for identifying essential
proteins based on protein domains and protein--protein
interaction networks",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "276--288",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2338317",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Prediction of essential proteins which are crucial to
an organism's survival is important for disease
analysis and drug design, as well as the understanding
of cellular life. The majority of prediction methods
infer the possibility of proteins to be essential by
using the network topology. However, these methods are
limited to the completeness of available
protein--protein interaction (PPI) data and depend on
the network accuracy. To overcome these limitations,
some computational methods have been proposed. However,
seldom of them solve this problem by taking
consideration of protein domains. In this work, we
first analyze the correlation between the essentiality
of proteins and their domain features based on data of
13 species. We find that the proteins containing more
protein domain types which rarely occur in other
proteins tend to be essential. Accordingly, we propose
a new prediction method, named UDoNC, by combining the
domain features of proteins with their topological
properties in PPI network. In UDoNC, the essentiality
of proteins is decided by the number and the frequency
of their protein domain types, as well as the
essentiality of their adjacent edges measured by edge
clustering coefficient. The experimental results on S.
cerevisiae data show that UDoNC outperforms other
existing methods in terms of area under the curve
(AUC). Additionally, UDoNC can also perform well in
predicting essential proteins on data of E. coli.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mei:2015:ESN,
author = "Yongguo Mei and Adria Carbo and Stefan Hoops and
Raquel Hontecillas and Josep Bassaganya-Riera",
title = "{ENISI SDE}: a new web-based tool for modeling
stochastic processes",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "289--297",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2351823",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Modeling and simulations approaches have been widely
used in computational biology, mathematics,
bioinformatics and engineering to represent complex
existing knowledge and to effectively generate novel
hypotheses. While deterministic modeling strategies are
widely used in computational biology, stochastic
modeling techniques are not as popular due to a lack of
user-friendly tools. This paper presents ENISI SDE, a
novel web-based modeling tool with stochastic
differential equations. ENISI SDE provides
user-friendly web user interfaces to facilitate
adoption by immunologists and computational biologists.
This work provides three major contributions: (1)
discussion of SDE as a generic approach for stochastic
modeling in computational biology; (2) development of
ENISI SDE, a web-based user-friendly SDE modeling tool
that highly resembles regular ODE-based modeling; (3)
applying ENISI SDE modeling tool through a use case for
studying stochastic sources of cell heterogeneity in
the context of CD4+ T cell differentiation. The CD4+ T
cell differential ODE model has been published [8] and
can be downloaded from biomodels.net. The case study
reproduces a biological phenomenon that is not captured
by the previously published ODE model and shows the
effectiveness of SDE as a stochastic modeling approach
in biology in general and immunology in particular and
the power of ENISI SDE.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pang:2015:IAS,
author = "Bin Pang and David Schlessman and Xingyan Kuang and
Nan Zhao and Daniel Shyu and Dmitry Korkin and Chi-Ren
Shyu",
title = "An integrated approach to sequence-independent local
alignment of protein binding sites",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "298--308",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2355208",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Accurate alignment of protein--protein binding sites
can aid in protein docking studies and constructing
templates for predicting structure of protein
complexes, along with in-depth understanding of
evolutionary and functional relationships. However,
over the past three decades, structural alignment
algorithms have focused predominantly on global
alignments with little effort on the alignment of local
interfaces. In this paper, we introduce the PBSalign
(Protein--protein Binding Site alignment) method, which
integrates techniques in graph theory, 3D localized
shape analysis, geometric scoring, and utilization of
physicochemical and geometrical properties.
Computational results demonstrate that PBSalign is
capable of identifying similar homologous and analogous
binding sites accurately and performing alignments with
better geometric match measures than existing
protein--protein interface comparison tools. The
proportion of better alignment quality generated by
PBSalign is 46, 56, and 70 percent more than iAlign as
judged by the average match index (MI), similarity
index (SI), and structural alignment score (SAS),
respectively. PBSalign provides the life science
community an efficient and accurate solution to
binding-site alignment while striking the balance
between topological details and computational
complexity.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cho:2015:PFR,
author = "Young-Rae Cho and Yanan Xin and Greg Speegle",
title = "{P-Finder}: reconstruction of signaling networks from
protein--protein interactions and {GO} annotations",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "309--321",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2355216",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Because most complex genetic diseases are caused by
defects of cell signaling, illuminating a signaling
cascade is essential for understanding their
mechanisms. We present three novel computational
algorithms to reconstruct signaling networks between a
starting protein and an ending protein using
genome-wide protein--protein interaction (PPI) networks
and gene ontology (GO) annotation data. A signaling
network is represented as a directed acyclic graph in a
merged form of multiple linear pathways. An advanced
semantic similarity metric is applied for weighting
PPIs as the preprocessing of all three methods. The
first algorithm repeatedly extends the list of nodes
based on path frequency towards an ending protein. The
second algorithm repeatedly appends edges based on the
occurrence of network motifs which indicate the link
patterns more frequently appearing in a PPI network
than in a random graph. The last algorithm uses the
information propagation technique which iteratively
updates edge orientations based on the path strength
and merges the selected directed edges. Our
experimental results demonstrate that the proposed
algorithms achieve higher accuracy than previous
methods when they are tested on well-studied pathways
of S. cerevisiae. Furthermore, we introduce an
interactive web application tool, called P-Finder, to
visualize reconstructed signaling networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jeong:2015:NSF,
author = "Jong Cheol Jeong and Xuewen Chen",
title = "A new semantic functional similarity over gene
ontology",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "322--334",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2343963",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Identifying functionally similar or closely related
genes and gene products has significant impacts on
biological and clinical studies as well as drug
discovery. In this paper, we propose an effective and
practically useful method measuring both gene and gene
product similarity by integrating the topology of gene
ontology, known functional domains and their functional
annotations. The proposed method is comprehensively
evaluated through statistical analysis of the
similarities derived from sequence, structure and
phylogenetic profiles, and clustering analysis of
disease genes clusters. Our results show that the
proposed method clearly outperforms other conventional
methods. Furthermore, literature analysis also reveals
that the proposed method is both statistically and
biologically promising for identifying functionally
similar genes or gene products. In particular, we
demonstrate that the proposed functional similarity
metric is capable of discovering new disease-related
genes or gene products.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ashtawy:2015:CAP,
author = "Hossam M. Ashtawy and Nihar R. Mahapatra",
title = "A comparative assessment of predictive accuracies of
conventional and machine learning scoring functions for
protein--ligand binding affinity prediction",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "335--347",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2351824",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Accurately predicting the binding affinities of large
diverse sets of protein--ligand complexes efficiently
is a key challenge in computational biomolecular
science, with applications in drug discovery, chemical
biology, and structural biology. Since a scoring
function (SF) is used to score, rank, and identify
potential drug leads, the fidelity with which it
predicts the affinity of a ligand candidate for a
protein's binding site has a significant bearing on the
accuracy of virtual screening. Despite intense efforts
in developing conventional SFs, which are either
force-field based, knowledge-based, or empirical, their
limited predictive accuracy has been a major roadblock
toward cost-effective drug discovery. Therefore, in
this work, we explore a range of novel SFs employing
different machine-learning (ML) approaches in
conjunction with a variety of physicochemical and
geometrical features characterizing protein--ligand
complexes. We assess the scoring accuracies of these
new ML SFs as well as those of conventional SFs in the
context of the 2007 and 2010 PDBbind benchmark datasets
on both diverse and protein--family-specific test sets.
We also investigate the influence of the size of the
training dataset and the type and number of features
used on scoring accuracy. We find that the best
performing ML SF has a Pearson correlation coefficient
of 0.806 between predicted and measured binding
affinities compared to 0.644 achieved by a
state-of-the-art conventional SF. We also find that ML
SFs benefit more than their conventional counterparts
from increases in the number of features and the size
of training dataset. In addition, they perform better
on novel proteins that they were never trained on
before.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yang:2015:FDW,
author = "Lina Yang and Yuan Yan Tang and Yang Lu and Huiwu
Luo",
title = "A fractal dimension and wavelet transform based method
for protein sequence similarity analysis",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "348--369",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2363480",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "One of the key tasks related to proteins is the
similarity comparison of protein sequences in the area
of bioinformatics and molecular biology, which helps
the prediction and classification of protein structure
and function. It is a significant and open issue to
find similar proteins from a large scale of protein
database efficiently. This paper presents a new
distance based protein similarity analysis using a new
encoding method of protein sequence which is based on
fractal dimension. The protein sequences are first
represented into the 1-dimensional feature vectors by
their biochemical quantities. A series of Hybrid method
involving discrete Wavelet transform, Fractal dimension
calculation (HWF) with sliding window are then applied
to form the feature vector. At last, through the
similarity calculation, we can obtain the distance
matrix, by which, the phylogenic tree can be
constructed. We apply this approach by analyzing the
ND5 (NADH dehydrogenase subunit 5) protein cluster data
set. The experimental results show that the proposed
model is more accurate than the existing ones such as
Su's model, Zhang's model, Yao's model and MEGA
software, and it is consistent with some known
biological facts.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Konur:2015:PDM,
author = "Savas Konur and Marian Gheorghe",
title = "A property-driven methodology for formal analysis of
synthetic biology systems",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "360--371",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2362531",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper proposes a formal methodology to analyse
bio-systems, in particular synthetic biology systems.
An integrative analysis perspective combining different
model checking approaches based on different property
categories is provided. The methodology is applied to
the synthetic pulse generator system and several
verification experiments are carried out to demonstrate
the use of our approach to formally analyse various
aspects of synthetic biology systems.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2015:TPB,
author = "Min Li and Yu Lu and Jianxin Wang and Fang-Xiang Wu
and Yi Pan",
title = "A topology potential-based method for identifying
essential proteins from {PPI} networks",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "372--383",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2361350",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Essential proteins are indispensable for cellular
life. It is of great significance to identify essential
proteins that can help us understand the minimal
requirements for cellular life and is also very
important for drug design. However, identification of
essential proteins based on experimental approaches are
typically time-consuming and expensive. With the
development of high-throughput technology in the
post-genomic era, more and more protein--protein
interaction data can be obtained, which make it
possible to study essential proteins from the network
level. There have been a series of computational
approaches proposed for predicting essential proteins
based on network topologies. Most of these topology
based essential protein discovery methods were to use
network centralities. In this paper, we investigate the
essential proteins' topological characters from a
completely new perspective. To our knowledge it is the
first time that topology potential is used to identify
essential proteins from a protein--protein interaction
(PPI) network. The basic idea is that each protein in
the network can be viewed as a material particle which
creates a potential field around itself and the
interaction of all proteins forms a topological field
over the network. By defining and computing the value
of each protein's topology potential, we can obtain a
more precise ranking which reflects the importance of
proteins from the PPI network. The experimental results
show that topology potential-based methods TP and TP-NC
outperform traditional topology measures: degree
centrality (DC), betweenness centrality (BC), closeness
centrality (CC), subgraph centrality (SC), eigenvector
centrality (EC), information centrality (IC), and
network centrality (NC) for predicting essential
proteins. In addition, these centrality measures are
improved on their performance for identifying essential
proteins in biological network when controlled by
topology potential.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2015:EEA,
author = "Qiang Yu and Hongwei Huo and Jeffrey Scott Vitter and
Jun Huan and Yakov Nekrich",
title = "An efficient exact algorithm for the motif stem search
problem over large alphabets",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "384--397",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2361668",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In recent years, there has been an increasing interest
in planted ( l, d ) motif search (PMS) with
applications to discovering significant segments in
biological sequences. However, there has been little
discussion about PMS over large alphabets. This paper
focuses on motif stem search (MSS), which is recently
introduced to search motifs on large-alphabet inputs. A
motif stem is an l -length string with some wildcards.
The goal of the MSS problem is to find a set of stems
that represents a superset of all ( l, d ) motifs
present in the input sequences, and the superset is
expected to be as small as possible. The three main
contributions of this paper are as follows: (1) We
build motif stem representation more precisely by using
regular expressions. (2) We give a method for
generating all possible motif stems without redundant
wildcards. (3) We propose an efficient exact algorithm,
called StemFinder, for solving the MSS problem.
Compared with the previous MSS algorithms, StemFinder
runs much faster and reports fewer stems which
represent a smaller superset of all ( l, d ) motifs.
StemFinder is freely available at
http://sites.google.com/site/feqond/stemfinder.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2015:III,
author = "Xian Zhang and Ligang Wu and Shaochun Cui",
title = "An improved integral inequality to stability analysis
of genetic regulatory networks with interval
time-varying delays",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "398--409",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2351815",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper focuses on stability analysis for a class
of genetic regulatory networks with interval
time-varying delays. An improved integral inequality
concerning on double-integral items is first
established. Then, we use the improved integral
inequality to deal with the resultant double-integral
items in the derivative of the involved
Lyapunov-Krasovskii functional. As a result, a
delay-range-dependent and delay-rate-dependent
asymptotical stability criterion is established for
genetic regulatory networks with differential
time-varying delays. Furthermore, it is theoretically
proven that the stability criterion proposed here is
less conservative than the corresponding one in
[Neurocomputing, 2012, 93: 19-26]. Based on the
obtained result, another stability criterion is given
under the case that the information of the derivatives
of delays is unknown. Finally, the effectiveness of the
approach proposed in this paper is illustrated by a
pair of numerical examples which give the comparisons
of stability criteria proposed in this paper and some
literature.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2015:BLC,
author = "Zhenhua Li and Ying He and Limsoon Wong and Jinyan
Li",
title = "Burial level change defines a high energetic relevance
for protein binding interfaces",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "410--421",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2361355",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein--protein interfaces defined through atomic
contact or solvent accessibility change are widely
adopted in structural biology studies. But, these
definitions cannot precisely capture energetically
important regions at protein interfaces. The burial
depth of an atom in a protein is related to the atom's
energy. This work investigates how closely the change
in burial level of an atom/residue upon complexation is
related to the binding. Burial level change is
different from burial level itself. An atom deeply
buried in a monomer with a high burial level may not
change its burial level after an interaction and it may
have little burial level change. We hypothesize that an
interface is a region of residues all undergoing burial
level changes after interaction. By this definition, an
interface can be decomposed into an onion-like
structure according to the burial level change extent.
We found that our defined interfaces cover
energetically important residues more precisely, and
that the binding free energy of an interface is
distributed progressively from the outermost layer to
the core. These observations are used to predict
binding hot spots. Our approach's F-measure performance
on a benchmark dataset of alanine mutagenesis residues
is much superior or similar to those by complicated
energy modeling or machine learning approaches.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dasarathy:2015:DRP,
author = "Gautam Dasarathy and Robert Nowak and Sebastien Roch",
title = "Data requirement for phylogenetic inference from
multiple loci: a new distance method",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "422--432",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2361685",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We consider the problem of estimating the evolutionary
history of a set of species (phylogeny or species tree)
from several genes. It is known that the evolutionary
history of individual genes (gene trees) might be
topologically distinct from each other and from the
underlying species tree, possibly confounding
phylogenetic analysis. A further complication in
practice is that one has to estimate gene trees from
molecular sequences of finite length. We provide the
first full data-requirement analysis of a species tree
reconstruction method that takes into account
estimation errors at the gene level. Under that
criterion, we also devise a novel reconstruction
algorithm that provably improves over all previous
methods in a regime of interest.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Meng:2015:GSI,
author = "Jun Meng and Jing Zhang and Yushi Luan",
title = "Gene selection integrated with biological knowledge
for plant stress response using neighborhood system and
rough set theory",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "433--444",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2361329",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Mining knowledge from gene expression data is a hot
research topic and direction of bioinformatics. Gene
selection and sample classification are significant
research trends, due to the large amount of genes and
small size of samples in gene expression data. Rough
set theory has been successfully applied to gene
selection, as it can select attributes without
redundancy. To improve the interpretability of the
selected genes, some researchers introduced biological
knowledge. In this paper, we first employ neighborhood
system to deal directly with the new information table
formed by integrating gene expression data with
biological knowledge, which can simultaneously present
the information in multiple perspectives and do not
weaken the information of individual gene for selection
and classification. Then, we give a novel framework for
gene selection and propose a significant gene selection
method based on this framework by employing reduction
algorithm in rough set theory. The proposed method is
applied to the analysis of plant stress response.
Experimental results on three data sets show that the
proposed method is effective, as it can select
significant gene subsets without redundancy and achieve
high classification accuracy. Biological analysis for
the results shows that the interpretability is well.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cickovski:2015:GPI,
author = "Trevor Cickovski and Tiffany Flor and Galen
Irving-Sachs and Philip Novikov and James Parda and
Giri Narasimhan",
title = "{GPUDePiCt}: a parallel implementation of a clustering
algorithm for computing degenerate primers on graphics
processing units",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "445--454",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2355231",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In order to make multiple copies of a target sequence
in the laboratory, the technique of Polymerase Chain
Reaction (PCR) requires the design of ``primers'',
which are short fragments of nucleotides complementary
to the flanking regions of the target sequence. If the
same primer is to amplify multiple closely related
target sequences, then it is necessary to make the
primers ``degenerate'', which would allow it to
hybridize to target sequences with a limited amount of
variability that may have been caused by mutations.
However, the PCR technique can only allow a limited
amount of degeneracy, and therefore the design of
degenerate primers requires the identification of
reasonably well-conserved regions in the input
sequences. We take an existing algorithm for designing
degenerate primers that is based on clustering and
parallelize it in a web-accessible software package
GPUDePiCt, using a shared memory model and the
computing power of Graphics Processing Units (GPUs). We
test our implementation on large sets of aligned
sequences from the human genome and show a multi-fold
speedup for clustering using our hybrid GPU/CPU
implementation over a pure CPU approach for these
sequences, which consist of more than 7,500
nucleotides. We also demonstrate that this speedup is
consistent over larger numbers and longer lengths of
aligned sequences.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cai:2015:IPC,
author = "Bingjing Cai and Haiying Wang and Huiru Zheng and Hui
Wang",
title = "Identification of protein complexes from tandem
affinity purification\slash mass spectrometry data via
biased random walk",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "455--466",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2352616",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Systematic identification of protein complexes from
protein--protein interaction networks (PPIs) is an
important application of data mining in life science.
Over the past decades, various new clustering
techniques have been developed based on modelling PPIs
as binary relations. Non-binary information of
co-complex relations (prey/bait) in PPIs data derived
from tandem affinity purification/mass spectrometry
(TAP-MS) experiments has been unfairly disregarded. In
this paper, we propose a Biased Random Walk based
algorithm for detecting protein complexes from TAP-MS
data, resulting in the random walk with restarting
baits (RWRB). RWRB is developed based on Random walk
with restart. The main contribution of RWRB is the
incorporation of co-complex relations in TAP-MS PPI
networks into the clustering process, by implementing a
new restarting strategy during the process of random
walk. Through experimentation on un-weighted and
weighted TAP-MS data sets, we validated biological
significance of our results by mapping them to manually
curated complexes. Results showed that, by
incorporating non-binary, co-membership information,
significant improvement has been achieved in terms of
both statistical measurements and biological relevance.
Better accuracy demonstrates that the proposed method
outperformed several state-of-the-art clustering
algorithms for the detection of protein complexes in
TAP-MS data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2015:IDN,
author = "Xueming Liu and Linqiang Pan",
title = "Identifying driver nodes in the human signaling
network using structural controllability analysis",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "467--472",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2360396",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Cell signaling governs the basic cellular activities
and coordinates the actions in cell. Abnormal
regulations in cell signaling processing are
responsible for many human diseases, such as diabetes
and cancers. With the accumulation of massive data
related to human cell signaling, it is feasible to
obtain a human signaling network. Some studies have
shown that interesting biological phenomenon and
drug-targets could be discovered by applying structural
controllability analysis to biological networks. In
this work, we apply structural controllability to a
human signaling network and detect driver nodes,
providing a systematic analysis of the role of
different proteins in controlling the human signaling
network. We find that the proteins in the upstream of
the signaling information flow and the low in-degree
proteins play a crucial role in controlling the human
signaling network. Interestingly, inputting different
control signals on the regulators of the
cancer-associated genes could cost less than
controlling the cancer-associated genes directly in
order to control the whole human signaling network in
the sense that less drive nodes are needed. This
research provides a fresh perspective for controlling
the human cell signaling system.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jamil:2015:IIE,
author = "Hasan M. Jamil",
title = "Improving integration effectiveness of {ID} mapping
based biological record linkage",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "473--486",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2355213",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Traditionally, biological objects such as genes,
proteins, and pathways are represented by a convenient
identifier, or ID, which is then used to cross
reference, link and describe objects in biological
databases. Relationships among the objects are often
established using non-trivial and computationally
complex ID mapping systems or converters, and are
stored in authoritative databases such as UniGene,
GeneCards, PIR and BioMart. Despite best efforts, such
mappings are largely incomplete and riddled with false
negatives. Consequently, data integration using record
linkage that relies on these mappings produces poor
quality of data, inadvertently leading to erroneous
conclusions. In this paper, we discuss this largely
ignored dimension of data integration, examine how the
ubiquitous use of identifiers in biological databases
is a significant barrier to knowledge fusion using
distributed computational pipelines, and propose two
algorithms for ad hoc and restriction free ID mapping
of arbitrary types using online resources. We also
propose two declarative statements for ID conversion
and data integration based on ID mapping on-the-fly.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Priyadarshana:2015:MBP,
author = "W. J. R. M. Priyadarshana and Georgy Sofronov",
title = "Multiple break-points detection in array {CGH} data
via the cross-entropy method",
journal = j-TCBB,
volume = "12",
number = "2",
pages = "487--498",
month = mar,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2361639",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Array comparative genome hybridization (aCGH) is a
widely used methodology to detect copy number
variations of a genome in high resolution. Knowing the
number of break-points and their corresponding
locations in genomic sequences serves different
biological needs. Primarily, it helps to identify
disease-causing genes that have functional importance
in characterizing genome wide diseases. For human
autosomes the normal copy number is two, whereas at the
sites of oncogenes it increases (gain of DNA) and at
the tumour suppressor genes it decreases (loss of DNA).
The majority of the current detection methods are
deterministic in their set-up and use dynamic
programming or different smoothing techniques to obtain
the estimates of copy number variations. These
approaches limit the search space of the problem due to
different assumptions considered in the methods and do
not represent the true nature of the uncertainty
associated with the unknown break-points in genomic
sequences. We propose the Cross-Entropy method, which
is a model-based stochastic optimization technique as
an exact search method, to estimate both the number and
locations of the break-points in aCGH data. We model
the continuous scale log-ratio data obtained by the
aCGH technique as a multiple breakpoint problem. The
proposed methodology is compared with well established
publicly available methods using both artificially
generated data and real data. Results show that the
proposed procedure is an effective way of estimating
number and especially the locations of break-points
with high level of precision. Availability: The methods
described in this article are implemented in the new R
package breakpoint and it is available from the
Comprehensive R Archive Network at
http://CRAN.R-project.org/package=breakpoint.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Setubal:2015:TSS,
author = "Jo{\~a}o C. Setubal and Nalvo Almeida",
title = "{TCBB} special section on the {Brazilian Symposium on
Bioinformatics 2013}",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "499--499",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2410352",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Braga:2015:SLG,
author = "Mar{\'\i}lia D. V. Braga and Jens Stoye",
title = "Sorting linear genomes with rearrangements and
indels",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "500--506",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2329297",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Rearrangements are mutations that can change the
organization of a genome, but not its content. Examples
are inversions of DNA segments, translocations of
chromosome ends, fusions and fissions of chromosomes.
All mentioned rearrangements can be represented by the
generic Double Cut and Join (DCJ) operation. However,
the DCJ operation also allows circular chromosomes to
be created at intermediate steps, even if the compared
genomes are linear. In this case it is more plausible
to consider a restriction in which the reincorporation
of a circular chromosome has to be done immediately
after its creation. We call these two consecutive
operations an ER composition. It has been shown that an
ER composition mimics either an internal block
interchange (when two segments in the same chromosome
exchange their positions), or an internal transposition
(the special case of a block interchange when the two
segments are adjacent). The DCJ distance of two genomes
is the same, regardless of this restriction, and can be
computed in linear time. For comparing two genomes with
unequal contents, in addition to rearrangements we have
to allow insertions and deletions of DNA
segments--named indels. It is already known that the
distance in the model combining DCJ and indel
operations can be exactly computed. Again, for linear
genomes it would be more plausible to adopt a
restricted version with ER compositions. This model was
studied recently by da Silva et al. (BMC Bioinformatics
13, Suppl. 19, S14, 2012), but only an upper bound for
the restricted DCJ-indel distance was provided. Here we
first solve an open problem posed in that paper and
present a very simple proof showing that the distance,
which can be computed in linear time, is the same for
both the unrestricted and the restricted DCJ-indel
models. We then give a simpler algorithm for computing
an optimal restricted DCJ-indel sorting scenario in O(n
log n) time. We also relate the DCJ-indel distance to
the restricted DCJ-substitution distance, which instead
of indels considers a more powerful operation that
allows the substitution of a DNA segment by another DNA
segment. We show that the DCJ-indel distance is a
2-approximation for the restricted DCJ-substitution
distance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Siederdissen:2015:PGA,
author = "Christian H{\"o}ner Zu Siederdissen and Ivo L.
Hofacker and Peter F. Stadler",
title = "Product grammars for alignment and folding",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "507--519",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2326155",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We develop a theory of algebraic operations over
linear and context-free grammars that makes it possible
to combine simple ``atomic'' grammars operating on
single sequences into complex, multi-dimensional
grammars. We demonstrate the utility of this framework
by constructing the search spaces of complex alignment
problems on multiple input sequences explicitly as
algebraic expressions of very simple one-dimensional
grammars. In particular, we provide a fully worked
frameshift-aware, semiglobal DNA-protein alignment
algorithm whose grammar is composed of products of
small, atomic grammars. The compiler accompanying our
theory makes it easy to experiment with the combination
of multiple grammars and different operations.
Composite grammars can be written out in LATEX for
documentation and as a guide to implementation of
dynamic programming algorithms. An embedding in Haskell
as a domain-specific language makes the theory directly
accessible to writing and using grammar products
without the detour of an external compiler. Software
and supplemental files available here:
http://www.bioinf.uni-leipzig.de/Software/gramprod/",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hoksza:2015:MRS,
author = "David Hoksza and Daniel Svozil",
title = "Multiple {$3$D} {RNA} structure superposition using
neighbor joining",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "520--530",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2351810",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recent advances in RNA research and the steady growth
of available RNA structures call for bioinformatics
methods for handling and analyzing RNA structural data.
Recently, we introduced SETTER--a fast and accurate
method for RNA pairwise structure alignment. In this
paper, we describe MultiSETTER, SETTER extension for
multiple RNA structure alignment. MultiSETTER combines
SETTER's decomposition of RNA structures into
non-overlapping structural subunits with the multiple
sequence alignment algorithm ClustalW adapted for the
structure alignment. The accuracy of MultiSETTER was
assessed by the automatic classification of RNA
structures and its comparison to SCOR annotations. In
addition, MultiSETTER classification was also compared
to multiple sequence alignment-based and secondary
structure alignment-based classifications provided by
LocARNA and RNADistance tools, respectively.
MultiSETTER precompiled Windows libraries, as well as
the C++ source code, are freely available from
http://siret.cz/multisetter.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Carroll:2015:IRE,
author = "Hyrum D. Carroll and Alex C. Williams and Anthony G.
Davis and John L. Spouge",
title = "Improving retrieval efficacy of homology searches
using the false discovery rate",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "531--537",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2366112",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Over the past few decades, discovery based on sequence
homology has become a widely accepted practice.
Consequently, comparative accuracy of retrieval
algorithms (e.g., BLAST) has been rigorously studied
for improvement. Unlike most components of retrieval
algorithms, the E-value threshold criterion has yet to
be thoroughly investigated. An investigation of the
threshold is important as it exclusively dictates which
sequences are declared relevant and irrelevant. In this
paper, we introduce the false discovery rate (FDR)
statistic as a replacement for the uniform threshold
criterion in order to improve efficacy in retrieval
systems. Using NCBI's BLAST and PSI-BLAST software
packages, we demonstrate the applicability of such a
replacement in both non-iterative (BLAST FDR) and
iterative (PSI-BLASTFDR) homology searches. For each
application, we performed an evaluation of retrieval
efficacy with five different multiple testing methods
on a large training database. For each algorithm, we
choose the best performing method, Benjamini-Hochberg,
as the default statistic. As measured by the threshold
average precision, BLASTFDR yielded 14.1 percent better
retrieval performance than BLAST on a large (5,161
queries) test database and PSI-BLASTFDR attained 11.8
percent better retrieval performance than PSI-BLAST.
The C++ source code specific to BLASTFDR and
PSI-BLASTFDR and instructions are available at
http://www.cs.mtsu.edu/~hcarroll/blast_fdr/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Birlutiu:2015:BFC,
author = "Adriana Birlutiu and Florence D'Alch{\'e}-Buc and Tom
Heskes",
title = "A {Bayesian} framework for combining protein and
network topology information for predicting
protein--protein interactions",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "538--550",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2359441",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Computational methods for predicting protein--protein
interactions are important tools that can complement
high-throughput technologies and guide biologists in
designing new laboratory experiments. The proteins and
the interactions between them can be described by a
network which is characterized by several topological
properties. Information about proteins and interactions
between them, in combination with knowledge about
topological properties of the network, can be used for
developing computational methods that can accurately
predict unknown protein--protein interactions. This
paper presents a supervised learning framework based on
Bayesian inference for combining two types of
information: (i) network topology information, and (ii)
information related to proteins and the interactions
between them. The motivation of our model is that by
combining these two types of information one can
achieve a better accuracy in predicting
protein--protein interactions, than by using models
constructed from these two types of information
independently.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Song:2015:MLA,
author = "Yinglei Song and Chunmei Liu and Zhi Wang",
title = "A machine learning approach for accurate annotation of
noncoding {RNAs}",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "551--559",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2366758",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Searching genomes to locate noncoding RNA genes with
known secondary structure is an important problem in
bioinformatics. In general, the secondary structure of
a searched noncoding RNA is defined with a structure
model constructed from the structural alignment of a
set of sequences from its family. Computing the optimal
alignment between a sequence and a structure model is
the core part of an algorithm that can search genomes
for noncoding RNAs. In practice, a single structure
model may not be sufficient to capture all crucial
features important for a noncoding RNA family. In this
paper, we develop a novel machine learning approach
that can efficiently search genomes for noncoding RNAs
with high accuracy. During the search procedure, a
sequence segment in the searched genome sequence is
processed and a feature vector is extracted to
represent it. Based on the feature vector, a classifier
is used to determine whether the sequence segment is
the searched ncRNA or not. Our testing results show
that this approach is able to efficiently capture
crucial features of a noncoding RNA family. Compared
with existing search tools, it significantly improves
the accuracy of genome annotation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mehmood:2015:PLS,
author = "Tahir Mehmood and Jon Bohlin and Lars Snipen",
title = "A partial least squares based procedure for upstream
sequence classification in prokaryotes",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "560--567",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2366146",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The upstream region of coding genes is important for
several reasons, for instance locating transcription
factor, binding sites, and start site initiation in
genomic DNA. Motivated by a recently conducted study,
where multivariate approach was successfully applied to
coding sequence modeling, we have introduced a partial
least squares (PLS) based procedure for the
classification of true upstream prokaryotic sequence
from background upstream sequence. The upstream
sequences of conserved coding genes over genomes were
considered in analysis, where conserved coding genes
were found by using pan-genomics concept for each
considered prokaryotic species. PLS uses position
specific scoring matrix (PSSM) to study the
characteristics of upstream region. Results obtained by
PLS based method were compared with Gini importance of
random forest (RF) and support vector machine (SVM),
which is much used method for sequence classification.
The upstream sequence classification performance was
evaluated by using cross validation, and suggested
approach identifies prokaryotic upstream region
significantly better to RF (p-value {$<$} 0:01) and SVM
(p-value {$<$} 0:01). Further, the proposed method also
produced results that concurred with known biological
characteristics of the upstream region.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dong:2015:ANA,
author = "Liang Dong and Bing Shi and Guangdong Tian and YanBo
Li and Bing Wang and MengChu Zhou",
title = "An accurate de novo algorithm for glycan topology
determination from mass spectra",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "568--578",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2368981",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Determining the glycan topology automatically from
mass spectra represents a great challenge. Existing
methods fall into approximate and exact ones. The
former including greedy and heuristic ones can reduce
the computational complexity, but suffer from
information lost in the procedure of glycan
interpretation. The latter including dynamic
programming and exhaustive enumeration are much slower
than the former. In the past years, nearly all emerging
methods adopted a tree structure to represent a glycan.
They share such problems as repetitive peak counting in
reconstructing a candidate structure. Besides,
tree-based glycan representation methods often have to
give different computational formulas for binary and
ternary glycans. We propose a new directed acyclic
graph structure for glycan representation. Based on it,
this work develops a de novo algorithm to accurately
reconstruct the tree structure iteratively from mass
spectra with logical constraints and some known
biosynthesis rules, by a single computational formula.
The experiments on multiple complex glycans extracted
from human serum show that the proposed algorithm can
achieve higher accuracy to determine a glycan topology
than prior methods without increasing computational
burden.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2015:CNI,
author = "Pei Wang and Jinhu L{\"u} and Xinghuo Yu",
title = "Colored noise induced bistable switch in the genetic
toggle switch systems",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "579--589",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2368982",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Noise can induce various dynamical behaviors in
nonlinear systems. White noise perturbed systems have
been extensively investigated during the last decades.
In gene networks, experimentally observed extrinsic
noise is colored. As an attempt, we investigate the
genetic toggle switch systems perturbed by colored
extrinsic noise and with kinetic parameters. Compared
with white noise perturbed systems, we show there also
exists optimal colored noise strength to induce the
best stochastic switch behaviors in the single toggle
switch, and the best synchronized switching in the
networked systems, which demonstrate that noise-induced
optimal switch behaviors are widely in existence.
Moreover, under a wide range of system parameter
regions, we find there exist wider ranges of white and
colored noises strengths to induce good switch and
synchronization behaviors, respectively; therefore,
white noise is beneficial for switch and colored noise
is beneficial for population synchronization. Our
observations are very robust to extrinsic stimulus
strength, cell density, and diffusion rate. Finally,
based on the Waddington's epigenetic landscape and the
Wiener-Khintchine theorem, physical mechanisms
underlying the observations are interpreted. Our
investigations can provide guidelines for experimental
design, and have potential clinical implications in
gene therapy and synthetic biology.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bhattacharyya:2015:CCS,
author = "Sourya Bhattacharyya and Jayanta Mukherjee",
title = "{COSPEDTree}: couplet supertree by equivalence
partitioning of taxa set and {DAG} formation",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "590--603",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2366778",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "From a set of phylogenetic trees with overlapping taxa
set, a supertree exhibits evolutionary relationships
among all input taxa. The key is to resolve the
contradictory relationships with respect to input
trees, between individual taxa subsets. Formulation of
this NP hard problem employs either local search
heuristics to reduce tree search space, or resolves the
conflicts with respect to fixed or varying size subtree
level decompositions. Different approximation
techniques produce supertrees with considerable
performance variations. Moreover, the majority of the
algorithms involve high computational complexity, thus
not suitable for use on large biological data sets.
Current study presents COSPEDTree, a novel method for
supertree construction. The technique resolves source
tree conflicts by analyzing couplet (taxa pair)
relationships for each source trees. Subsequently,
individual taxa pairs are resolved with a single
relation. To prioritize the consensus relations among
individual taxa pairs for resolving them, greedy
scoring is employed to assign higher score values for
the consensus relations among a taxa pair. Selected set
of relations resolving individual taxa pairs is
subsequently used to construct a directed acyclic graph
(DAG). Vertices of DAG represents a taxa subset
inferred from the same speciation event. Thus,
COSPEDTree can generate non-binary supertrees as well.
Depth first traversal on this DAG yields final
supertree. According to the performance metrics on
branch dissimilarities (such as FP, FN and RF),
COSPEDTree produces mostly conservative, well resolved
supertrees. Specifically, RF metrics are mostly lower
compared to the reference approaches, and FP values are
lower apart from only strictly conservative (or veto)
approaches. COSPEDTree has worst case time and space
complexities of cubic and quadratic order,
respectively, better or comparable to the reference
approaches. Such high performance and low computational
costs enable COSPEDTree to be applied on large scale
biological data sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gopinath:2015:DKD,
author = "Krishnasamy Gopinath and Ramaraj Jayakumararaj and
Muthusamy Karthikeyan",
title = "{DAPD}: a knowledgebase for diabetes associated
proteins",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "604--610",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2359442",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recent advancements in genomics and proteomics provide
a solid foundation for understanding the pathogenesis
of diabetes. Proteomics of diabetes associated pathways
help to identify the most potent target for the
management of diabetes. The relevant datasets are
scattered in various prominent sources which takes much
time to select the therapeutic target for the clinical
management of diabetes. However, additional information
about target proteins is needed for validation. This
lacuna may be resolved by linking diabetes associated
genes, pathways and proteins and it will provide a
strong base for the treatment and planning management
strategies of diabetes. Thus, a web source ``Diabetes
Associated Proteins Database (DAPD)'' has been
developed to link the diabetes associated genes,
pathways and proteins using PHP, MySQL. The current
version of DAPD has been built with proteins associated
with different types of diabetes. In addition, DAPD has
been linked to external sources to gain the access to
more participatory proteins and their pathway network.
DAPD will reduce the time and it is expected to pave
the way for the discovery of novel anti-diabetic leads
using computational drug designing for diabetes
management. DAPD is open accessed via following url
www.mkarthikeyan.bioinfoau.org/dapd.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2015:DCP,
author = "Dong-Jun Yu and Yang Li and Jun Hu and Xibei Yang and
Jing-Yu Yang and Hong-Bin Shen",
title = "Disulfide connectivity prediction based on modelled
protein {$3$D} structural information and random forest
regression",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "611--621",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2359451",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Disulfide connectivity is an important protein
structural characteristic. Accurately predicting
disulfide connectivity solely from protein sequence
helps to improve the intrinsic understanding of protein
structure and function, especially in the post-genome
era where large volume of sequenced proteins without
being functional annotated is quickly accumulated. In
this study, a new feature extracted from the predicted
protein 3D structural information is proposed and
integrated with traditional features to form
discriminative features. Based on the extracted
features, a random forest regression model is performed
to predict protein disulfide connectivity. We compare
the proposed method with popular existing predictors by
performing both cross-validation and independent
validation tests on benchmark datasets. The
experimental results demonstrate the superiority of the
proposed method over existing predictors. We believe
the superiority of the proposed method benefits from
both the good discriminative capability of the newly
developed features and the powerful modelling
capability of the random forest. The web server
implementation, called Target Disulfide, and the
benchmark datasets are freely available at:
http://csbio.njust.edu.cn/bioinf/TargetDisulfide for
academic use.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huang:2015:EMS,
author = "Lei Huang and Li Liao and Cathy H. Wu",
title = "Evolutionary model selection and parameter estimation
for protein--protein interaction network based on
differential evolution algorithm",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "622--631",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2366748",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Revealing the underlying evolutionary mechanism plays
an important role in understanding protein interaction
networks in the cell. While many evolutionary models
have been proposed, the problem about applying these
models to real network data, especially for
differentiating which model can better describe
evolutionary process for the observed network remains a
challenge. The traditional way is to use a model with
presumed parameters to generate a network, and then
evaluate the fitness by summary statistics, which
however cannot capture the complete network structures
information and estimate parameter distribution. In
this work, we developed a novel method based on
Approximate Bayesian Computation and modified
Differential Evolution algorithm (ABC-DEP) that is
capable of conducting model selection and parameter
estimation simultaneously and detecting the underlying
evolutionary mechanisms for PPI networks more
accurately. We tested our method for its power in
differentiating models and estimating parameters on
simulated data and found significant improvement in
performance benchmark, as compared with a previous
method. We further applied our method to real data of
protein interaction networks in human and yeast. Our
results show duplication attachment model as the
predominant evolutionary mechanism for human PPI
networks and Scale-Free model as the predominant
mechanism for yeast PPI networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Basu:2015:EIF,
author = "Saurav Basu and Chi Liu and Gustavo Kunde Rohde",
title = "Extraction of individual filaments from {$2$D}
confocal microscopy images of flat cells",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "632--643",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2372783",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A crucial step in understanding the architecture of
cells and tissues from microscopy images, and
consequently explain important biological events such
as wound healing and cancer metastases, is the complete
extraction and enumeration of individual filaments from
the cellular cytoskeletal network. Current efforts at
quantitative estimation of filament length
distribution, architecture and orientation from
microscopy images are predominantly limited to visual
estimation and indirect experimental inference. Here we
demonstrate the application of a new algorithm to
reliably estimate centerlines of biological filament
bundles and extract individual filaments from the
centerlines by systematically disambiguating filament
intersections. We utilize a filament enhancement step
followed by reverse diffusion based filament
localization and an integer programming based set
combination to systematically extract accurate
filaments automatically from microscopy images.
Experiments on simulated and real confocal microscope
images of flat cells (2D images) show efficacy of the
new method.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ng:2015:FAL,
author = "Yen Kaow Ng and Linzhi Yin and Hirotaka Ono and Shuai
Cheng Li",
title = "Finding all longest common segments in protein
structures efficiently",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "644--655",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2372782",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The Local/Global Alignment (Zemla, 2003), or LGA, is a
popular method for the comparison of protein
structures. One of the two components of LGA requires
us to compute the longest common contiguous segments
between two protein structures. That is, given two
structures $ A = (a_1; \ldots {}; a_n) $ and $ B =
(b_1; \ldots {}; b_n) $ where $ a_k, b_k \in R^3 $, we
are to find, among all the segments $ f = (a_i; \ldots
{}; a_j) $ and $ g = (b_i; \ldots {}; b_j) $ that
fulfill a certain criterion regarding their similarity,
those of the maximum length. We consider the following
criteria: (1) the root mean squared deviation (RMSD)
between $f$ and $g$ is to be within a given $ t \in R$;
(2) $f$ and $g$ can be superposed such that for each $
k, i \leq k \leq j$, $ ||a k - b k|| \leq t$ for a
given $ t \in R$. We give an algorithm of $ O(n \log n
+ n l)$ time complexity when the first requirement
applies, where $l$ is the maximum length of the
segments fulfilling the criterion. We show an FPTAS
which, for any $ \epsilon \in R$, finds a segment of
length at least $l$, but of RMSD up to $ (1 + \epsilon)
t$, in $ O(n \log n + n / \epsilon)$ time. We propose
an FPTAS which for any given $ \epsilon \in R$, finds
all the segments $f$ and $g$ of the maximum length
which can be superposed such that for each $ k, i \leq
k \leq j$, $ ||a k - b k|| \leq (1 + \epsilon) t$, thus
fulfilling the second requirement approximately. The
algorithm has a time complexity of $ O(n \log^2 n /
\epsilon^5)$ when consecutive points in $A$ are
separated by the same distance (which is the case with
protein structures). These worst-case runtime
complexities are verified using C++ implementations of
the algorithms, which we have made available at
http://alcs.sourceforge.net/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gonzalez-Alvarez:2015:FPP,
author = "David L. Gonz{\'a}lez-{\'A}lvarez and Miguel A.
Vega-Rodr{\'\i}guez and {\'A}lvaro Rubio-Largo",
title = "Finding patterns in protein sequences by using a
hybrid multiobjective teaching learning based
optimization algorithm",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "656--666",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2369043",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Proteins are molecules that form the mass of living
beings. These proteins exist in dissociated forms like
amino-acids and carry out various biological functions,
in fact, almost all body reactions occur with the
participation of proteins. This is one of the reasons
why the analysis of proteins has become a major issue
in biology. In a more concrete way, the identification
of conserved patterns in a set of related protein
sequences can provide relevant biological information
about these protein functions. In this paper, we
present a novel algorithm based on teaching learning
based optimization (TLBO) combined with a local search
function specialized to predict common patterns in sets
of protein sequences. This population-based
evolutionary algorithm defines a group of individuals
(solutions) that enhance their knowledge (quality) by
means of different learning stages. Thus, if we
correctly adapt it to the biological context of the
mentioned problem, we can get an acceptable set of
quality solutions. To evaluate the performance of the
proposed technique, we have used six instances composed
of different related protein sequences obtained from
the PROSITE database. As we will see, the designed
approach makes good predictions and improves the
quality of the solutions found by other well-known
biological tools.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mazza:2015:FIA,
author = "Tommaso Mazza and Caterina Fusilli and Chiara Saracino
and Gianluigi Mazzoccoli and Francesca Tavano and
Manlio Vinciguerra and Valerio Pazienza",
title = "Functional impact of autophagy-related genes on the
homeostasis and dynamics of pancreatic cancer cell
lines",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "667--678",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2371824",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Pancreatic cancer is a highly aggressive and
chemotherapy-resistant malignant neoplasm. In basal
condition, it is characterized by elevated autophagy
activity, which is required for tumor growth and that
correlates with treatment failure. We analyzed the
expression of autophagy related genes in different
pancreatic cancer cell lines. A correlation-based
network analysis evidenced the sociality and
topological roles of the autophagy-related genes after
serum starvation. Structural and functional tests
identified a core set of autophagy related genes,
suggesting different scenarios of autophagic responses
to starvation, which may be responsible for the
clinical variations associated with pancreatic cancer
pathogenesis.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xia:2015:IGA,
author = "Hong Xia and Yuanning Liu and Minghui Wang and Ao Li",
title = "Identification of genomic aberrations in cancer
subclones from heterogeneous tumor samples",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "679--685",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2366114",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Tumor samples are usually heterogeneous, containing
admixture of more than one kind of tumor subclones.
Studies of genomic aberrations from heterogeneous tumor
data are hindered by the mixed signal of tumor subclone
cells. Most of the existing algorithms cannot
distinguish contributions of different subclones from
the measured single nucleotide polymorphism (SNP) array
signals, which may cause erroneous estimation of
genomic aberrations. Here, we have introduced a
computational method, Cancer Heterogeneity Analysis
from SNP-array Experiments (CHASE), to automatically
detect subclone proportions and genomic aberrations
from heterogeneous tumor samples. Our method is based
on HMM, and incorporates EM algorithm to build a
statistical model for modeling mixed signal of multiple
tumor subclones. We tested the proposed approach on
simulated datasets and two real datasets, and the
results show that the proposed method can efficiently
estimate tumor subclone proportions and recovery the
genomic aberrations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Song:2015:PBM,
author = "Dandan Song and Jiaxing Chen and Guang Chen and Ning
Li and Jin Li and Jun Fan and Dongbo Bu and Shuai Cheng
Li",
title = "Parameterized {BLOSUM} matrices for protein
alignment",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "686--694",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2366126",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein alignment is a basic step for many molecular
biology researches. The BLOSUM matrices, especially
BLOSUM62, are the de facto standard matrices for
protein alignments. However, after widely utilization
of the matrices for 15 years, programming errors were
surprisingly found in the initial version of source
codes for their generation. And amazingly, after bug
correction, the ``intended'' BLOSUM62 matrix performs
consistently worse than the ``miscalculated'' one. In
this paper, we find linear relationships among the
eigenvalues of the matrices and propose an algorithm to
find optimal unified eigenvectors. With them, we can
parameterize matrix BLOSUMx for any given variable x
that could change continuously. We compare the
effectiveness of our parameterized isentropic matrix
with BLOSUM62. Furthermore, an iterative alignment and
matrix selection process is proposed to adaptively find
the best parameter and globally align two sequences.
Experiments are conducted on aligning 13,667 families
of Pfam database and on clustering MHC II protein
sequences, whose improved accuracy demonstrates the
effectiveness of our proposed method.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ding:2015:SHO,
author = "Xiaojun Ding and Jianxin Wang and Alex Zelikovsky and
Xuan Guo and Minzhu Xie and Yi Pan",
title = "Searching high-order {SNP} combinations for complex
diseases based on energy distribution difference",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "695--704",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2363459",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Single nucleotide polymorphisms, a dominant type of
genetic variants, have been used successfully to
identify defective genes causing human single gene
diseases. However, most common human diseases are
complex diseases and caused by gene-gene and
gene-environment interactions. Many SNP-SNP interaction
analysis methods have been introduced but they are not
powerful enough to discover interactions more than
three SNPs. The paper proposes a novel method that
analyzes all SNPs simultaneously. Different from
existing methods, the method regards an individual's
genotype data on a list of SNPs as a point with a unit
of energy in a multi-dimensional space, and tries to
find a new coordinate system where the energy
distribution difference between cases and controls
reaches the maximum. The method will find different
multiple SNPs combinatorial patterns between cases and
controls based on the new coordinate system. The
experiment on simulated data shows that the method is
efficient. The tests on the real data of age-related
macular degeneration (AMD) disease show that it can
find out more significant multi-SNP combinatorial
patterns than existing methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Boareto:2015:SVR,
author = "Marcelo Boareto and Jonatas Cesar and Vitor B. P.
Leite and Nestor Caticha",
title = "Supervised variational relevance learning, an analytic
geometric feature selection with applications to omic
datasets",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "705--711",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2377750",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We introduce Supervised Variational Relevance Learning
(Suvrel), a variational method to determine metric
tensors to define distance based similarity in pattern
classification, inspired in relevance learning. The
variational method is applied to a cost function that
penalizes large intraclass distances and favors small
interclass distances. We find analytically the metric
tensor that minimizes the cost function. Preprocessing
the patterns by doing linear transformations using the
metric tensor yields a dataset which can be more
efficiently classified. We test our methods using
publicly available datasets, for some standard
classifiers. Among these datasets, two were tested by
the MAQC-II project and, even without the use of
further preprocessing, our results improve on their
performance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hsu:2015:SBF,
author = "Chih-Yuan Hsu and Zhen-Ming Pan and Rei-Hsing Hu and
Chih-Chun Chang and Hsiao-Chun Cheng and Che Lin and
Bor-Sen Chen",
title = "Systematic biological filter design with a desired
{I/O} filtering response based on promoter-{RBS}
libraries",
journal = j-TCBB,
volume = "12",
number = "3",
pages = "712--725",
month = may,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2372790",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 28 05:40:09 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this study, robust biological filters with an
external control to match a desired input/output (I/O)
filtering response are engineered based on the
well-characterized promoter-RBS libraries and a cascade
gene circuit topology. In the field of synthetic
biology, the biological filter system serves as a
powerful detector or sensor to sense different
molecular signals and produces a specific output
response only if the concentration of the input
molecular signal is higher or lower than a specified
threshold. The proposed systematic design method of
robust biological filters is summarized into three
steps. Firstly, several well-characterized promoter-RBS
libraries are established for biological filter design
by identifying and collecting the quantitative and
qualitative characteristics of their promoter-RBS
components via nonlinear parameter estimation method.
Then, the topology of synthetic biological filter is
decomposed into three cascade gene regulatory modules,
and an appropriate promoter-RBS library is selected for
each module to achieve the desired I/O specification of
a biological filter. Finally, based on the proposed
systematic method, a robust externally tunable
biological filter is engineered by searching the
promoter-RBS component libraries and a control inducer
concentration library to achieve the optimal reference
match for the specified I/O filtering response.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Janga:2015:KDU,
author = "Sarath Chandra Janga and Dongxiao Zhu and Jake Y. Chen
and Mohammed J. Zaki",
title = "Knowledge discovery using big data in biomedical
systems",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "726--728",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2454551",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The 13th International Workshop on Data Mining in
Bioinformatics (BIOKDD'14) was organized in conjunction
with the ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining on August 24, 2014
in New York, USA. It brought together international
researchers in the interacting disciplines of data
mining, systems biology, and bioinformatics at the
Bloomberg Headquarters venue. The goal of this workshop
is to encourage Knowledge Discovery and Data mining
(KDD) researchers to take on the numerous challenges
that Bioinformatics offers. This year, the workshop
featured the theme of ``Knowledge discovery using big
data in biological/biomedical systems''.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Koneru:2015:DCA,
author = "Suvarna Vani Koneru and Bhavani S. Durga",
title = "Divide and conquer approach to contact map overlap
problem using {$2$D}-pattern mining of protein contact
networks",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "729--737",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2394402",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A novel approach to Contact Map Overlap (CMO) problem
is proposed using the two dimensional clusters present
in the contact maps. Each protein is represented as a
set of the non-trivial clusters of contacts extracted
from its contact map. The approach involves finding
matching regions between the two contact maps using
approximate 2D-pattern matching algorithm and dynamic
programming technique. These matched pairs of small
contact maps are submitted in parallel to a fast
heuristic CMO algorithm. The approach facilitates
parallelization at this level since all the pairs of
contact maps can be submitted to the algorithm in
parallel. Then, a merge algorithm is used in order to
obtain the overall alignment. As a proof of concept,
MSVNS, a heuristic CMO algorithm is used for global as
well as local alignment. The divide and conquer
approach is evaluated for two benchmark data sets that
of Skolnick and Ding et al. It is interesting to note
that along with achieving saving of time, better
overlap is also obtained for certain protein folds.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Henriques:2015:BFP,
author = "Rui Henriques and Sara C. Madeira",
title = "Biclustering with flexible plaid models to unravel
interactions between biological processes",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "738--752",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2388206",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genes can participate in multiple biological processes
at a time and thus their expression can be seen as a
composition of the contributions from the active
processes. Biclustering under a plaid assumption allows
the modeling of interactions between transcriptional
modules or biclusters (subsets of genes with coherence
across subsets of conditions) by assuming an additive
composition of contributions in their overlapping
areas. Despite the biological interest of plaid models,
few biclustering algorithms consider plaid effects and,
when they do, they place restrictions on the allowed
types and structures of biclusters, and suffer from
robustness problems by seizing exact additive
matchings. We propose BiP (Biclustering using Plaid
models), a biclustering algorithm with relaxations to
allow expression levels to change in overlapping areas
according to biologically meaningful assumptions
(weighted and noise-tolerant composition of
contributions). BiP can be used over existing
biclustering solutions (seizing their benefits) as it
is able to recover excluded areas due to unaccounted
plaid effects and detect noisy areas non-explained by a
plaid assumption, thus producing an explanatory model
of overlapping transcriptional activity. Experiments on
synthetic data support BiP's efficiency and
effectiveness. The learned models from expression data
unravel meaningful and non-trivial functional
interactions between biological processes associated
with putative regulatory modules.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Vogt:2015:USD,
author = "Julia E. Vogt",
title = "Unsupervised structure detection in biomedical data",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "753--760",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2394408",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A major challenge in computational biology is to find
simple representations of high-dimensional data that
best reveal the underlying structure. In this work, we
present an intuitive and easy-to-implement method based
on ranked neighborhood comparisons that detects
structure in unsupervised data. The method is based on
ordering objects in terms of similarity and on the
mutual overlap of nearest neighbors. This basic
framework was originally introduced in the field of
social network analysis to detect actor communities. We
demonstrate that the same ideas can successfully be
applied to biomedical data sets in order to reveal
complex underlying structure. The algorithm is very
efficient and works on distance data directly without
requiring a vectorial embedding of data. Comprehensive
experiments demonstrate the validity of this approach.
Comparisons with state-of-the-art clustering methods
show that the presented method outperforms hierarchical
methods as well as density based clustering methods and
model-based clustering. A further advantage of the
method is that it simultaneously provides a
visualization of the data. Especially in biomedical
applications, the visualization of data can be used as
a first pre-processing step when analyzing real world
data sets to get an intuition of the underlying data
structure. We apply this model to synthetic data as
well as to various biomedical data sets which
demonstrate the high quality and usefulness of the
inferred structure.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Shibuya:2015:GEI,
author = "Tetsuo Shibuya and Chuan Yi Tang and Paul Horton and
Kiyoshi Asai",
title = "Guest editorial for the {25th International Conference
on Genome Informatics (GIW\slash ISCB-Asia 2014)}",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "761--762",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2439791",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The International Conference on Genome Informatics,
known as ``GIW'', is one of the longest running annual
conferences in bioinformatics or computational biology.
GIW has played an important role in the development of
the bioinformatics community in the Asia-Pacific region
since its establishment in 1990. The first GIW was held
as a Japanese workshop (called the Genome Informatics
Workshop) in Tokyo, Japan, in 1990. It has been held
annually since then, switching from a domestic
conference to an international one in 1993 and updating
its official name to the current ``International
Conference on Genome Informatics'' in 2001. GIW was
held in Japan (Tokyo or Yokohama) until 2006, but since
then GIW has been held in various locations in the
Asia-Pacific region. The 18th GIW was held in Singapore
in 2007. In the following years, GIW was held in Gold
Coast, Australia (2008); Yokohama, Japan (2009);
Hangzhou, China (2010); Busan, Korea (2011); Tainan,
Taiwan (2012); and again in Singapore in 2013. With its
long history and track record in attracting
state-of-the art bioinformatics research in general,
and especially algorithmic work, GIW is arguably not
only the top bioinformatics conference in the
Asia-Pacific region, but also one of the most important
worldwide. Now solidly established as an international
conference, GIW will be revisiting its birthplace,
Tokyo, for 2014 and 2015.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Al-Jarrah:2015:RSL,
author = "Omar Y. Al-Jarrah and Paul D. Yoo and Kamal Taha and
Sami Muhaidat and Abdallah Shami and Nazar Zaki",
title = "Randomized subspace learning for proline cis--trans
isomerization prediction",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "763--769",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2369040",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Proline residues are common source of kinetic
complications during folding. The X-Pro peptide bond is
the only peptide bond for which the stability of the
cis and trans conformations is comparable. The
cis--trans isomerization (CTI) of X-Pro peptide bonds
is a widely recognized rate-limiting factor, which can
not only induces additional slow phases in protein
folding but also modifies the millisecond and
sub-millisecond dynamics of the protein. An accurate
computational prediction of proline CTI is of great
importance for the understanding of protein folding,
splicing, cell signaling, and transmembrane active
transport in both the human body and animals. In our
earlier work, we successfully developed a biophysically
motivated proline CTI predictor utilizing a novel
tree-based consensus model with a powerful metalearning
technique and achieved 86.58 percent Q2 accuracy and
0.74 Mcc, which is a better result than the results
(70-73 percent Q2 accuracies) reported in the
literature on the well-referenced benchmark dataset. In
this paper, we describe experiments with novel
randomized subspace learning and bootstrap seeding
techniques as an extension to our earlier work, the
consensus models as well as entropy-based learning
methods, to obtain better accuracy through a precise
and robust learning scheme for proline CTI
prediction.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sung:2015:MLM,
author = "Wing-Kin Sung and Kunihiko Sadakane and Tetsuo Shibuya
and Abha Belorkar and Iana Pyrogova",
title = "An {$ O(m \log m) $}-time algorithm for detecting
superbubbles",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "770--777",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2385696",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In genome assembly graphs, motifs such as tips,
bubbles, and cross links are studied in order to find
sequencing errors and to understand the nature of the
genome. Superbubble, a complex generalization of
bubbles, was recently proposed as an important subgraph
class for analyzing assembly graphs. At present, a
quadratic time algorithm is known. This paper gives an
O ( m log m )-time algorithm to solve this problem for
a graph with m edges.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Seok:2015:ESA,
author = "Ho-Sik Seok and Taemin Song and Sek Won Kong and
Kyu-Baek Hwang",
title = "An efficient search algorithm for finding
genomic-range overlaps based on the maximum range
length",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "778--784",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2369042",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Efficient search algorithms for finding genomic-range
overlaps are essential for various bioinformatics
applications. A majority of fast algorithms for
searching the overlaps between a query range (e.g., a
genomic variant) and a set of N reference ranges (e.g.,
exons) has time complexity of O ( k + log N ), where k
denotes a term related to the length and location of
the reference ranges. Here, we present a simple but
efficient algorithm that reduces k, based on the
maximum reference range length. Specifically, for a
given query range and the maximum reference range
length, the proposed method divides the reference range
set into three subsets: always, potentially, and never
overlapping. Therefore, search effort can be reduced by
excluding never overlapping subset. We demonstrate that
the running time of the proposed algorithm is
proportional to potentially overlapping subset size,
that is proportional to the maximum reference range
length if all the other conditions are the same.
Moreover, an implementation of our algorithm was 13.8
to 30.0 percent faster than one of the fastest range
search methods available when tested on various
genomic-range data sets. The proposed algorithm has
been incorporated into a disease-linked variant
prioritization pipeline for WGS
(http://gnome.tchlab.org) and its implementation is
available at http://ml.ssu.ac.kr/gSearch.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hsu:2015:CNE,
author = "Yi-Yu Hsu and Hung-Yu Kao",
title = "Curatable named-entity recognition using semantic
relations",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "785--792",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2366770",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Named-entity recognition (NER) plays an important role
in the development of biomedical databases. However,
the existing NER tools produce multifarious
named-entities which may result in both curatable and
non-curatable markers. To facilitate biocuration with a
straightforward approach, classifying curatable
named-entities is helpful with regard to accelerating
the biocuration workflow. Co-occurrence Interaction
Nexus with Named-entity Recognition (CoINNER) is a
web-based tool that allows users to identify genes,
chemicals, diseases, and action term mentions in the
Comparative Toxicogenomic Database (CTD). To further
discover interactions, CoINNER uses multiple advanced
algorithms to recognize the mentions in the BioCreative
IV CTD Track. CoINNER is developed based on a prototype
system that annotated gene, chemical, and disease
mentions in PubMed abstracts at BioCreative 2012 Track
I (literature triage). We extended our previous system
in developing CoINNER. The pre-tagging results of
CoINNER were developed based on the state-of-the-art
named entity recognition tools in BioCreative III.
Next, a method based on conditional random fields
(CRFs) is proposed to predict chemical and disease
mentions in the articles. Finally, action term mentions
were collected by latent Dirichlet allocation (LDA). At
the BioCreative IV CTD Track, the best F-measures
reached for gene/protein, chemical/drug and disease NER
were 54 percent while CoINNER achieved a 61.5 percent
F-measure. System URL:
http://ikmbio.csie.ncku.edu.tw/coinner/introduction.htm.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2015:GEI,
author = "Dong Xu and Kun Huang and Jeanette Schmidt",
title = "Guest editors introduction to the special section on
software and databases",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "793--794",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2454931",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Software tools and information systems in
bioinformatics and computational biology are playing
more and more important roles in biology and medical
research. This special section consists of a selection
of papers focusing on software and databases that are
central in bioinformatics and computational biology.
Following a rigorous review process, 11 papers were
selected for publication. These papers cover a broad
range of topics, including computational genomics and
transcriptomics, analysis of biological networks and
interactions, drug design, biomedical signal/image
analysis, biomedical text mining and ontologies,
biological data mining, visualization and integration,
and high performance computing application in
bioinformatics.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Feng:2015:RES,
author = "Weixing Feng and Peichao Sang and Deyuan Lian and
Yansheng Dong and Fengfei Song and Meng Li and Bo He
and Fenglin Cao and Yunlong Liu",
title = "{ResSeq}: enhancing short-read sequencing alignment by
rescuing error-containing reads",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "795--798",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2366103",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Next-generation short-read sequencing is widely
utilized in genomic studies. Biological applications
require an alignment step to map sequencing reads to
the reference genome, before acquiring expected genomic
information. This requirement makes alignment accuracy
a key factor for effective biological interpretation.
Normally, when accounting for measurement errors and
single nucleotide polymorphisms, short read mappings
with a few mismatches are generally considered
acceptable. However, to further improve the efficiency
of short-read sequencing alignment, we propose a method
to retrieve additional reliably aligned reads (reads
with more than a pre-defined number of mismatches),
using a Bayesian-based approach. In this method, we
first retrieve the sequence context around the
mismatched nucleotides within the already aligned
reads; these loci contain the genomic features where
sequencing errors occur. Then, using the derived
pattern, we evaluate the remaining (typically
discarded) reads with more than the allowed number of
mismatches, and calculate a score that represents the
probability that a specific alignment is correct. This
strategy allows the extraction of more reliably aligned
reads, therefore improving alignment sensitivity.
Implementation: The source code of our tool, ResSeq,
can be downloaded from:
https://github.com/hrbeubiocenter/Resseq.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Segar:2015:MTD,
author = "Matthew W. Segar and Cynthia J. Sakofsky and Anna
Malkova and Yunlong Liu",
title = "{MMBIRFinder}: a tool to detect microhomology-mediated
break-induced replication",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "799--806",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2359450",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The introduction of next-generation sequencing
technologies has radically changed the way we view
structural genetic events. Microhomology-mediated
break-induced replication (MMBIR) is just one of the
many mechanisms that can cause genomic destabilization
that may lead to cancer. Although the mechanism for
MMBIR remains unclear, it has been shown that MMBIR is
typically associated with template-switching events.
Currently, to our knowledge, there is no existing
bioinformatics tool to detect these template-switching
events. We have developed MMBIRFinder, a method that
detects template-switching events associated with MMBIR
from whole-genome sequenced data. MMBIRFinder uses a
half-read alignment approach to identify potential
regions of interest. Clustering of these potential
regions helps narrow the search space to regions with
strong evidence. Subsequent local alignments identify
the template-switching events with single-nucleotide
accuracy. Using simulated data, MMBIRFinder identified
83 percent of the MMBIR regions within a five
nucleotide tolerance. Using real data, MMBIRFinder
identified 16 MMBIR regions on a normal breast tissue
data sample and 51 MMBIR regions on a triple-negative
breast cancer tumor sample resulting in detection of 37
novel template-switching events. Finally, we identified
template-switching events residing in the promoter
region of seven genes that have been implicated in
breast cancer. The program is freely available for
download at https://github.com/msegar/MMBIRFinder.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Matthews:2015:HCL,
author = "Suzanne J. Matthews",
title = "Heterogeneous compression of large collections of
evolutionary trees",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "807--814",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2366756",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Compressing heterogeneous collections of trees is an
open problem in computational phylogenetics. In a
heterogeneous tree collection, each tree can contain a
unique set of taxa. An ideal compression method would
allow for the efficient archival of large tree
collections and enable scientists to identify common
evolutionary relationships over disparate analyses. In
this paper, we extend TreeZip to compress heterogeneous
collections of trees. TreeZip is the most efficient
algorithm for compressing homogeneous tree collections.
To the best of our knowledge, no other domain-based
compression algorithm exists for large heterogeneous
tree collections or enable their rapid analysis. Our
experimental results indicate that TreeZip averages
89.03 percent (72.69 percent) space savings on
unweighted (weighted) collections of trees when the
level of heterogeneity in a collection is moderate. The
organization of the TRZ file allows for efficient
computations over heterogeneous data. For example,
consensus trees can be computed in mere seconds.
Lastly, combining the TreeZip compressed (TRZ) file
with general-purpose compression yields average space
savings of 97.34 percent (81.43 percent) on unweighted
(weighted) collections of trees. Our results lead us to
believe that TreeZip will prove invaluable in the
efficient archival of tree collections, and enables
scientists to develop novel methods for relating
heterogeneous collections of trees.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2015:CCA,
author = "Jianxin Wang and Jiancheng Zhong and Gang Chen and Min
Li and Fang-xiang Wu and Yi Pan",
title = "{ClusterViz}: a cytoscape {APP} for cluster analysis
of biological network",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "815--822",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2361348",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Cluster analysis of biological networks is one of the
most important approaches for identifying functional
modules and predicting protein functions. Furthermore,
visualization of clustering results is crucial to
uncover the structure of biological networks. In this
paper, ClusterViz, an APP of Cytoscape 3 for cluster
analysis and visualization, has been developed. In
order to reduce complexity and enable extendibility for
ClusterViz, we designed the architecture of ClusterViz
based on the framework of Open Services Gateway
Initiative. According to the architecture, the
implementation of ClusterViz is partitioned into three
modules including interface of ClusterViz, clustering
algorithms and visualization and export. ClusterViz
fascinates the comparison of the results of different
algorithms to do further related analysis. Three
commonly used clustering algorithms, FAG-EC, EAGLE and
MCODE, are included in the current version. Due to
adopting the abstract interface of algorithms in module
of the clustering algorithms, more clustering
algorithms can be included for the future use. To
illustrate usability of ClusterViz, we provided three
examples with detailed steps from the important
scientific articles, which show that our tool has
helped several research teams do their research work on
the mechanism of the biological networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nepomuceno-Chamorro:2015:BTA,
author = "Isabel A. Nepomuceno-Chamorro and Alfonso
Marquez-Chamorro and Jesus S. Aguilar-Ruiz",
title = "Building transcriptional association networks in
cytoscape with {RegNetC}",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "823--824",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2385702",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The Regression Network plugin for Cytoscape ( RegNetC
) implements the RegNet algorithm for the inference of
transcriptional association network from gene
expression profiles. This algorithm is a model
tree-based method to detect the relationship between
each gene and the remaining genes simultaneously
instead of analyzing individually each pair of genes as
correlation-based methods do. Model trees are a very
useful technique to estimate the gene expression value
by regression models and favours localized similarities
over more global similarity, which is one of the major
drawbacks of correlation-based methods. Here, we
present an integrated software suite, named RegNetC, as
a Cytoscape plugin that can operate on its own as well.
RegNetC facilitates, according to user-defined
parameters, the resulted transcriptional gene
association network in .sif format for visualization,
analysis and interoperates with other Cytoscape
plugins, which can be exported for publication figures.
In addition to the network, the RegNetC plugin also
provides the quantitative relationships between genes
expression values of those genes involved in the
inferred network, i.e., those defined by the regression
models.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Taha:2015:ISI,
author = "Kamal Taha and Paul D. Yoo and Mohammed Alzaabi",
title = "{iPFPi}: a system for improving protein function
prediction through cumulative iterations",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "825--836",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2344681",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We propose a classifier system called iPFPi that
predicts the functions of un-annotated proteins. iPFPi
assigns an un-annotated protein P the functions of GO
annotation terms that are semantically similar to P. An
un-annotated protein P and a GO annotation term T are
represented by their characteristics. The
characteristics of P are GO terms found within the
abstracts of biomedical literature associated with P.
The characteristics of T are GO terms found within the
abstracts of biomedical literature associated with the
proteins annotated with the function of T. Let F and F
' be the important (dominant) sets of characteristic
terms representing T and P, respectively. iPFPi would
annotate P with the function of T, if F and F ' are
semantically similar. We constructed a novel semantic
similarity measure that takes into consideration
several factors, such as the dominance degree of each
characteristic term t in set F based on its score,
which is a value that reflects the dominance status of
t relative to other characteristic terms, using
pairwise beats and looses procedure. Every time a
protein P is annotated with the function of T, iPFPi
updates and optimizes the current scores of the
characteristic terms for T based on the weights of the
characteristic terms for P. Set F will be updated
accordingly. Thus, the accuracy of predicting the
function of T as the function of subsequent proteins
improves. This prediction accuracy keeps improving over
time iteratively through the cumulative weights of the
characteristic terms representing proteins that are
successively annotated with the function of T. We
evaluated the quality of iPFPi by comparing it
experimentally with two recent protein function
prediction systems. Results showed marked
improvement.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chicco:2015:SSG,
author = "Davide Chicco and Marco Masseroli",
title = "Software suite for gene and protein annotation
prediction and similarity search",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "837--843",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2382127",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In the computational biology community, machine
learning algorithms are key instruments for many
applications, including the prediction of
gene-functions based upon the available biomolecular
annotations. Additionally, they may also be employed to
compute similarity between genes or proteins. Here, we
describe and discuss a software suite we developed to
implement and make publicly available some of such
prediction methods and a computational technique based
upon Latent Semantic Indexing (LSI), which leverages
both inferred and available annotations to search for
semantically similar genes. The suite consists of three
components. BioAnnotationPredictor is a computational
software module to predict new gene-functions based
upon Singular Value Decomposition of available
annotations. SimilBio is a Web module that leverages
annotations available or predicted by
BioAnnotationPredictor to discover similarities between
genes via LSI. The suite includes also SemSim, a new
Web service built upon these modules to allow accessing
them programmatically. We integrated SemSim in the Bio
Search Computing framework
(http://www.bioinformatics.deib.polimi.it/bio-seco/seco/),
where users can exploit the Search Computing technology
to run multi-topic complex queries on multiple
integrated Web services. Accordingly, researchers may
obtain ranked answers involving the computation of the
functional similarity between genes in support of
biomedical knowledge discovery.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xiang:2015:RAA,
author = "Yan-Ping Xiang and Ke Liu and Xian-Ying Cheng and
Cheng Cheng and Fang Gong and Jian-Bo Pan and Zhi-Liang
Ji",
title = "Rapid assessment of adverse drug reactions by
statistical solution of gene association network",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "844--850",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2338292",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Adverse drug reaction (ADR) is a common clinical
problem, sometimes accompanying with high risk of
mortality and morbidity. It is also one of the major
factors that lead to failure in new drug development.
Unfortunately, most of current experimental and
computational methods are unable to evaluate clinical
safety of drug candidates in early drug discovery stage
due to the very limited knowledge of molecular
mechanisms underlying ADRs. Therefore, in this study,
we proposed a novel na{\"\i}ve Bayesian model for rapid
assessment of clinical ADRs with frequency estimation.
This model was constructed on a gene-ADR association
network, which covered 611 US FDA approved drugs,
14,251 genes, and 1,254 distinct ADR terms. An average
detection rate of 99.86 and 99.73 percent were achieved
eventually in identification of known ADRs in internal
test data set and external case analyses respectively.
Moreover, a comparative analysis between the estimated
frequencies of ADRs and their observed frequencies was
undertaken. It is observed that these two frequencies
have the similar distribution trend. These results
suggest that the na{\"\i}ve Bayesian model based on
gene-ADR association network can serve as an efficient
and economic tool in rapid ADRs assessment.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Barma:2015:QMS,
author = "Shovan Barma and Bo-Wei Chen and Ka Lok Man and
Jhing-Fa Wang",
title = "Quantitative measurement of split of the second heart
sound ({S2})",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "851--860",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2351804",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This study proposes a quantitative measurement of
split of the second heart sound (S2) based on
nonstationary signal decomposition to deal with
overlaps and energy modeling of the subcomponents of
S2. The second heart sound includes aortic (A2) and
pulmonic (P2) closure sounds. However, the split
detection is obscured due to A2-P2 overlap and low
energy of P2. To identify such split, HVD method is
used to decompose the S2 into a number of components
while preserving the phase information. Further, A2s
and P2s are localized using smoothed pseudo
Wigner-Ville distribution followed by reassignment
method. Finally, the split is calculated by taking the
differences between the means of time indices of A2s
and P2s. Experiments on total 33 clips of S2 signals
are performed for evaluation of the method. The mean
\pm standard deviation of the split is 34.7 \pm 4.6 ms.
The method measures the split efficiently, even when
A2-P2 overlap is {$<$}= 20 ms and the normalized peak
temporal ratio of P2 to A2 is low ({$>$}= 0.22). This
proposed method thus, demonstrates its robustness by
defining split detectability (SDT), the split detection
aptness through detecting P2s, by measuring upto 96
percent. Such findings reveal the effectiveness of the
method as competent against the other baselines,
especially for A2-P2 overlaps and low energy P2.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Golshan:2015:OLB,
author = "Hosein M. Golshan and Reza P. R. Hasanzadeh",
title = "An optimized {LMMSE} based method for {$3$D} {MRI}
denoising",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "861--870",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2344675",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Post-acquisition denoising of magnetic resonance (MR)
images is an important step to improve any quantitative
measurement of the acquired data. In this paper,
assuming a Rician noise model, a new filtering method
based on the linear minimum mean square error (LMMSE)
estimation is introduced, which employs the
self-similarity property of the MR data to restore the
noise-less signal. This method takes into account the
structural characteristics of images and the Bayesian
mean square error (Bmse) of the estimator to address
the denoising problem. In general, a twofold data
processing approach is developed; first, the noisy MR
data is processed using a patch-based L$^2$ -norm
similarity measure to provide the primary set of
samples required for the estimation process.
Afterwards, the Bmse of the estimator is derived as the
optimization function to analyze the pre-selected
samples and minimize the error between the estimated
and the underlying signal. Compared to the LMMSE method
and also its recently proposed SNR-adapted realization
(SNLMMSE), the optimized way of choosing the samples
together with the automatic adjustment of the filtering
parameters lead to a more robust estimation performance
with our approach. Experimental results show the
competitive performance of the proposed method in
comparison with related state-of-the-art methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Varghese:2015:RLW,
author = "Blesson Varghese and Ishan Patel and Adam Barker",
title = "{RBioCloud}: a light-weight framework for bioconductor
and {R}-based jobs on the cloud",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "871--878",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2361327",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/s-plus.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Large-scale ad hoc analytics of genomic data is
popular using the R-programming language supported by
over 700 software packages provided by Bioconductor.
More recently, analytical jobs are benefitting from
on-demand computing and storage, their scalability and
their low maintenance cost, all of which are offered by
the cloud. While biologists and bioinformaticists can
take an analytical job and execute it on their personal
workstations, it remains challenging to seamlessly
execute the job on the cloud infrastructure without
extensive knowledge of the cloud dashboard. How
analytical jobs can not only with minimum effort be
executed on the cloud, but also how both the resources
and data required by the job can be managed is explored
in this paper. An open-source light-weight framework
for executing R-scripts using Bioconductor packages,
referred to as 'RBioCloud', is designed and developed.
RBioCloud offers a set of simple command-line tools for
managing the cloud resources, the data and the
execution of the job. Three biological test cases
validate the feasibility of RBioCloud. The framework is
available from http://www.rbiocloud.com.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2015:NMD,
author = "Wei Zhang and Xiufen Zou",
title = "A new method for detecting protein complexes based on
the three node cliques",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "879--886",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2386314",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The identification of protein complexes in
protein-protein interaction (PPI) networks is
fundamental for understanding biological processes and
cellular molecular mechanisms. Many graph computational
algorithms have been proposed to identify protein
complexes from PPI networks by detecting densely
connected groups of proteins. These algorithms assess
the density of subgraphs through evaluation of the sum
of individual edges or nodes; thus, incomplete and
inaccurate measures may miss meaningful biological
protein complexes with functional significance. In this
study, we propose a novel method for assessing the
compactness of local subnetworks by measuring the
number of three node cliques. The present method
detects each optimal cluster by growing a seed and
maximizing the compactness function. To demonstrate the
efficacy of the new proposed method, we evaluate its
performance using five PPI networks on three reference
sets of yeast protein complexes with five different
measurements and compare the performance of the
proposed method with four state-of-the-art methods. The
results show that the protein complexes generated by
the proposed method are of better quality than those
generated by four classic methods. Therefore, the new
proposed method is effective and useful for detecting
protein complexes in PPI networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2015:AFC,
author = "Zhiwen Yu and Hantao Chen and Jane You and Jiming Liu
and Hau-San Wong and Guoqiang Han and Le Li",
title = "Adaptive fuzzy consensus clustering framework for
clustering analysis of cancer data",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "887--901",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2359433",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Performing clustering analysis is one of the important
research topics in cancer discovery using gene
expression profiles, which is crucial in facilitating
the successful diagnosis and treatment of cancer. While
there are quite a number of research works which
perform tumor clustering, few of them considers how to
incorporate fuzzy theory together with an optimization
process into a consensus clustering framework to
improve the performance of clustering analysis. In this
paper, we first propose a random double clustering
based cluster ensemble framework (RDCCE) to perform
tumor clustering based on gene expression data.
Specifically, RDCCE generates a set of representative
features using a randomly selected clustering algorithm
in the ensemble, and then assigns samples to their
corresponding clusters based on the grouping results.
In addition, we also introduce the random double
clustering based fuzzy cluster ensemble framework
(RDCFCE), which is designed to improve the performance
of RDCCE by integrating the newly proposed fuzzy
extension model into the ensemble framework. RDCFCE
adopts the normalized cut algorithm as the consensus
function to summarize the fuzzy matrices generated by
the fuzzy extension models, partition the consensus
matrix, and obtain the final result. Finally, adaptive
RDCFCE (A-RDCFCE) is proposed to optimize RDCFCE and
improve the performance of RDCFCE further by adopting a
self-evolutionary process (SEPP) for the parameter set.
Experiments on real cancer gene expression profiles
indicate that RDCFCE and A-RDCFCE works well on these
data sets, and outperform most of the state-of-the-art
tumor clustering algorithms.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Deng:2015:IFF,
author = "Lei Deng and Zhigang Chen",
title = "An integrated framework for functional annotation of
protein structural domains",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "902--913",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2389213",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Structural domains are evolutionary and functional
units of proteins and play a critical role in
comparative and functional genomics. Computational
assignment of domain function with high reliability is
essential for understanding whole-protein functions.
However, functional annotations are conventionally
assigned onto full-length proteins rather than
associating specific functions to the individual
structural domains. In this article, we present
Structural Domain Annotation (SDA), a novel
computational approach to predict functions for SCOP
structural domains. The SDA method integrates
heterogeneous information sources, including structure
alignment based protein-SCOP mapping features,
InterPro2GO mapping information, PSSM Profiles, and
sequence neighborhood features, with a Bayesian
network. By large-scale annotating Gene Ontology terms
to SCOP domains with SDA, we obtained a database of
SCOP domain to Gene Ontology mappings, which contains
~162,000 out of the approximately 166,900 domains in
SCOPe 2.03 ({$>$97} percent) and their predicted Gene
Ontology functions. We have benchmarked SDA using a
single-domain protein dataset and an independent
dataset from different species. Comparative studies
show that SDA significantly outperforms the existing
function prediction methods for structural domains in
terms of coverage and maximum F-measure.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ranjbar:2015:BNM,
author = "Mohammad R. Nezami Ranjbar and Mahlet G. Tadesse and
Yue Wang and Habtom W. Ressom",
title = "{Bayesian} normalization model for label-free
quantitative analysis by {LC--MS}",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "914--927",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2377723",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We introduce a new method for normalization of data
acquired by liquid chromatography coupled with mass
spectrometry (LC--MS) in label-free differential
expression analysis. Normalization of LC--MS data is
desired prior to subsequent statistical analysis to
adjust variabilities in ion intensities that are not
caused by biological differences but experimental bias.
There are different sources of bias including
variabilities during sample collection and sample
storage, poor experimental design, noise, etc. In
addition, instrument variability in experiments
involving a large number of LC--MS runs leads to a
significant drift in intensity measurements. Although
various methods have been proposed for normalization of
LC--MS data, there is no universally applicable
approach. In this paper, we propose a Bayesian
normalization model (BNM) that utilizes scan-level
information from LC--MS data. Specifically, the
proposed method uses peak shapes to model the
scan-level data acquired from extracted ion
chromatograms (EIC) with parameters considered as a
linear mixed effects model. We extended the model into
BNM with drift (BNMD) to compensate for the variability
in intensity measurements due to long LC--MS runs. We
evaluated the performance of our method using synthetic
and experimental data. In comparison with several
existing methods, the proposed BNM and BNMD yielded
significant improvement.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liang:2015:IDA,
author = "Muxuan Liang and Zhizhong Li and Ting Chen and
Jianyang Zeng",
title = "Integrative data analysis of multi-platform cancer
data with a multimodal deep learning approach",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "928--937",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2377729",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Identification of cancer subtypes plays an important
role in revealing useful insights into disease
pathogenesis and advancing personalized therapy. The
recent development of high-throughput sequencing
technologies has enabled the rapid collection of
multi-platform genomic data (e.g., gene expression,
miRNA expression, and DNA methylation) for the same set
of tumor samples. Although numerous integrative
clustering approaches have been developed to analyze
cancer data, few of them are particularly designed to
exploit both deep intrinsic statistical properties of
each input modality and complex cross-modality
correlations among multi-platform input data. In this
paper, we propose a new machine learning model, called
multimodal deep belief network (DBN), to cluster cancer
patients from multi-platform observation data. In our
integrative clustering framework, relationships among
inherent features of each single modality are first
encoded into multiple layers of hidden variables, and
then a joint latent model is employed to fuse common
features derived from multiple input modalities. A
practical learning algorithm, called contrastive
divergence (CD), is applied to infer the parameters of
our multimodal DBN model in an unsupervised manner.
Tests on two available cancer datasets show that our
integrative data analysis approach can effectively
extract a unified representation of latent features to
capture both intra- and cross-modality correlations,
and identify meaningful disease subtypes from
multi-platform cancer data. In addition, our approach
can identify key genes and miRNAs that may play
distinct roles in the pathogenesis of different cancer
subtypes. Among those key miRNAs, we found that the
expression level of miR-29a is highly correlated with
survival time in ovarian cancer patients. These results
indicate that our multimodal DBN based data analysis
approach may have practical applications in cancer
pathogenesis studies and provide useful guidelines for
personalized cancer therapy.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dehghannasiri:2015:OED,
author = "Roozbeh Dehghannasiri and Byung-Jun Yoon and Edward R.
Dougherty",
title = "Optimal experimental design for gene regulatory
networks in the presence of uncertainty",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "938--950",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2377733",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Of major interest to translational genomics is the
intervention in gene regulatory networks (GRNs) to
affect cell behavior; in particular, to alter
pathological phenotypes. Owing to the complexity of
GRNs, accurate network inference is practically
challenging and GRN models often contain considerable
amounts of uncertainty. Considering the cost and time
required for conducting biological experiments, it is
desirable to have a systematic method for prioritizing
potential experiments so that an experiment can be
chosen to optimally reduce network uncertainty.
Moreover, from a translational perspective it is
crucial that GRN uncertainty be quantified and reduced
in a manner that pertains to the operational cost that
it induces, such as the cost of network intervention.
In this work, we utilize the concept of mean objective
cost of uncertainty (MOCU) to propose a novel framework
for optimal experimental design. In the proposed
framework, potential experiments are prioritized based
on the MOCU expected to remain after conducting the
experiment. Based on this prioritization, one can
select an optimal experiment with the largest potential
to reduce the pertinent uncertainty present in the
current network model. We demonstrate the effectiveness
of the proposed method via extensive simulations based
on synthetic and real gene regulatory networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2015:SDI,
author = "Ting Chen and Ulisses M. Braga-Neto",
title = "Statistical detection of intrinsically multivariate
predictive genes",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "951--963",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2377731",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Canalizing genes possess broad regulatory power over a
wide swath of regulatory processes. On the other hand,
it has been hypothesized that the phenomenon of
intrinsically multivariate prediction (IMP) is
associated with canalization. However, applications
have relied on user-selectable thresholds on the IMP
score to decide on the presence of IMP. A methodology
is developed here that avoids arbitrary thresholds, by
providing a statistical test for the IMP score. In
addition, the proposed procedure allows the
incorporation of prior knowledge if available, which
can alleviate the problem of loss of power due to small
sample sizes. The issue of multiplicity of tests is
addressed by family-wise error rate (FWER) and false
discovery rate (FDR) controlling approaches. The
proposed methodology is demonstrated by experiments
using synthetic and real gene-expression data from
studies on melanoma and ionizing radiation (IR)
responsive genes. The results with the real data
identified DUSP1 and p53, two well-known canalizing
genes associated with melanoma and IR response,
respectively, as the genes with a clear majority of IMP
predictor pairs. This validates the potential of the
proposed methodology as a tool for discovery of
canalizing genes from binary gene-expression data. The
procedure is made available through an R package.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2015:RBT,
author = "Jin-Xing Liu and Yong Xu and Chun-Hou Zheng and Heng
Kong and Zhi-Hui Lai",
title = "{RPCA}-based tumor classification using gene
expression data",
journal = j-TCBB,
volume = "12",
number = "4",
pages = "964--970",
month = jul,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2383375",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Sep 16 18:55:37 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Microarray techniques have been used to delineate
cancer groups or to identify candidate genes for cancer
prognosis. As such problems can be viewed as
classification ones, various classification methods
have been applied to analyze or interpret gene
expression data. In this paper, we propose a novel
method based on robust principal component analysis
(RPCA) to classify tumor samples of gene expression
data. Firstly, RPCA is utilized to highlight the
characteristic genes associated with a special
biological process. Then, RPCA and RPCA+LDA (robust
principal component analysis and linear discriminant
analysis) are used to identify the features. Finally,
support vector machine (SVM) is applied to classify the
tumor samples of gene expression data based on the
identified features. Experiments on seven data sets
demonstrate that our methods are effective and feasible
for tumor classification.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gomez-Pulido:2015:ABA,
author = "Juan A. Gomez-Pulido and Bertil Schmidt and Wu-chun
Feng",
title = "Accelerating bioinformatics applications via emerging
parallel computing systems",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "971--972",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2457736",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fernandez:2015:FFB,
author = "Edward B. Fernandez and Jason Villarreal and Stefano
Lonardi and Walid A. Najjar",
title = "{FHAST}: {FPGA}-Based Acceleration of Bowtie in
Hardware",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "973--981",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2405333",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gonzalez-Dominguez:2015:PED,
author = "Jorge Gonzalez-Dominguez and Lars Wienbrandt and Jan
Christian Kassens and David Ellinghaus and Manfred
Schimmler and Bertil Schmidt",
title = "Parallelizing Epistasis Detection in {GWAS} on {FPGA}
and {GPU}-Accelerated Computing Systems",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "982--994",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2389958",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Martinez:2015:CAS,
author = "Hector Martinez and Joaquin Tarraga and Ignacio Medina
and Sergio Barrachina and Maribel Castillo and Joaquin
Dopazo and Enrique S. Quintana-Orti",
title = "Concurrent and Accurate Short Read Mapping on
Multicore Processors",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "995--1007",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2392077",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Misra:2015:PMI,
author = "Sanchit Misra and Kiran Pamnany and Srinivas Aluru",
title = "Parallel Mutual Information Based Construction of
Genome-Scale Networks on the {Intel Xeon Phi}
Coprocessor",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1008--1020",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2415931",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jeannin-Girardon:2015:LST,
author = "Anne Jeannin-Girardon and Pascal Ballet and Vincent
Rodin",
title = "Large Scale Tissue Morphogenesis Simulation on
Heterogeneous Systems Based on a Flexible Biomechanical
Cell Model",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1021--1033",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2418994",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xin:2015:ASI,
author = "Yao Xin and Will X. Y. Li and Zhaorui Zhang and Ray C.
C. Cheung and Dong Song and Theodore W. Berger",
title = "An Application Specific Instruction Set Processor
{(ASIP)} for Adaptive Filters in Neural Prosthetics",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1034--1047",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2440248",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chacon:2015:BFI,
author = "Alejandro Chacon and Santiago Marco-Sola and Antonio
Espinosa and Paolo Ribeca and Juan Carlos Moure",
title = "Boosting the {FM-Index} on the {GPU}: Effective
Techniques to Mitigate Random Memory Access",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1048--1059",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2377716",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nguyen:2015:EAO,
author = "Thuy-Diem Nguyen and Bertil Schmidt and Zejun Zheng
and Chee-Keong Kwoh",
title = "Efficient and Accurate {OTU} Clustering with
{GPU}-Based Sequence Alignment and Dynamic Dendrogram
Cutting",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1060--1073",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2407574",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2015:GES,
author = "Shihua Zhang and Luonan Chen",
title = "Guest Editorial for Special Section on {ISB\slash TBC
2014}",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1074--1075",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2443211",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Frost:2015:IFG,
author = "H. Robert Frost and Zhigang Li and Folkert W.
Asselbergs and Jason H. Moore",
title = "An Independent Filter for Gene Set Testing Based on
Spectral Enrichment",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1076--1086",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2415815",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chu:2015:EPY,
author = "Dominique Chu and Anton Salykin",
title = "Evolutionary Pressures on the Yeast Transcriptome",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1087--1093",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2420554",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kang:2015:ISO,
author = "Hao Kang and Kwang-Hyun Cho and Xiaohua Douglas Zhang
and Tao Zeng and Luonan Chen",
title = "Inferring Sequential Order of Somatic Mutations during
Tumorgenesis based on {Markov} Chain Model",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1094--1103",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2424408",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Meng:2015:ICG,
author = "Nan Meng and Raghu Machiraju and Kun Huang",
title = "Identify Critical Genes in Development with Consistent
{H3K4me2} Patterns across Multiple Tissues",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1104--1111",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2430340",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Qiu:2015:IMA,
author = "Yu-Qing Qiu and Xue Tian and Shihua Zhang",
title = "Infer Metagenomic Abundance and Reveal Homologous
Genomes Based on the Structure of Taxonomy Tree",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1112--1122",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2415814",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Leoncini:2015:CCT,
author = "Mauro Leoncini and Manuela Montangero and Marco
Pellegrini and Karina Panucia Tillan",
title = "{CMStalker}: a Combinatorial Tool for Composite Motif
Discovery",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1123--1136",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2359444",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zrimec:2015:FPD,
author = "Jan Zrimec and Ales Lapanje",
title = "Fast Prediction of {DNA} Melting Bubbles Using {DNA}
Thermodynamic Stability",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1137--1145",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2396057",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2015:FMA,
author = "Shuqin Zhang and Hongyu Zhao and Michael K. Ng",
title = "Functional Module Analysis for Gene Coexpression
Networks with Network Integration",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1146--1160",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2396073",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kim:2015:IGN,
author = "Jeong-Rae Kim and Sang-Mok Choo and Hyung-Seok Choi
and Kwang-Hyun Cho",
title = "Identification of Gene Networks with Time Delayed
Regulation Based on Temporal Expression Profiles",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1161--1168",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2394312",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lo:2015:ITD,
author = "Leung-Yau Lo and Kwong-Sak Leung and Kin-Hong Lee",
title = "Inferring Time-Delayed Causal Gene Network Using
Time-Series Expression Data",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1169--1182",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2394442",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kleftogiannis:2015:YNE,
author = "Dimitrios Kleftogiannis and Konstantinos Theofilatos
and Spiros Likothanassis and Seferina Mavroudi",
title = "{YamiPred}: a Novel Evolutionary Method for Predicting
{Pre-miRNAs} and Selecting Relevant Features",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1183--1192",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2388227",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lovato:2015:MAP,
author = "Pietro Lovato and Alejandro Giorgetti and Manuele
Bicego",
title = "A Multimodal Approach for Protein Remote Homology
Detection",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1193--1198",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2424417",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bordon:2015:FLC,
author = "Jure Bordon and Miha Moskon and Nikolaj Zimic and Miha
Mraz",
title = "Fuzzy Logic as a Computational Tool for Quantitative
Modelling of Biological Systems with Uncertain Kinetic
Data",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1199--1205",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2424424",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2015:SGC,
author = "Po-Kuei Chen and Chun-Liang Lin",
title = "Synthesis of Genetic Clock with Combinational Biologic
Circuits",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1206--1212",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2396060",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Athanasiadis:2015:ZAM,
author = "Emmanouil I. Athanasiadis and Marilena M. Bourdakou
and George M. Spyrou",
title = "{ZoomOut}: Analyzing Multiple Networks as Single
Nodes",
journal = j-TCBB,
volume = "12",
number = "5",
pages = "1213--1216",
month = sep,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2424411",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Dec 8 06:52:41 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huang:2015:GEA,
author = "Hsien-Da Huang and Yi-Ping Phoebe Chen",
title = "Guest Editorial for the {13th Asia Pacific
Bioinformatics Conference}",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1217--1218",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2451231",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cheng:2015:USA,
author = "Liang Cheng and Jie Li and Yang Hu and Yue Jiang and
Yongzhuang Liu and Yanshuo Chu and Zhenxing Wang and
Yadong Wang",
title = "Using Semantic Association to Extend and Infer
Literature-Oriented Relativity Between Terms",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1219--1226",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2430289",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Relative terms often appear together in the
literature. Methods have been presented for weighting
relativity of pairwise terms by their co-occurring
literature and inferring new relationship. Terms in the
literature are also in the directed acyclic graph of
ontologies, such as Gene Ontology and Disease Ontology.
Therefore, semantic association between terms may help
for establishing relativities between terms in
literature. However, current methods do not use these
associations. In this paper, an adjusted R-scaled score
(ARSS) based on information content (ARSSIC) method is
introduced to infer new relationship between terms.
First, set inclusion relationship between terms of
ontology was exploited to extend relationships between
these terms and literature. Next, the ARSS method was
presented to measure relativity between terms across
ontologies according to these extensional
relationships. Then, the ARSSIC method using ratios of
information shared of term's ancestors was designed to
infer new relationship between terms across ontologies.
The result of the experiment shows that ARSS identified
more pairs of statistically significant terms based on
corresponding gene sets than other methods. And the
high average area under the receiver operating
characteristic curve (0.9293) shows that ARSSIC
achieved a high true positive rate and a low false
positive rate. Data is available at
http://mlg.hit.edu.cn/ARSSIC/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wieseke:2015:CRI,
author = "Nicolas Wieseke and Tom Hartmann and Matthias Bernt
and Martin Middendorf",
title = "Cophylogenetic Reconciliation with {ILP}",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1227--1235",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2430336",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, we present an integer linear
programming (ILP) approach, called CoRe-ILP, for
finding an optimal time consistent cophylogenetic
host-parasite reconciliation under the cophylogenetic
event model with the events cospeciation, duplication,
sorting, host switch, and failure to diverge. Instead
of assuming event costs, a simplified model is used,
maximizing primarily for cospeciations and secondarily
minimizing host switching events. Duplications,
sortings, and failure to diverge events are not
explicitly scored. Different from existing event based
reconciliation methods, CoRe-ILP can use (approximate)
phylogenetic branch lengths for filtering possible
ancestral host-parasite interactions. Experimentally,
it is shown that CoRe-ILP can successfully use branch
length information and performs well for biological and
simulated data sets. The results of CoRe-ILP are
compared with the results of the reconciliation tools
Jane 4, Treemap 3b, NOTUNG 2.8 Beta, and Ranger-DTL.
Algorithm CoRe-ILP is implemented using IBM ILOG CPLEX
Optimizer 12.6 and is freely available from
http://pacosy.informatik.uni-leipzig.de/core-ilp.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2015:DIN,
author = "Huidong Chen and Jihong Guan and Shuigeng Zhou",
title = "{DPNuc}: Identifying Nucleosome Positions Based on the
{Dirichlet} Process Mixture Model",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1236--1247",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2430350",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Nucleosomes and the free linker DNA between them
assemble the chromatin. Nucleosome positioning plays an
important role in gene transcription regulation, DNA
replication and repair, alternative splicing, and so
on. With the rapid development of ChIP-seq, it is
possible to computationally detect the positions of
nucleosomes on chromosomes. However, existing methods
cannot provide accurate and detailed information about
the detected nucleosomes, especially for the
nucleosomes with complex configurations where overlaps
and noise exist. Meanwhile, they usually require some
prior knowledge of nucleosomes as input, such as the
size or the number of the unknown nucleosomes, which
may significantly influence the detection results. In
this paper, we propose a novel approach DPNuc for
identifying nucleosome positions based on the Dirichlet
process mixture model. In our method, Markov chain
Monte Carlo (MCMC) simulations are employed to
determine the mixture model with no need of prior
knowledge about nucleosomes. Compared with three
existing methods, our approach can provide more
detailed information of the detected nucleosomes and
can more reasonably reveal the real configurations of
the chromosomes; especially, our approach performs
better in the complex overlapping situations. By
mapping the detected nucleosomes to a synthetic
benchmark nucleosome map and two existing benchmark
nucleosome maps, it is shown that our approach achieves
a better performance in identifying nucleosome
positions and gets a higher $F$-score. Finally, we show
that our approach can more reliably detect the size
distribution of nucleosomes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gao:2015:CCE,
author = "Nan Gao and Yan Zhang and Bing Feng and Jijun Tang",
title = "A Cooperative Co-Evolutionary Genetic Algorithm for
Tree Scoring and Ancestral Genome Inference",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1248--1254",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2430860",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recent advances of technology have made it easy to
obtain and compare whole genomes. Rearrangements of
genomes through operations such as reversals and
transpositions are rare events that enable researchers
to reconstruct deep evolutionary history among species.
Some of the popular methods need to search a large tree
space for the best scored tree, thus it is desirable to
have a fast and accurate method that can score a given
tree efficiently. During the tree scoring procedure,
the genomic structures of internal tree nodes are also
provided, which provide important information for
inferring ancestral genomes and for modeling the
evolutionary processes. However, computing tree scores
and ancestral genomes are very difficult and a lot of
researchers have to rely on heuristic methods which
have various disadvantages. In this paper, we describe
the first genetic algorithm for tree scoring and
ancestor inference, which uses a fitness function
considering co-evolution, adopts different initial
seeding methods to initialize the first population
pool, and utilizes a sorting-based approach to realize
evolution. Our extensive experiments show that compared
with other existing algorithms, this new method is more
accurate and can infer ancestral genomes that are much
closer to the true ancestors.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xie:2015:LFA,
author = "Minzhu Xie and Jianxin Wang and Xin Chen",
title = "{LGH}: a Fast and Accurate Algorithm for Single
Individual Haplotyping Based on a Two-Locus Linkage
Graph",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1255--1266",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2430352",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Phased haplotype information is crucial in our
complete understanding of differences between
individuals at the genetic level. Given a collection of
DNA fragments sequenced from a homologous pair of
chromosomes, the problem of single individual
haplotyping (SIH) aims to reconstruct a pair of
haplotypes using a computer algorithm. In this paper,
we encode the information of aligned DNA fragments into
a two-locus linkage graph and approach the SIH problem
by vertex labeling of the graph. In order to find a
vertex labeling with the minimum sum of weights of
incompatible edges, we develop a fast and accurate
heuristic algorithm. It starts with detecting
error-tolerant components by an adapted breadth-first
search. A proper labeling of vertices is then
identified for each component, with which sequencing
errors are further corrected and edge weights are
adjusted accordingly. After contracting each
error-tolerant component into a single vertex, the
above procedure is iterated on the resulting condensed
linkage graph until error-tolerant components are no
longer detected. The algorithm finally outputs a
haplotype pair based on the vertex labeling. Extensive
experiments on simulated and real data show that our
algorithm is more accurate and faster than five
existing algorithms for single individual
haplotyping.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kumozaki:2015:MLB,
author = "Shotaro Kumozaki and Kengo Sato and Yasubumi
Sakakibara",
title = "A Machine Learning Based Approach to de novo
Sequencing of Glycans from Tandem Mass Spectrometry
Spectrum",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1267--1274",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2430317",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recently, glycomics has been actively studied and
various technologies for glycomics have been rapidly
developed. Currently, tandem mass spectrometry (MS/MS)
is one of the key experimental tools for identification
of structures of oligosaccharides. MS/MS can observe
MS/MS peaks of fragmented glycan ions including
cross-ring ions resulting from internal cleavages,
which provide valuable information to infer glycan
structures. Thus, the aim of de novo sequencing of
glycans is to find the most probable assignments of
observed MS/MS peaks to glycan substructures without
databases. However, there are few satisfiable
algorithms for glycan de novo sequencing from MS/MS
spectra. We present a machine learning based approach
to de novo sequencing of glycans from MS/MS spectrum.
First, we build a suitable model for the fragmentation
of glycans including cross-ring ions, and implement a
solver that employs Lagrangian relaxation with a
dynamic programming technique. Then, to optimize scores
for the algorithm, we introduce a machine learning
technique called structured support vector machines
that enable us to learn parameters including scores for
cross-ring ions from training data, i.e., known glycan
mass spectra. Furthermore, we implement additional
constraints for core structures of well-known glycan
types including N-linked glycans and O-linked glycans.
This enables us to predict more accurate glycan
structures if the glycan type of given spectra is
known. Computational experiments show that our
algorithm performs accurate de novo sequencing of
glycans. The implementation of our algorithm and the
datasets are available at
http://glyfon.dna.bio.keio.ac.jp/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xie:2015:CTC,
author = "Xiaojing Xie and Shuigeng Zhou and Jihong Guan",
title = "{CoGI}: Towards Compressing Genomes as an Image",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1275--1285",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2430331",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genomic science is now facing an explosive increase of
data thanks to the fast development of sequencing
technology. This situation poses serious challenges to
genomic data storage and transferring. It is desirable
to compress data to reduce storage and transferring
cost, and thus to boost data distribution and
utilization efficiency. Up to now, a number of
algorithms / tools have been developed for compressing
genomic sequences. Unlike the existing algorithms, most
of which treat genomes as one-dimensional text strings
and compress them based on dictionaries or probability
models, this paper proposes a novel approach called
CoGI (the abbreviation of Compressing Genomes as an
Image) for genome compression, which transforms the
genomic sequences to a two-dimensional binary image (or
bitmap), then applies a rectangular partition coding
algorithm to compress the binary image. CoGI can be
used as either a reference-based compressor or a
reference-free compressor. For the former, we develop
two entropy-based algorithms to select a proper
reference genome. Performance evaluation is conducted
on various genomes. Experimental results show that the
reference-based CoGI significantly outperforms two
state-of-the-art reference-based genome compressors
GReEn and RLZ-opt in both compression ratio and
compression efficiency. It also achieves comparable
compression ratio but two orders of magnitude higher
compression efficiency in comparison with XM-one
state-of-the-art reference-free genome compressor.
Furthermore, our approach performs much better than
Gzip-a general-purpose and widely-used compressor, in
both compression speed and compression ratio. So, CoGI
can serve as an effective and practical genome
compressor. The source code and other related documents
of CoGI are available at:
http://admis.fudan.edu.cn/projects/cogi.htm.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2015:BMM,
author = "Beichen Wang and Xiaodong Chen and Hiroshi Mamitsuka
and Shanfeng Zhu",
title = "{BMExpert}: Mining {MEDLINE} for Finding Experts in
Biomedical Domains Based on Language Model",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1286--1294",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2430338",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "With the rapid development of biomedical sciences, a
great number of documents have been published to report
new scientific findings and advance the process of
knowledge discovery. By the end of 2013, the largest
biomedical literature database, MEDLINE, has indexed
over 23 million abstracts. It is thus not easy for
scientific professionals to find experts on a certain
topic in the biomedical domain. In contrast to the
existing services that use some ad hoc approaches, we
developed a novel solution to biomedical expert
finding, BMExpert, based on the language model. For
finding biomedical experts, who are the most relevant
to a specific topic query, BMExpert mines MEDLINE
documents by considering three important factors:
relevance of documents to the query topic, importance
of documents, and associations between documents and
experts. The performance of BMExpert was evaluated on a
benchmark dataset, which was built by collecting the
program committee members of ISMB in the past three
years (2012-2014) on 14 different topics. Experimental
results show that BMExpert outperformed three existing
biomedical expert finding services: JANE, GoPubMed, and
eTBLAST, with respect to both MAP (mean average
precision) and P@50 (Precision). BMExpert is freely
accessed at
http://datamining-iip.fudan.edu.cn/service/BMExpert/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2015:RBC,
author = "Han Li and Chun Li and Jie Hu and Xiaodan Fan",
title = "A Resampling Based Clustering Algorithm for Replicated
Gene Expression Data",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1295--1303",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2403320",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In gene expression data analysis, clustering is a
fruitful exploratory technique to reveal the underlying
molecular mechanism by identifying groups of
co-expressed genes. To reduce the noise, usually
multiple experimental replicates are performed. An
integrative analysis of the full replicate data,
instead of reducing the data to the mean profile,
carries the promise of yielding more precise and robust
clusters. In this paper, we propose a novel resampling
based clustering algorithm for genes with replicated
expression measurements. Assuming those replicates are
exchangeable, we formulate the problem in the bootstrap
framework, and aim to infer the consensus clustering
based on the bootstrap samples of replicates. In our
approach, we adopt the mixed effect model to
accommodate the heterogeneous variances and implement a
quasi-MCMC algorithm to conduct statistical inference.
Experiments demonstrate that by taking advantage of the
full replicate data, our algorithm produces more
reliable clusters and has robust performance in diverse
scenarios, especially when the data is subject to
multiple sources of variance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Esfahani:2015:OBF,
author = "Mohammad Shahrokh Esfahani and Edward R. Dougherty",
title = "An Optimization-Based Framework for the Transformation
of Incomplete Biological Knowledge into a Probabilistic
Structure and Its Application to the Utilization of
Gene\slash Protein Signaling Pathways in Discrete
Phenotype Classification",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1304--1321",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2424407",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Phenotype classification via genomic data is hampered
by small sample sizes that negatively impact classifier
design. Utilization of prior biological knowledge in
conjunction with training data can improve both
classifier design and error estimation via the
construction of the optimal Bayesian classifier. In the
genomic setting, gene/protein signaling pathways
provide a key source of biological knowledge. Although
these pathways are neither complete, nor regulatory,
with no timing associated with them, they are capable
of constraining the set of possible models representing
the underlying interaction between molecules. The aim
of this paper is to provide a framework and the
mathematical tools to transform signaling pathways to
prior probabilities governing uncertainty classes of
feature-label distributions used in classifier design.
Structural motifs extracted from the signaling pathways
are mapped to a set of constraints on a prior
probability on a Multinomial distribution. Being the
conjugate prior for the Multinomial distribution, we
propose optimization paradigms to estimate the
parameters of a Dirichlet distribution in the Bayesian
setting. The performance of the proposed methods is
tested on two widely studied pathways: mammalian cell
cycle and a p53 pathway model.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cheng:2015:CMD,
author = "Kin-On Cheng and Paula Wu and Ngai-Fong Law and
Wan-Chi Siu",
title = "Compression of Multiple {DNA} Sequences Using
Intra-Sequence and Inter-Sequence Similarities",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1322--1332",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2403370",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Traditionally, intra-sequence similarity is exploited
for compressing a single DNA sequence. Recently,
remarkable compression performance of individual DNA
sequence from the same population is achieved by
encoding its difference with a nearly identical
reference sequence. Nevertheless, there is lack of
general algorithms that also allow less similar
reference sequences. In this work, we extend the
intra-sequence to the inter-sequence similarity in that
approximate matches of subsequences are found between
the DNA sequence and a set of reference sequences.
Hence, a set of nearly identical DNA sequences from the
same population or a set of partially similar DNA
sequences like chromosome sequences and DNA sequences
of related species can be compressed together. For
practical compressors, the compressed size is usually
influenced by the compression order of sequences. Fast
search algorithms for the optimal compression order are
thus developed for multiple sequences compression.
Experimental results on artificial and real datasets
demonstrate that our proposed multiple sequences
compression methods with fast compression order search
are able to achieve good compression performance under
different levels of similarity in the multiple DNA
sequences.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ou-Yang:2015:DPC,
author = "Le Ou-Yang and Dao-Qing Dai and Xiao-Fei Zhang",
title = "Detecting Protein Complexes from Signed
Protein-Protein Interaction Networks",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1333--1344",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2401014",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Identification of protein complexes is fundamental for
understanding the cellular functional organization.
With the accumulation of physical protein-protein
interaction (PPI) data, computational detection of
protein complexes from available PPI networks has drawn
a lot of attentions. While most of the existing protein
complex detection algorithms focus on analyzing the
physical protein-protein interaction network, none of
them take into account the ``signs'' (i.e.,
activation-inhibition relationships) of physical
interactions. As the ``signs'' of interactions reflect
the way proteins communicate, considering the ``signs''
of interactions can not only increase the accuracy of
protein complex identification, but also deepen our
understanding of the mechanisms of cell functions. In
this study, we proposed a novel Signed Graph
regularized Nonnegative Matrix Factorization (SGNMF)
model to identify protein complexes from signed PPI
networks. In our experiments, we compared the results
collected by our model on signed PPI networks with
those predicted by the state-of-the-art complex
detection techniques on the original unsigned PPI
networks. We observed that considering the ``signs'' of
interactions significantly benefits the detection of
protein complexes. Furthermore, based on the predicted
complexes, we predicted a set of signed complex-complex
interactions for each dataset, which provides a novel
insight of the higher level organization of the cell.
All the experimental results and codes can be
downloaded from
http://mail.sysu.edu.cn/home/ HREF="mailto:stsddq@mail.sysu.edu.cn">stsddq@mail.sysu.edu.cn/dai/others/SGNMF.zip.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tomescu:2015:EWD,
author = "Alexandru I. Tomescu and Travis Gagie and Alexandru
Popa and Romeo Rizzi and Anna Kuosmanen and Veli
Makinen",
title = "Explaining a Weighted {DAG} with Few Paths for Solving
Genome-Guided Multi-Assembly",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1345--1354",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2418753",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "RNA-Seq technology offers new high-throughput ways for
transcript identification and quantification based on
short reads, and has recently attracted great interest.
This is achieved by constructing a weighted DAG whose
vertices stand for exons, and whose arcs stand for
split alignments of the RNA-Seq reads to the exons. The
task consists of finding a number of paths, together
with their expression levels, which optimally explain
the weights of the graph under various fitting
functions, such as least sum of squared residuals. In
(Tomescu et al. BMC Bioinformatics, 2013) we studied
this genome-guided multi-assembly problem when the
number of allowed solution paths was linear in the
number of arcs. In this paper, we further refine this
problem by asking for a bounded number $k$ of solution
paths, which is the setting of most practical interest.
We formulate this problem in very broad terms, and show
that for many choices of the fitting function it
becomes NP-hard. Nevertheless, we identify a natural
graph parameter of a DAG $G$ , which we call arc-width
and denote $ \langle G \rangle $ , and give a dynamic
programming algorithm running in time $ O(W^k \langle G
\rangle^k(\langle G \rangle + k)n)$ , where $n$ is the
number of vertices and $W$ is the maximum weight of
$G$. This implies that the problem is fixed-parameter
tractable (FPT) in the parameters $W$ , $ \langle G
\rangle $ $k$ . We also show that the arc-width of DAGs
constructed from simulated and real RNA-Seq reads is
small in practice. Finally, we study the
approximability of this problem, and, in particular,
give a fully polynomial-time approximation scheme
(FPTAS) for the case when the fitting function
penalizes the maximum ratio between the weights of the
arcs and their predicted coverage.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Seniya:2015:SSS,
author = "Chandrabhan Seniya and Ajay Yadav and G. J. Khan and
Nand K. Sah",
title = "In-silico Studies Show Potent Inhibition of {HIV-1}
Reverse Transcriptase Activity by a Herbal Drug",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1355--1364",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2415771",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Acquired immunodeficiency syndrome (AIDS) is a life
threatening disease of the human immune system caused
by human immunodeficiency virus (HIV). Effective
inhibition of reverse transcriptase activity is a
prominent, clinically viable approach for the treatment
of AIDS. Few non-nucleoside reverse transcriptase
inhibitors (NNRTIs) have been approved by the United
States Food and Drug Administration (US FDA) as drugs
for AIDS. In order to enhance therapeutic options
against AIDS we examined novel herbal compounds of
4-thiazolidinone and its derivatives that are known to
have remarkable antiviral potency. Our molecular
docking and simulation experiments have identified one
such herbal molecule known as
(5E)-3-(2-aminoethyl)-5-benzylidene-1,
3-thiazolidine-2,4-dione that may bind HIV-1RT with
high affinity to cause noncompetitive inhibition.
Results are also compared with other US FDA approved
drugs. Long de novo simulations and docking study
suggest that the ligand
(5E)-3-(2-aminoethyl)-5-benzylidene-1,
3-thiazolidine-2,4-dione (CID: 1656714) has strong
binding interactions with Asp113, Asp110, Asp185 and
Asp186 amino acids, all of which belong to one or the
other catalytic pockets of HIV-1RT. It is expected that
these interactions could be critical in the inhibitory
activity of the HIV-1RT. Therefore, this study provides
an evidence for consideration of
(5E)-3-(2-aminoethyl)-5-benzylidene-1,
3-thiazolidine-2,4-dione as a valuable natural molecule
in the treatment and prevention of HIV- associated
disorders.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rubiolo:2015:MGR,
author = "Mariano Rubiolo and Diego H. Milone and Georgina
Stegmayer",
title = "Mining Gene Regulatory Networks by Neural Modeling of
Expression Time-Series",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1365--1373",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2420551",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Discovering gene regulatory networks from data is one
of the most studied topics in recent years. Neural
networks can be successfully used to infer an
underlying gene network by modeling expression profiles
as times series. This work proposes a novel method
based on a pool of neural networks for obtaining a gene
regulatory network from a gene expression dataset. They
are used for modeling each possible interaction between
pairs of genes in the dataset, and a set of mining
rules is applied to accurately detect the subjacent
relations among genes. The results obtained on
artificial and real datasets confirm the method
effectiveness for discovering regulatory networks from
a proper modeling of the temporal dynamics of gene
expression profiles.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liao:2015:EFR,
author = "Bo Liao and Yan Jiang and Wei Liang and Lihong Peng
and Li Peng and Damien Hanyurwimfura and Zejun Li and
Min Chen",
title = "On Efficient Feature Ranking Methods for
High-Throughput Data Analysis",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1374--1384",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2415790",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Efficient mining of high-throughput data has become
one of the popular themes in the big data era. Existing
biology-related feature ranking methods mainly focus on
statistical and annotation information. In this study,
two efficient feature ranking methods are presented.
Multi-target regression and graph embedding are
incorporated in an optimization framework, and feature
ranking is achieved by introducing structured sparsity
norm. Unlike existing methods, the presented methods
have two advantages: (1) the feature subset
simultaneously account for global margin information as
well as locality manifold information. Consequently,
both global and locality information are considered.
(2) Features are selected by batch rather than
individually in the algorithm framework. Thus, the
interactions between features are considered and the
optimal feature subset can be guaranteed. In addition,
this study presents a theoretical justification.
Empirical experiments demonstrate the effectiveness and
efficiency of the two algorithms in comparison with
some state-of-the-art feature ranking methods through a
set of real-world gene expression data sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ma:2015:PPR,
author = "Xin Ma and Jing Guo and Ke Xiao and Xiao Sun",
title = "{PRBP}: Prediction of {RNA}-Binding Proteins Using a
Random Forest Algorithm Combined with an {RNA}-Binding
Residue Predictor",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1385--1393",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2418773",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The prediction of RNA-binding proteins is an
incredibly challenging problem in computational
biology. Although great progress has been made using
various machine learning approaches with numerous
features, the problem is still far from being solved.
In this study, we attempt to predict RNA-binding
proteins directly from amino acid sequences. A novel
approach, PRBP predicts RNA-binding proteins using the
information of predicted RNA-binding residues in
conjunction with a random forest based method. For a
given protein, we first predict its RNA-binding
residues and then judge whether the protein binds RNA
or not based on information from that prediction. If
the protein cannot be identified by the information
associated with its predicted RNA-binding residues,
then a novel random forest predictor is used to
determine if the query protein is a RNA-binding
protein. We incorporated features of evolutionary
information combined with physicochemical features
(EIPP) and amino acid composition feature to establish
the random forest predictor. Feature analysis showed
that EIPP contributed the most to the prediction of
RNA-binding proteins. The results also showed that the
information from the RNA-binding residue prediction
improved the overall performance of our RNA-binding
protein prediction. It is anticipated that the PRBP
method will become a useful tool for identifying
RNA-binding proteins. A PRBP Web server implementation
is freely available at
http://www.cbi.seu.edu.cn/PRBP/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sriwastava:2015:PPP,
author = "Brijesh K. Sriwastava and Subhadip Basu and Ujjwal
Maulik",
title = "Predicting Protein-Protein Interaction Sites with a
Novel Membership Based Fuzzy {SVM} Classifier",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1394--1404",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2401018",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Predicting residues that participate in
protein-protein interactions (PPI) helps to identify,
which amino acids are located at the interface. In this
paper, we show that the performance of the classical
support vector machine (SVM) algorithm can further be
improved with the use of a custom-designed fuzzy
membership function, for the partner-specific PPI
interface prediction problem. We evaluated the
performances of both classical SVM and fuzzy SVM
(F-SVM) on the PPI databases of three different model
proteomes of Homo sapiens, Escherichia coli and
Saccharomyces Cerevisiae and calculated the statistical
significance of the developed F-SVM over classical SVM
algorithm. We also compared our performance with the
available state-of-the-art fuzzy methods in this domain
and observed significant performance improvements. To
predict interaction sites in protein complexes, local
composition of amino acids together with their
physico-chemical characteristics are used, where the
F-SVM based prediction method exploits the membership
function for each pair of sequence fragments. The
average F-SVM performance (area under ROC curve) on the
test samples in 10-fold cross validation experiment are
measured as 77.07, 78.39, and 74.91 percent for the
aforementioned organisms respectively. Performances on
independent test sets are obtained as 72.09, 73.24 and
82.74 percent respectively. The software is available
for free download from
http://code.google.com/p/cmater-bioinfo.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ullah:2015:PUA,
author = "Ehsan Ullah and Mark Walker and Kyongbum Lee and Soha
Hassoun",
title = "{PreProPath}: an Uncertainty-Aware Algorithm for
Identifying Predictable Profitable Pathways in
Biochemical Networks",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1405--1415",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2394470",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Pathway analysis is a powerful approach to enable
rational design or redesign of biochemical networks for
optimizing metabolic engineering and synthetic biology
objectives such as production of desired chemicals or
biomolecules from specific nutrients. While
experimental methods can be quite successful,
computational approaches can enhance discovery and
guide experimentation by efficiently exploring very
large design spaces. We present a computational
algorithm, Predictably Profitable Path (PreProPath), to
identify target pathways best suited for engineering
modifications. The algorithm utilizes uncertainties
about the metabolic networks operating state inherent
in the underdetermined linear equations representing
the stoichiometric model. Flux Variability Analysis is
used to determine the operational flux range.
PreProPath identifies a path that is predictable in
behavior, exhibiting small flux ranges, and profitable,
containing the least restrictive flux-limiting reaction
in the network. The algorithm is computationally
efficient because it does not require enumeration of
pathways. The results of case studies show that
PreProPath can efficiently analyze variances in
metabolic states and model uncertainties to suggest
pathway engineering strategies that have been
previously supported by experimental data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wong:2015:PIM,
author = "Ka-Chun Wong and Chengbin Peng and Yue Li",
title = "Probabilistic Inference on Multiple Normalized Signal
Profiles from Next Generation Sequencing: Transcription
Factor Binding Sites",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1416--1428",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2424421",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "With the prevalence of chromatin immunoprecipitation
(ChIP) with sequencing (ChIP-Seq) technology, massive
ChIP-Seq data has been accumulated. The ChIP-Seq
technology measures the genome-wide occupancy of
DNA-binding proteins in vivo. It is well-known that
different DNA-binding protein occupancies may result in
a gene being regulated in different conditions (e.g.
different cell types). To fully understand a gene's
function, it is essential to develop probabilistic
models on multiple ChIP-Seq profiles for deciphering
the gene transcription causalities. In this work, we
propose and describe two probabilistic models. Assuming
the conditional independence of different DNA-binding
proteins' occupancies, the first method (SignalRanker)
is developed as an intuitive method for ChIP-Seq
genome-wide signal profile inference. Unfortunately,
such an assumption may not always hold in some gene
regulation cases. Thus, we propose and describe another
method (FullSignalRanker) which does not make the
conditional independence assumption. The proposed
methods are compared with other existing methods on
ENCODE ChIP-Seq datasets, demonstrating its regression
and classification ability. The results suggest that
FullSignalRanker is the best-performing method for
recovering the signal ranks on the promoter and
enhancer regions. In addition, FullSignalRanker is also
the best-performing method for peak sequence
classification. We envision that SignalRanker and
FullSignalRanker will become important in the era of
next generation sequencing. FullSignalRanker program is
available on the following website:
\url{http://www.cs.toronto.edu/~wkc/FullSignalRanker/}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Raveh:2015:RFM,
author = "Alon Raveh and Yoram Zarai and Michael Margaliot and
Tamir Tuller",
title = "Ribosome Flow Model on a Ring",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1429--1439",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2418782",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The asymmetric simple exclusion process (ASEP) is an
important model from statistical physics describing
particles that hop randomly from one site to the next
along an ordered lattice of sites, but only if the next
site is empty. ASEP has been used to model and analyze
numerous multiagent systems with local interactions
including the flow of ribosomes along the mRNA strand.
In ASEP with periodic boundary conditions a particle
that hops from the last site returns to the first one.
The mean field approximation of this model is referred
to as the ribosome flow model on a ring (RFMR). The
RFMR may be used to model both synthetic and endogenous
gene expression regimes. We analyze the RFMR using the
theory of monotone dynamical systems. We show that it
admits a continuum of equilibrium points and that every
trajectory converges to an equilibrium point.
Furthermore, we show that it entrains to periodic
transition rates between the sites. We describe the
implications of the analysis results to understanding
and engineering cyclic mRNA translation in-vitro and
in-vivo.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sehhati:2015:SGS,
author = "Mohammadreza Sehhati and Alireza Mehridehnavi and
Hossein Rabbani and Meraj Pourhossein",
title = "Stable Gene Signature Selection for Prediction of
Breast Cancer Recurrence Using Joint Mutual
Information",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1440--1448",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2407407",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this experiment, a gene selection technique was
proposed to select a robust gene signature from
microarray data for prediction of breast cancer
recurrence. In this regard, a hybrid scoring criterion
was designed as linear combinations of the scores that
were determined in the mutual information (MI) domain
and protein-protein interactions network. Whereas, the
MI-based score represents the complementary information
between the selected genes for outcome prediction; and
the number of connections in the PPI network between
the selected genes builds the PPI-based score. All
genes were scored by using the proposed function in a
hybrid forward-backward gene-set selection process to
select the optimum biomarker-set from the gene
expression microarray data. The accuracy and stability
of the finally selected biomarkers were evaluated by
using five-fold cross-validation (CV) to classify
available data on breast cancer patients into two
cohorts of poor and good prognosis. The results showed
an appealing improvement in the cross-dataset accuracy
in comparison with similar studies whenever we applied
a primary signature, which was selected from one
dataset, to predict survival in other independent
datasets. Moreover, the proposed method demonstrated
58-92 percent overlap between 50-genes signatures,
which were selected from seven independent datasets
individually.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2015:SAS,
author = "Hao Zhang and Xingyuan Wang and Xiaohui Lin",
title = "Synchronization of Asynchronous Switched {Boolean}
Network",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1449--1456",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2404802",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, the complete synchronizations for
asynchronous switched Boolean network with free Boolean
sequence controllers and close-loop controllers are
studied. First, the basic asynchronous switched Boolean
network model is provided. With the method of
semi-tensor product, the Boolean dynamics is translated
into linear representation. Second, necessary and
sufficient conditions for ASBN synchronization with
free Boolean sequence control and close-loop control
are derived, respectively. Third, some illustrative
examples are provided to show the efficiency of the
proposed methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sengupta:2015:CAU,
author = "T. Sengupta and M. Bhushan and P. P. Wangikar",
title = "A Computational Approach Using Ratio Statistics for
Identifying Housekeeping Genes from {cDNA} Microarray
Data",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1457--1463",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2407399",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We predict housekeeping genes from replicate
microarray gene expression data of human lymphoblastoid
cells and liver tissue with outliers removed using a
scoring scheme, by an algorithm based on statistical
hypothesis testing, assuming that such genes are
constitutively expressed. A few predicted genes were
examined and found to be housekeeping.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pradeep:2015:NSB,
author = "Prachi Pradeep and Craig Struble and Terrence Neumann
and Daniel S. Sem and Stephen J. Merrill",
title = "A Novel Scoring Based Distributed Protein Docking
Application to Improve Enrichment",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1464--1469",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2401020",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Molecular docking is a computational technique which
predicts the binding energy and the preferred binding
mode of a ligand to a protein target. Virtual screening
is a tool which uses docking to investigate large
chemical libraries to identify ligands that bind
favorably to a protein target. We have developed a
novel scoring based distributed protein docking
application to improve enrichment in virtual screening.
The application addresses the issue of time and cost of
screening in contrast to conventional systematic
parallel virtual screening methods in two ways.
Firstly, it automates the process of creating and
launching multiple independent dockings on a high
performance computing cluster. Secondly, it uses a
Na{\"\i}ve Bayes scoring function to calculate binding
energy of un-docked ligands to identify and
preferentially dock (Autodock predicted) better
binders. The application was tested on four proteins
using a library of 10,573 ligands. In all the
experiments, (i). 200 of the 1,000 best binders are
identified after docking only $ \sim 14 $ percent of
the chemical library, (ii). 9 or 10 best-binders are
identified after docking only $ \sim 19 $ percent of
the chemical library, and (iii). no significant
enrichment is observed after docking $ \sim 70 $
percent of the chemical library. The results show
significant increase in enrichment of potential drug
leads in early rounds of virtual screening.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dellen:2015:GSR,
author = "Babette Dellen and Hanno Scharr and Carme Torras",
title = "Growth Signatures of Rosette Plants from Time-Lapse
Video",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1470--1478",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2404810",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Plant growth is a dynamic process, and the precise
course of events during early plant development is of
major interest for plant research. In this work, we
investigate the growth of rosette plants by processing
time-lapse videos of growing plants, where we use
Nicotiana tabacum (tobacco) as a model plant. In each
frame of the video sequences, potential leaves are
detected using a leaf-shape model. These detections are
prone to errors due to the complex shape of plants and
their changing appearance in the image, depending on
leaf movement, leaf growth, and illumination
conditions. To cope with this problem, we employ a
novel graph-based tracking algorithm which can bridge
gaps in the sequence by linking leaf detections across
a range of neighboring frames. We use the overlap of
fitted leaf models as a pairwise similarity measure,
and forbid graph edges that would link leaf detections
within a single frame. We tested the method on a set of
tobacco-plant growth sequences, and could track the
first leaves of the plant, including partially or
temporarily occluded ones, along complete sequences,
demonstrating the applicability of the method to
automatic plant growth analysis. All seedlings
displayed approximately the same growth behavior, and a
characteristic growth signature was found.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wigren:2015:MOI,
author = "Torbjorn Wigren",
title = "Model Order and Identifiability of Non-Linear
Biological Systems in Stable Oscillation",
journal = j-TCBB,
volume = "12",
number = "6",
pages = "1479--1484",
month = nov,
year = "2015",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2404799",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 15 05:57:23 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The paper presents a theoretical result that clarifies
when it is at all possible to determine the nonlinear
dynamic equations of a biological system in stable
oscillation, from measured data. As it turns out the
minimal order needed for this is dependent on the
minimal dimension in which the stable orbit of the
system does not intersect itself. This is illustrated
with a simulated fourth order Hodgkin--Huxley spiking
neuron model, which is identified using a non-linear
second order differential equation model. The simulated
result illustrates that the underlying higher order
model of the spiking neuron cannot be uniquely
determined given only the periodic measured data. The
result of the paper is of general validity when the
dynamics of biological systems in stable oscillation is
identified, and illustrates the need to carefully
address non-linear identifiability aspects when
validating models based on periodic data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huang:2016:GES,
author = "De-Shuang Huang and Vitoantonio Bevilacqua and M.
Michael Gromiha",
title = "Guest Editorial for Special Section on the {10th
International Conference on Intelligent Computing
(ICIC)}",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "1--3",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2491058",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Han:2016:GII,
author = "Kyungsook Han and Jeonghoon Lee",
title = "{GeneNetFinder2}: Improved Inference of Dynamic Gene
Regulatory Relations with Multiple Regulators",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "4--11",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2450728",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A gene involved in complex regulatory interactions may
have multiple regulators since gene expression in such
interactions is often controlled by more than one gene.
Another thing that makes gene regulatory interactions
complicated is that regulatory interactions are not
static, but change over time during the cell cycle.
Most research so far has focused on identifying gene
regulatory relations between individual genes in a
particular stage of the cell cycle. In this study we
developed a method for identifying dynamic gene
regulations of several types from the time-series gene
expression data. The method can find gene regulations
with multiple regulators that work in combination or
individually as well as those with single regulators.
The method has been implemented as the second version
of GeneNetFinder (hereafter called GeneNetFinder2) and
tested on several gene expression datasets.
Experimental results with gene expression data revealed
the existence of genes that are not regulated by
individual genes but rather by a combination of several
genes. Such gene regulatory relations cannot be found
by conventional methods. Our method finds such
regulatory relations as well as those with multiple,
independent regulators or single regulators, and
represents gene regulatory relations as a dynamic
network in which different gene regulatory relations
are shown in different stages of the cell cycle.
GeneNetFinder2 is available at
http://bclab.inha.ac.kr/GeneNetFinder and will be
useful for modeling dynamic gene regulations with
multiple regulators.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bonilla-Huerta:2016:HFU,
author = "Edmundo Bonilla-Huerta and Alberto Hernandez-Montiel
and Roberto-Morales Caporal and Marco Arjona Lopez",
title = "Hybrid Framework Using Multiple-Filters and an
Embedded Approach for an Efficient Selection and
Classification of Microarray Data",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "12--26",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2474384",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A hybrid framework composed of two stages for gene
selection and classification of DNA microarray data is
proposed. At the first stage, five traditional
statistical methods are combined for preliminary gene
selection (Multiple Fusion Filter). Then, different
relevant gene subsets are selected by using an embedded
Genetic Algorithm (GA), Tabu Search (TS), and Support
Vector Machine (SVM). A gene subset, consisting of the
most relevant genes, is obtained from this process, by
analyzing the frequency of each gene in the different
gene subsets. Finally, the most frequent genes are
evaluated by the embedded approach to obtain a final
relevant small gene subset with high performance. The
proposed method is tested in four DNA microarray
datasets. From simulation study, it is observed that
the proposed approach works better than other methods
reported in the literature.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Deng:2016:PHG,
author = "Su-Ping Deng and Lin Zhu and De-Shuang Huang",
title = "Predicting Hub Genes Associated with Cervical Cancer
through Gene Co-Expression Networks",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "27--35",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2476790",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Cervical cancer is the third most common malignancy in
women worldwide. It remains a leading cause of
cancer-related death for women in developing countries.
In order to contribute to the treatment of the cervical
cancer, in our work, we try to find a few key genes
resulting in the cervical cancer. Employing functions
of several bioinformatics tools, we selected 143
differentially expressed genes (DEGs) associated with
the cervical cancer. The results of bioinformatics
analysis show that these DEGs play important roles in
the development of cervical cancer. Through comparing
two differential co-expression networks (DCNs) at two
different states, we found a common sub-network and two
differential sub-networks as well as some hub genes in
three sub-networks. Moreover, some of the hub genes
have been reported to be related to the cervical
cancer. Those hub genes were analyzed from Gene
Ontology function enrichment, pathway enrichment and
protein binding three aspects. The results can help us
understand the development of the cervical cancer and
guide further experiments about the cervical cancer.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Qu:2016:PSL,
author = "Xumi Qu and Dong Wang and Yuehui Chen and Shanping
Qiao and Qing Zhao",
title = "Predicting the Subcellular Localization of Proteins
with Multiple Sites Based on Multiple Features Fusion",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "36--42",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2485207",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein sub-cellular localization prediction has
attracted much attention in recent years because of its
importance for protein function studying and targeted
drug discovery, and that makes it to be an important
research field in bioinformatics. Traditional
experimental methods which ascertain the protein
sub-cellular locations are costly and time consuming.
In the last two decades, machine learning methods got
increasing development, and a large number of machine
learning based protein sub-cellular location predictors
have been developed. However, most of such predictors
can only predict proteins in only one subcellular
location. With the development of biology techniques,
more and more proteins which have two or even more
sub-cellular locations have been found. It is much more
significant to study such proteins because they have
extremely useful implication for both basic biology and
bioinformatics research. In order to improve the
accuracy of prediction, much more feature information
which can represent the protein sequence should be
extracted. In this paper, several feature extraction
methods were fused together to extract the feature
information, then the multi-label k nearest neighbors
(ML-KNN) algorithm was used to predict protein
sub-cellular locations. The best overall accuracies we
got for dataset s1 in constructing Gpos-mploc is
66.7304 and 59.9206 percent for dataset s2 in
constructing Virus-mPLoc.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hsieh:2016:FCM,
author = "Sun-Yuan Hsieh and Yu-Chun Chou",
title = "A Faster {cDNA} Microarray Gene Expression Data
Classifier for Diagnosing Diseases",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "43--54",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2474389",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Profiling cancer molecules has several advantages;
however, using microarray technology in routine
clinical diagnostics is challenging for physicians. The
classification of microarray data has two main
limitations: (1) the data set is unreliable for
building classifiers; and (2) the classifiers exhibit
poor performance. Current microarray classification
algorithms typically yield a high rate of
false-positives cases, which is unacceptable in
diagnostic applications. Numerous algorithms have been
developed to detect false-positive cases; however, they
require a considerable computation time. To address
this problem, this study enhanced a previously proposed
gene expression graph (GEG)-based classifier to shorten
the computation time. The modified classifier filters
genes by using an edge weight to determine their
significance, thereby facilitating accurate comparison
and classification. This study experimentally compared
the proposed classifier with a GEG-based classifier by
using real data and benchmark tests. The results show
that the proposed classifier is faster at detecting
false-positives.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhu:2016:CPE,
author = "Lin Zhu and Wei-Li Guo and Su-Ping Deng and De-Shuang
Huang",
title = "{ChIP--PIT}: Enhancing the Analysis of {ChIP-Seq} Data
Using Convex-Relaxed Pair-Wise Interaction Tensor
Decomposition",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "55--63",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2465893",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In recent years, thanks to the efforts of individual
scientists and research consortiums, a huge amount of
chromatin immunoprecipitation followed by
high-throughput sequencing (ChIP-seq) experimental data
have been accumulated. Instead of investigating them
independently, several recent studies have convincingly
demonstrated that a wealth of scientific insights can
be gained by integrative analysis of these ChIP-seq
data. However, when used for the purpose of integrative
analysis, a serious drawback of current ChIP-seq
technique is that it is still expensive and
time-consuming to generate ChIP-seq datasets of high
standard. Most researchers are therefore unable to
obtain complete ChIP-seq data for several TFs in a wide
variety of cell lines, which considerably limits the
understanding of transcriptional regulation pattern. In
this paper, we propose a novel method called ChIP-PIT
to overcome the aforementioned limitation. In ChIP-PIT,
ChIP-seq data corresponding to a diverse collection of
cell types, TFs and genes are fused together using the
three-mode pair-wise interaction tensor (PIT) model,
and the prediction of unperformed ChIP-seq experimental
results is formulated as a tensor completion problem.
Computationally, we propose efficient first-order
method based on extensions of coordinate descent method
to learn the optimal solution of ChIP-PIT, which makes
it particularly suitable for the analysis of massive
scale ChIP-seq data. Experimental evaluation the ENCODE
data illustrate the usefulness of the proposed model.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hsiao:2016:PGI,
author = "Yu-Ting Hsiao and Wei-Po Lee and Wei Yang and Stefan
Muller and Christoph Flamm and Ivo Hofacker and Philipp
Kugler",
title = "Practical Guidelines for Incorporating Knowledge-Based
and Data-Driven Strategies into the Inference of Gene
Regulatory Networks",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "64--75",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2465954",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Modeling gene regulatory networks (GRNs) is essential
for conceptualizing how genes are expressed and how
they influence each other. Typically, a reverse
engineering approach is employed; this strategy is
effective in reproducing possible fitting models of
GRNs. To use this strategy, however, two daunting tasks
must be undertaken: one task is to optimize the
accuracy of inferred network behaviors; and the other
task is to designate valid biological topologies for
target networks. Although existing studies have
addressed these two tasks for years, few of the studies
can satisfy both of the requirements simultaneously. To
address these difficulties, we propose an integrative
modeling framework that combines knowledge-based and
data-driven input sources to construct biological
topologies with their corresponding network behaviors.
To validate the proposed approach, a real dataset
collected from the cell cycle of the yeast S.
cerevisiae is used. The results show that the proposed
framework can successfully infer solutions that meet
the requirements of both the network behaviors and
biological structures. Therefore, the outcomes are
exploitable for future in vivo experimental design.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fang:2016:IGS,
author = "Yi Fang and Mengtian Sun and Guoxian Dai and Karthik
Ramain",
title = "The Intrinsic Geometric Structure of Protein-Protein
Interaction Networks for Protein Interaction
Prediction",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "76--85",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2456876",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recent developments in high-throughput technologies
for measuring protein-protein interaction (PPI) have
profoundly advanced our ability to systematically infer
protein function and regulation. However, inherently
high false positive and false negative rates in
measurement have posed great challenges in
computational approaches for the prediction of PPI. A
good PPI predictor should be (1) resistant to high rate
of missing and spurious PPIs, and (2) robust against
incompleteness of observed PPI networks. To predict PPI
in a network, we developed an intrinsic geometry
structure (IGS) for network, which exploits the
intrinsic and hidden relationship among proteins in
network through a heat diffusion process. In this
process, all explicit PPIs participate simultaneously
to glue local infinitesimal and noisy experimental
interaction data to generate a global macroscopic
descriptions about relationships among proteins. The
revealed implicit relationship can be interpreted as
the probability of two proteins interacting with each
other. The revealed relationship is intrinsic and
robust against individual, local and explicit protein
interactions in the original network. We apply our
approach to publicly available PPI network data for the
evaluation of the performance of PPI prediction.
Experimental results indicate that, under different
levels of the missing and spurious PPIs, IGS is able to
robustly exploit the intrinsic and hidden relationship
for PPI prediction with a higher sensitivity and
specificity compared to that of recently proposed
methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cheng:2016:NTL,
author = "Yu-Huei Cheng",
title = "A Novel Teaching-Learning-Based Optimization for
Improved Mutagenic Primer Design in Mismatch {PCR-RFLP
SNP} Genotyping",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "86--98",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2430354",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Many single nucleotide polymorphisms (SNPs) for
complex genetic diseases are genotyped by polymerase
chain reaction-restriction fragment length polymorphism
(PCR-RFLP) in small-scale basic research studies. It is
an essential work to design feasible PCR-RFLP primer
pair and find out available restriction enzymes to
recognize the target SNP for PCR experiments. However,
many SNPs are incapable of performing PCR-RFLP makes
SNP genotyping become unpractical. A genetic algorithm
(GA) had been proposed for designing mutagenic primer
and get available restriction enzymes, but it gives an
unrefined solution in mutagenic primers. In order to
improve the mutagenic primer design, we propose TLBOMPD
(TLBO-based Mutagenic Primer Design) a novel
computational intelligence-based method that uses the
notion of ``teaching and learning'' to search for more
feasible mutagenic primers and provide the latest
available restriction enzymes. The original Wallace's
formula for the calculation of melting temperature is
maintained, and more accurate calculation formulas of
GC-based melting temperature and thermodynamic melting
temperature are introduced into the proposed method.
Mutagenic matrix is also reserved to increase the
efficiency of judging a hypothetical mutagenic primer
if involve available restriction enzymes for
recognizing the target SNP. Furthermore, the core of
SNP-RFLPing version 2 is used to enhance the mining
work for restriction enzymes based on the latest
REBASE. Twenty-five SNPs with mismatch PCR-RFLP
screened from 288 SNPs in human SLC6A4 gene are used to
appraise the TLBOMPD. Also, the computational results
are compared with those of the GAMPD. In the future,
the usage of the mutagenic primers in the wet lab needs
to been validated carefully to increase the reliability
of the method. The TLBOMPD is implemented in JAVA and
it is freely available at
http://tlbompd.googlecode.com/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Roy:2016:DMB,
author = "Indranil Roy and Srinivas Aluru",
title = "Discovering Motifs in Biological Sequences Using the
{Micron} Automata Processor",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "99--111",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2430313",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Finding approximately conserved sequences, called
motifs, across multiple DNA or protein sequences is an
important problem in computational biology. In this
paper, we consider the $ (l, d) $ motif search problem
of identifying one or more motifs of length $l$ present
in at least $q$ of the $n$ given sequences, with each
occurrence differing from the motif in at most $d$
substitutions. The problem is known to be NP-complete,
and the largest solved instance reported to date is $
(26, 11)$ . We propose a novel algorithm for the $ (l,
d)$ motif search problem using streaming execution over
a large set of non-deterministic finite automata (NFA).
This solution is designed to take advantage of the
micron automata processor, a new technology close to
deployment that can simultaneously execute multiple NFA
in parallel. We demonstrate the capability for solving
much larger instances of the $ (l, d)$ motif search
problem using the resources available within a single
automata processor board, by estimating run-times for
problem instances $ (39, 18)$ and $ (40, 17)$ . The
paper serves as a useful guide to solving problems
using this new accelerator technology.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bahlouli:2016:FBP,
author = "S. Bahlouli and A. Mokaddem and F. Hamdache and H.
Riane and M. Kameche",
title = "Fractal Behavior of the Pancreatic $ \beta $-Cell Near
the Percolation Threshold: Effect of the {K$_{\rm
ATP}$} Channel On the Electrical Response",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "112--121",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2415797",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The molecular system built with true chemical bonds or
strong molecular interaction can be described using
conceptual mathematical tools. Modeling of the natural
generated ionic currents on the human pancreatic $
\beta $ -cell activity had been already studied using
complicated analytical models. In our present
contribution, we prove the same using our simple
electrical model. The ionic currents are associated
with different proteins membrane channels (K-Ca, K$_v$,
K$_{ATP}$, Ca$_v$ -L) and Na/Ca Exchanger (NCX). The
proteins are Ohmic conductors and are modeled by
conductance randomly distributed. Switches are placed
in series with conductances in order to highlight the
channel activity. However, the K$_{ATP}$ channel
activity is stimulated by glucose, and the NCX's
conductance change according to the intracellular
calcium concentration. The percolation threshold of the
system is calculated by the fractal nature of the
infinite cluster using the Tarjan's depth-first-search
algorithm. It is shown that the behavior of the
internal concentration of Ca$^{2+}$ and the membrane
potential are modulated by glucose. The results confirm
that the inhibition of K$_{ATP}$ channels depolarizes
the membrane and increases the influx of
[Ca$^{2+}$]$_i$ through NCX and Ca$_v$ -L channel for
high glucose concentrations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ullah:2016:GAC,
author = "Ehsan Ullah and Shuchin Aeron and Soha Hassoun",
title = "{gEFM}: an Algorithm for Computing Elementary Flux
Modes Using Graph Traversal",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "122--134",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2430344",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Computational methods to engineer cellular metabolism
promise to play a critical role in producing
pharmaceutical, repairing defective genes, destroying
cancer cells, and generating biofuels. Elementary Flux
Mode (EFM) analysis is one such powerful technique that
has elucidated cell growth and regulation, predicted
product yield, and analyzed network robustness. EFM
analysis, however, is a computationally daunting task
because it requires the enumeration of all independent
and stoichiometrically balanced pathways within a
cellular network. We present in this paper an EFM
enumeration algorithm, termed graphical EFM or gEFM.
The algorithm is based on graph traversal, an approach
previously assumed unsuitable for enumerating EFMs. The
approach is derived from a pathway synthesis method
proposed by Mavrovouniotis et al. The algorithm is
described and proved correct. We apply gEFM to several
networks and report runtimes in comparison with other
EFM computation tools. We show how gEFM benefits from
network compression. Like other EFM computational
techniques, gEFM is sensitive to constraint ordering;
however, we are able to demonstrate that knowledge of
the underlying network structure leads to better
constraint ordering. gEFM is shown to be competitive
with state-of-the-art EFM computational techniques for
several networks, but less so for networks with a
larger number of EFMs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2016:GAS,
author = "Xian Zhang and Ligang Wu and Jiahua Zou",
title = "Globally Asymptotic Stability Analysis for Genetic
Regulatory Networks with Mixed Delays: an
{$M$}-Matrix-Based Approach",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "135--147",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2424432",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper deals with the problem of globally
asymptotic stability for nonnegative equilibrium points
of genetic regulatory networks (GRNs) with mixed delays
(i.e., time-varying discrete delays and constant
distributed delays). Up to now, all existing stability
criteria for equilibrium points of the kind of
considered GRNs are in the form of the linear matrix
inequalities (LMIs). In this paper, the Brouwer's fixed
point theorem is employed to obtain sufficient
conditions such that the kind of GRNs under
consideration here has at least one nonnegative
equilibrium point. Then, by using the nonsingular
M-matrix theory and the functional differential
equation theory, M-matrix-based sufficient conditions
are proposed to guarantee that the kind of GRNs under
consideration here has a unique nonnegative equilibrium
point which is globally asymptotically stable. The
M-matrix-based sufficient conditions derived here are
to check whether a constant matrix is a nonsingular
M-matrix, which can be easily verified, as there are
many equivalent statements on the nonsingular
M-matrices. So, in terms of computational complexity,
the M-matrix-based stability criteria established in
this paper are superior to the LMI-based ones in
literature. To illustrate the effectiveness of the
approach proposed in this paper, several numerical
examples and their simulations are given.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ranganarayanan:2016:IGB,
author = "Preethi Ranganarayanan and Narmadha Thanigesan and
Vivek Ananth and Valadi K. Jayaraman and Vigneshwar
Ramakrishnan",
title = "Identification of Glucose-Binding Pockets in Human
Serum Albumin Using Support Vector Machine and
Molecular Dynamics Simulations",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "148--157",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2415806",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Human Serum Albumin (HSA) has been suggested to be an
alternate biomarker to the existing Hemoglobin-A1c
(HbA1c) marker for glycemic monitoring. Development and
usage of HSA as an alternate biomarker requires the
identification of glycation sites, or equivalently,
glucose-binding pockets. In this work, we combine
molecular dynamics simulations of HSA and the
state-of-art machine learning method Support Vector
Machine (SVM) to predict glucose-binding pockets in
HSA. SVM uses the three dimensional arrangement of
atoms and their chemical properties to predict
glucose-binding ability of a pocket. Feature selection
reveals that the arrangement of atoms and their
chemical properties within the first 4{\AA} from the
centroid of the pocket play an important role in the
binding of glucose. With a 10-fold cross validation
accuracy of 84 percent, our SVM model reveals seven new
potential glucose-binding sites in HSA of which two are
exposed only during the dynamics of HSA. The
predictions are further corroborated using docking
studies. These findings can complement studies directed
towards the development of HSA as an alternate
biomarker for glycemic monitoring.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rivera-Borroto:2016:RAM,
author = "Oscar Miguel Rivera-Borroto and Jose Manuel Garcia-de
la Vega and Yovani Marrero-Ponce and Ricardo Grau",
title = "Relational Agreement Measures for Similarity Searching
of Cheminformatic Data Sets",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "158--167",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2424435",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Research on similarity searching of cheminformatic
data sets has been focused on similarity measures using
fingerprints. However, nominal scales are the least
informative of all metric scales, increasing the tied
similarity scores, and decreasing the effectivity of
the retrieval engines. Tanimoto's coefficient has been
claimed to be the most prominent measure for this task.
Nevertheless, this field is far from being exhausted
since the computer science no free lunch theorem
predicts that ``no similarity measure has overall
superiority over the population of data sets''. We
introduce 12 relational agreement (RA) coefficients for
seven metric scales, which are integrated within a
group fusion-based similarity searching algorithm.
These similarity measures are compared to a reference
panel of 21 proximity quantifiers over 17 benchmark
data sets (MUV), by using informative descriptors, a
feature selection stage, a suitable performance metric,
and powerful comparison tests. In this stage, RA
coefficients perform favourably with respect to the
state-of-the-art proximity measures. Afterward, the
RA-based method outperform another four nearest
neighbor searching algorithms over the same data
domains. In a third validation stage, RA measures are
successfully applied to the virtual screening of the
NCI data set. Finally, we discuss a possible molecular
interpretation for these similarity variants.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anonymous:2016:RL,
author = "Anonymous",
title = "2015 reviewers list",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "168--171",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2522778",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The publication offers a note of thanks and lists its
reviewers.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Title:2016:IIA,
author = "Title",
title = "2015 Index {IEEE\slash ACM Transactions on
Computational Biology and Bioinformatics} Vol. 12",
journal = j-TCBB,
volume = "13",
number = "1",
pages = "172--195",
month = jan,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2523430",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:42 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This index covers all technical items --- papers,
correspondence, reviews, etc. --- that appeared in this
periodical during the year, and items from previous
years that were commented upon or corrected in this
year. Departments and other items may also be covered
if they have been judged to have archival value. The
Author Index contains the primary entry for each item,
listed under the first author's name. The primary entry
includes the co-authors' names, the title of the paper
or other item, and its location, specified by the
publication abbreviation, year, month, and inclusive
pagination. The Subject Index contains entries
describing the item under all appropriate subject
headings, plus the first author's name, the publication
abbreviation, month, and year, and inclusive pages.
Note that the item title is found only under the
primary entry in the Author Index.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Guzzi:2016:GES,
author = "Pietro H. Guzzi and Marco Mina",
title = "Guest Editorial for Special Section on Semantic-Based
Approaches for Analysis of Biological Data",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "196--196",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2535578",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Agapito:2016:ECO,
author = "Giuseppe Agapito and Marianna Milano and Pietro Hiram
Guzzi and Mario Cannataro",
title = "Extracting Cross-Ontology Weighted Association Rules
from Gene Ontology Annotations",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "197--208",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2462348",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gene Ontology (GO) is a structured repository of
concepts (GO Terms) that are associated to one or more
gene products through a process referred to as
annotation. The analysis of annotated data is an
important opportunity for bioinformatics. There are
different approaches of analysis, among those, the use
of association rules (AR) which provides useful
knowledge, discovering biologically relevant
associations between terms of GO, not previously known.
In a previous work, we introduced GO-WAR (Gene
Ontology-based Weighted Association Rules), a
methodology for extracting weighted association rules
from ontology-based annotated datasets. We here adapt
the GO-WAR algorithm to mine cross-ontology association
rules, i.e., rules that involve GO terms present in the
three sub-ontologies of GO. We conduct a deep
performance evaluation of GO-WAR by mining publicly
available GO annotated datasets, showing how GO-WAR
outperforms current state of the art approaches.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Masseroli:2016:IQG,
author = "Marco Masseroli and Arif Canakoglu and Stefano Ceri",
title = "Integration and Querying of Genomic and Proteomic
Semantic Annotations for Biomedical Knowledge
Extraction",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "209--219",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2453944",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Understanding complex biological phenomena involves
answering complex biomedical questions on multiple
biomolecular information simultaneously, which are
expressed through multiple genomic and proteomic
semantic annotations scattered in many distributed and
heterogeneous data sources; such heterogeneity and
dispersion hamper the biologists' ability of asking
global queries and performing global evaluations. To
overcome this problem, we developed a software
architecture to create and maintain a Genomic and
Proteomic Knowledge Base (GPKB), which integrates
several of the most relevant sources of such dispersed
information (including Entrez Gene, UniProt, IntAct,
Expasy Enzyme, GO, GOA, BioCyc, KEGG, Reactome, and
OMIM). Our solution is general, as it uses a flexible,
modular, and multilevel global data schema based on
abstraction and generalization of integrated data
features, and a set of automatic procedures for easing
data integration and maintenance, also when the
integrated data sources evolve in data content,
structure, and number. These procedures also assure
consistency, quality, and provenance tracking of all
integrated data, and perform the semantic closure of
the hierarchical relationships of the integrated
biomedical ontologies. At
http://www.bioinformatics.deib.polimi.it/GPKB/, a Web
interface allows graphical easy composition of queries,
although complex, on the knowledge base, supporting
also semantic query expansion and comprehensive
explorative search of the integrated data to better
sustain biomedical knowledge extraction.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2016:PPF,
author = "Guoxian Yu and Guangyuan Fu and Jun Wang and Hailong
Zhu",
title = "Predicting Protein Function via Semantic Integration
of Multiple Networks",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "220--232",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2459713",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Determining the biological functions of proteins is
one of the key challenges in the post-genomic era. The
rapidly accumulated large volumes of proteomic and
genomic data drives to develop computational models for
automatically predicting protein function in large
scale. Recent approaches focus on integrating multiple
heterogeneous data sources and they often get better
results than methods that use single data source alone.
In this paper, we investigate how to integrate multiple
biological data sources with the biological knowledge,
i.e., Gene Ontology (GO), for protein function
prediction. We propose a method, called SimNet, to
Semantically integrate multiple functional association
Networks derived from heterogeneous data sources.
SimNet firstly utilizes GO annotations of proteins to
capture the semantic similarity between proteins and
introduces a semantic kernel based on the similarity.
Next, SimNet constructs a composite network, obtained
as a weighted summation of individual networks, and
aligns the network with the kernel to get the weights
assigned to individual networks. Then, it applies a
network-based classifier on the composite network to
predict protein function. Experiment results on
heterogeneous proteomic data sources of Yeast, Human,
Mouse, and Fly show that, SimNet not only achieves
better (or comparable) results than other related
competitive approaches, but also takes much less time.
The Matlab codes of SimNet are available at
https://sites.google.com/site/guoxian85/simnet.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fernandez:2016:OBS,
author = "Javier D. Fernandez and Maurizio Lenzerini and Marco
Masseroli and Francesco Venco and Stefano Ceri",
title = "Ontology-Based Search of Genomic Metadata",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "233--247",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2495179",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The Encyclopedia of DNA Elements (ENCODE) is a huge
and still expanding public repository of more than
4,000 experiments and 25,000 data files, assembled by a
large international consortium since 2007; unknown
biological knowledge can be extracted from these huge
and largely unexplored data, leading to data-driven
genomic, transcriptomic, and epigenomic discoveries.
Yet, search of relevant datasets for knowledge
discovery is limitedly supported: metadata describing
ENCODE datasets are quite simple and incomplete, and
not described by a coherent underlying ontology. Here,
we show how to overcome this limitation, by adopting an
ENCODE metadata searching approach which uses
high-quality ontological knowledge and state-of-the-art
indexing technologies. Specifically, we developed
S.O.S. GeM
(http://www.bioinformatics.deib.polimi.it/SOSGeM/), a
system supporting effective semantic search and
retrieval of ENCODE datasets. First, we constructed a
Semantic Knowledge Base by starting with concepts
extracted from ENCODE metadata, matched to and expanded
on biomedical ontologies integrated in the
well-established Unified Medical Language System. We
prove that this inference method is sound and complete.
Then, we leveraged the Semantic Knowledge Base to
semantically search ENCODE data from arbitrary
biologists' queries. This allows correctly finding more
datasets than those extracted by a purely syntactic
search, as supported by the other available systems. We
empirically show the relevance of found datasets to the
biologists' queries.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chicco:2016:OBP,
author = "Davide Chicco and Marco Masseroli",
title = "Ontology-Based Prediction and Prioritization of Gene
Functional Annotations",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "248--260",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2459694",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genes and their protein products are essential
molecular units of a living organism. The knowledge of
their functions is key for the understanding of
physiological and pathological biological processes, as
well as in the development of new drugs and therapies.
The association of a gene or protein with its
functions, described by controlled terms of
biomolecular terminologies or ontologies, is named gene
functional annotation. Very many and valuable gene
annotations expressed through terminologies and
ontologies are available. Nevertheless, they might
include some erroneous information, since only a subset
of annotations are reviewed by curators. Furthermore,
they are incomplete by definition, given the rapidly
evolving pace of biomolecular knowledge. In this
scenario, computational methods that are able to
quicken the annotation curation process and reliably
suggest new annotations are very important. Here, we
first propose a computational pipeline that uses
different semantic and machine learning methods to
predict novel ontology-based gene functional
annotations; then, we introduce a new semantic
prioritization rule to categorize the predicted
annotations by their likelihood of being correct. Our
tests and validations proved the effectiveness of our
pipeline and prioritization of predicted annotations,
by selecting as most likely manifold predicted
annotations that were later confirmed.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wong:2016:CSD,
author = "Ka-Chun Wong and Yue Li and Chengbin Peng and Hau-San
Wong",
title = "A Comparison Study for {DNA} Motif Modeling on Protein
Binding Microarray",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "261--271",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2443782",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Transcription factor binding sites (TFBSs) are
relatively short (5-15 bp) and degenerate. Identifying
them is a computationally challenging task. In
particular, protein binding microarray (PBM) is a
high-throughput platform that can measure the DNA
binding preference of a protein in a comprehensive and
unbiased manner; for instance, a typical PBM experiment
can measure binding signal intensities of a protein to
all possible DNA k-mers ( $ k = 8 \sim $ 10). Since
proteins can often bind to DNA with different binding
intensities, one of the major challenges is to build
TFBS (also known as DNA motif) models which can fully
capture the quantitative binding affinity data. To
learn DNA motif models from the non-convex objective
function landscape, several optimization methods are
compared and applied to the PBM motif model building
problem. In particular, representative methods from
different optimization paradigms have been chosen for
modeling performance comparison on hundreds of PBM
datasets. The results suggest that the multimodal
optimization methods are very effective for capturing
the binding preference information from PBM data. In
particular, we observe a general performance
improvement if choosing di-nucleotide modeling over
mono-nucleotide modeling. In addition, the models
learned by the best-performing method are applied to
two independent applications: PBM probe rotation
testing and ChIP-Seq peak sequence prediction,
demonstrating its biological applicability.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nguyen:2016:LDA,
author = "An Nguyen and Adam Prugel-Bennett and Srinandan
Dasmahapatra",
title = "A Low Dimensional Approximation For Competence In
\bioname{Bacillus Subtilis}",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "272--280",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2440275",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The behaviour of a high dimensional stochastic system
described by a chemical master equation (CME) depends
on many parameters, rendering explicit simulation an
inefficient method for exploring the properties of such
models. Capturing their behaviour by low-dimensional
models makes analysis of system behaviour tractable. In
this paper, we present low dimensional models for the
noise-induced excitable dynamics in Bacillus subtilis,
whereby a key protein ComK, which drives a complex
chain of reactions leading to bacterial competence,
gets expressed rapidly in large quantities (competent
state) before subsiding to low levels of expression
(vegetative state). These rapid reactions suggest the
application of an adiabatic approximation of the
dynamics of the regulatory model that, however, lead to
competence durations that are incorrect by a factor of
2. We apply a modified version of an iterative
functional procedure that faithfully approximates the
time-course of the trajectories in terms of a
two-dimensional model involving proteins ComK and ComS.
Furthermore, in order to describe the bimodal bivariate
marginal probability distribution obtained from the
Gillespie simulations of the CME, we introduce a
tunable multiplicative noise term in a two-dimensional
Langevin model whose stationary state is described by
the time-independent solution of the corresponding
Fokker--Planck equation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ni:2016:PME,
author = "Xumin Ni and Wei Guo and Kai Yuan and Xiong Yang and
Zhiming Ma and Shuhua Xu and Shihua Zhang",
title = "A Probabilistic Method for Estimating the Sharing of
Identity by Descent for Populations with Migration",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "281--290",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2480074",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The inference of demographic history of populations is
an important undertaking in population genetics. A few
recent studies have developed identity-by-descent (IBD)
based methods to reveal the signature of the relatively
recent historical events. Notably, Pe'er and his
colleagues have introduced a novel method (named PIBD
here) by employing IBD sharing to infer effective
population size and migration rate. However, under
island model, PIBD neglects the coalescent information
before the time to the most recent common ancestor
(tMRCA) which leads to apparent deviations in certain
situations. In this paper, we propose a new method,
MIBD, by adopting a Markov process to describe the
island model and develop a new formula for estimating
IBD sharing. The new formula considers the coalescent
information before tMRCA and the joint effect of the
coalescent and migration events. We apply both MIBD and
PIBD to the genome-wide data of two human populations
(Palestinian and Bedouin) obtained from the HGDP-CEPH
database, and demonstrate that MIBD is competitive to
PIBD. Our simulation analyses also show that the
results of MIBD are more accurate than those of PIBD
especially in the case of small effective population
size.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hu:2016:RTN,
author = "Yuanqi Hu and Pantelis Georgiou",
title = "A Real-Time de novo {DNA} Sequencing Assembly Platform
Based on an {FPGA} Implementation",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "291--300",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2442974",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper presents an FPGA based DNA comparison
platform which can be run concurrently with the sensing
phase of DNA sequencing and shortens the overall time
needed for de novo DNA assembly. A hybrid overlap
searching algorithm is applied which is scalable and
can deal with incremental detection of new bases. To
handle the incomplete data set which gradually
increases during sequencing time, all-against-all
comparisons are broken down into successive
window-against-window comparison phases and executed
using a novel dynamic suffix comparison algorithm
combined with a partitioned dynamic programming method.
The complete system has been designed to facilitate
parallel processing in hardware, which allows real-time
comparison and full scalability as well as a decrease
in the number of computations required. A base pair
comparison rate of 51.2 G/s is achieved when
implemented on an FPGA with successful DNA comparison
when using data sets from real genomes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Singh:2016:BAI,
author = "Nitin Singh and Mathukumalli Vidyasagar",
title = "{bLARS}: an Algorithm to Infer Gene Regulatory
Networks",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "301--314",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2450740",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Inferring gene regulatory networks (GRNs) from
high-throughput gene-expression data is an important
and challenging problem in systems biology. Several
existing algorithms formulate GRN inference as a
regression problem. The available regression based
algorithms are based on the assumption that all
regulatory interactions are linear. However, nonlinear
transcription regulation mechanisms are common in
biology. In this work, we propose a new regression
based method named bLARS that permits a variety of
regulatory interactions from a predefined but otherwise
arbitrary family of functions. On three DREAM benchmark
datasets, namely gene expression data from E. coli,
Yeast, and a synthetic data set, bLARS outperforms
state-of-the-art algorithms in the terms of the overall
score. On the individual networks, bLARS offers the
best performance among currently available similar
algorithms, namely algorithms that do not use
perturbation information and are not meta-algorithms.
Moreover, the presented approach can also be utilized
for general feature selection problems in domains other
than biology, provided they are of a similar
structure.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Taliun:2016:FSB,
author = "Daniel Taliun and Johann Gamper and Ulf Leser and
Cristian Pattaro",
title = "Fast Sampling-Based Whole-Genome Haplotype Block
Recognition",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "315--325",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2456897",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Scaling linkage disequilibrium (LD) based haplotype
block recognition to the entire human genome has always
been a challenge. The best-known algorithm has
quadratic runtime complexity and, even when
sophisticated search space pruning is applied, still
requires several days of computations. Here, we propose
a novel sampling-based algorithm, called S-MIG$^{++}$ ,
where the main idea is to estimate the area that most
likely contains all haplotype blocks by sampling a very
small number of SNP pairs. A subsequent refinement step
computes the exact blocks by considering only the SNP
pairs within the estimated area. This approach
significantly reduces the number of computed LD
statistics, making the recognition of haplotype blocks
very fast. We theoretically and empirically prove that
the area containing all haplotype blocks can be
estimated with a very high degree of certainty. Through
experiments on the 243,080 SNPs on chromosome 20 from
the 1,000 Genomes Project, we compared our previous
algorithm MIG$^{++}$ with the new S-MIG$^{++}$ and
observed a runtime reduction from 2.8 weeks to 34.8
hours. In a parallelized version of the S-MIG $^{++}$
algorithm using 32 parallel processes, the runtime was
further reduced to 5.1 hours.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sigdel:2016:FFS,
author = "Madhu S. Sigdel and Madhav Sigdel and Semih Dinc and
Imren Dinc and Marc L. Pusey and Ramazan S. Aygun",
title = "{FocusALL}: Focal Stacking of Microscopic Images Using
Modified {Harris} Corner Response Measure",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "326--340",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2459685",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Automated image analysis of microscopic images such as
protein crystallization images and cellular images is
one of the important research areas. If objects in a
scene appear at different depths with respect to the
camera's focal point, objects outside the depth of
field usually appear blurred. Therefore, scientists
capture a collection of images with different depths of
field. Focal stacking is a technique of creating a
single focused image from a stack of images collected
with different depths of field. In this paper, we
introduce a novel focal stacking technique, FocusALL,
which is based on our modified Harris Corner Response
Measure. We also propose enhanced FocusALL for
application on images collected under high resolution
and varying illumination. FocusALL resolves problems
related to the assumption that focus regions have high
contrast and high intensity. Especially, FocusALL
generates sharper boundaries around protein crystal
regions and good in focus images for high resolution
images in reasonable time. FocusALL outperforms other
methods on protein crystallization images and performs
comparably well on other datasets such as retinal
epithelial images and simulated datasets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Land:2016:MCS,
author = "Tyler A. Land and Perry Fizzano and Robin B. Kodner",
title = "Measuring Cluster Stability in a Large Scale
Phylogenetic Analysis of Functional Genes in
Metagenomes Using {{\tt pplacer}}",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "341--349",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2446470",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Analysis of metagenomic sequence data requires a
multi-stage workflow. The results of each intermediate
step possess an inherent uncertainty and potentially
impact the as-yet-unmeasured statistical significance
of downstream analyses. Here, we describe our
phylogenetic analysis pipeline which uses the {\tt
pplacer} program to place many shotgun sequences
corresponding to a single functional gene onto a fixed
phylogenetic tree. We then use the squash clustering
method to compare multiple samples with respect to that
gene. We approximate the statistical significance of
each gene's clustering result by measuring its cluster
stability, the consistency of that clustering result
when the probabilistic placements made by {\tt pplacer}
are systematically reassigned and then clustered again,
as measured by the adjusted Rand Index. We find that
among the genes investigated, the majority of analyses
are stable, based on the average adjusted Rand Index.
We investigated properties of each gene that may
explain less stable results. These genes tended to have
less convex reference trees, less total reads recruited
to the gene, and a greater Expected Distance between
Placement Locations as given by {\tt pplacer} when
examined in aggregate. However, for an individual
functional gene, these measures alone do not predict
cluster stability.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ganesan:2016:PSC,
author = "Narayan Ganesan and Jie Li and Vishakha Sharma and
Hanyu Jiang and Adriana Compagnoni",
title = "Process Simulation of Complex Biological Pathways in
Physical Reactive Space and Reformulated for Massively
Parallel Computing Platforms",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "365--379",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2443784",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Biological systems encompass complexity that far
surpasses many artificial systems. Modeling and
simulation of large and complex biochemical pathways is
a computationally intensive challenge. Traditional
tools, such as ordinary differential equations, partial
differential equations, stochastic master equations,
and Gillespie type methods, are all limited either by
their modeling fidelity or computational efficiency or
both. In this work, we present a scalable computational
framework based on modeling biochemical reactions in
explicit 3D space, that is suitable for studying the
behavior of large and complex biological pathways. The
framework is designed to exploit parallelism and
scalability offered by commodity massively parallel
processors such as the graphics processing units (GPUs)
and other parallel computing platforms. The reaction
modeling in 3D space is aimed at enhancing the realism
of the model compared to traditional modeling tools and
framework. We introduce the Parallel Select algorithm
that is key to breaking the sequential bottleneck
limiting the performance of most other tools designed
to study biochemical interactions. The algorithm is
designed to be computationally tractable, handle
hundreds of interacting chemical species and millions
of independent agents by considering all-particle
interactions within the system. We also present an
implementation of the framework on the popular graphics
processing units and apply it to the simulation study
of JAK-STAT Signal Transduction Pathway. The
computational framework will offer a deeper insight
into various biological processes within the cell and
help us observe key events as they unfold in space and
time. This will advance the current state-of-the-art in
simulation study of large scale biological systems and
also enable the realistic simulation study of
macro-biological cultures, where inter-cellular
interactions are prevalent.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tu:2016:UFC,
author = "Xudong Tu and Yuanliang Wang and Maolan Zhang and
Jinchuan Wu",
title = "Using Formal Concept Analysis to Identify Negative
Correlations in Gene Expression Data",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "380--391",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2443805",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recently, many biological studies reported that two
groups of genes tend to show negatively correlated or
opposite expression tendency in many biological
processes or pathways. The negative correlation between
genes may imply an important biological mechanism. In
this study, we proposed a FCA-based negative
correlation algorithm (NCFCA) that can effectively
identify opposite expression tendency between two gene
groups in gene expression data. After applying it to
expression data of cell cycle-regulated genes in yeast,
we found that six minichromosome maintenance family
genes showed the opposite changing tendency with eight
core histone family genes. Furthermore, we confirmed
that the negative correlation expression pattern
between these two families may be conserved in the cell
cycle. Finally, we discussed the reasons underlying the
negative correlation of six minichromosome maintenance
(MCM) family genes with eight core histone family
genes. Our results revealed that negative correlation
is an important and potential mechanism that maintains
the balance of biological systems by repressing some
genes while inducing others. It can thus provide new
understanding of gene expression and regulation, the
causes of diseases, etc.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2016:CIB,
author = "Jin-Xing Liu and Yong Xu and Ying-Lian Gao and
Chun-Hou Zheng and Dong Wang and Qi Zhu",
title = "A Class-Information-Based Sparse Component Analysis
Method to Identify Differentially Expressed Genes on
{RNA-Seq} Data",
journal = j-TCBB,
volume = "13",
number = "2",
pages = "392--398",
month = mar,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2440265",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 12:53:43 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "With the development of deep sequencing technologies,
many RNA-Seq data have been generated. Researchers have
proposed many methods based on the sparse theory to
identify the differentially expressed genes from these
data. In order to improve the performance of sparse
principal component analysis, in this paper, we propose
a novel class-information-based sparse component
analysis (CISCA) method which introduces the class
information via a total scatter matrix. First, CISCA
normalizes the RNA-Seq data by using a Poisson model to
obtain their differential sections. Second, the total
scatter matrix is gotten by combining the between-class
and within-class scatter matrices. Third, we decompose
the total scatter matrix by using singular value
decomposition and construct a new data matrix by using
singular values and left singular vectors. Then, aiming
at obtaining sparse components, CISCA decomposes the
constructed data matrix by solving an optimization
problem with sparse constraints on loading vectors.
Finally, the differentially expressed genes are
identified by using the sparse loading vectors. The
results on simulation and real RNA-Seq data demonstrate
that our method is effective and suitable for analyzing
these data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mozaffari-Kermani:2016:ISS,
author = "Mehran Mozaffari-Kermani and Reza Azarderakhsh and Kui
Ren and Jean-Luc Beuchat",
title = "Introduction to the special section on emerging
security trends for biomedical computations, devices,
and infrastructures: guest editorial",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "399--400",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "UNLIKE the traditional usage models for embedded
systems security, nowadays, emerging computing systems
are embedded in every aspect of human lives. One of the
emerging usage models in which security is vital is
deeply-embedded computing systems in human bodies,
e.g., implantable and wearable medical devices. In
addition to the security threats to traditional
embedded systems, emerging deeply-embedded computing
systems exhibit a larger attack surface, prone to more
serious or life-threatening attacks. Biomedical
deeply-embedded systems (deployed in human body, with
computer programs sending and receiving medical data
and performing data mining for the decisions) are
currently getting developed with rapid rate and
tremendous success. Moreover, the security/privacy
issues in every aspect of bioinformatics (algorithmic,
statistical, and the like) including secure and private
big data analytics, acquisition, and storage,
privacy-preserving data mining for biomedicine, secure
machine-learning of bioinformatics, and security of
hardware and software systems used for biological
databases are emerging given their unique constraints.
Many of the systems for such computations will need to
be transparently integrated into sensitive environments
--- the consequent size and energy constraints imposed
on any security solutions are extreme. Thus, unique
challenges arise due to the sensitivity of computation
processing, need for security in implementations, and
assurance ``gaps.''",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kocabas:2016:ESM,
author = "Ovunc Kocabas and Tolga Soyata and Mehmet K. Aktas",
title = "Emerging security mechanisms for medical cyber
physical systems",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "401--416",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The following decade will witness a surge in remote
health-monitoring systems that are based on body-worn
monitoring devices. These Medical Cyber Physical
Systems (MCPS) will be capable of transmitting the
acquired data to a private or public cloud for storage
and processing. Machine learning algorithms running in
the cloud and processing this data can provide decision
support to healthcare professionals. There is no doubt
that the security and privacy of the medical data is
one of the most important concerns in designing an
MCPS. In this paper, we depict the general architecture
of an MCPS consisting of four layers: data acquisition,
data aggregation, cloud processing, and action. Due to
the differences in hardware and communication
capabilities of each layer, different encryption
schemes must be used to guarantee data privacy within
that layer. We survey conventional and emerging
encryption schemes based on their ability to provide
secure storage, data sharing, and secure computation.
Our detailed experimental evaluation of each scheme
shows that while the emerging encryption schemes enable
exciting new features such as secure sharing and secure
computation, they introduce several orders-of-magnitude
computational and storage overhead. We conclude our
paper by outlining future research directions to
improve the usability of the emerging encryption
schemes in an MCPS.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2016:AMU,
author = "Cheng Chen and Fengchao Zhang and Jamie Barras and
Kaspar Althoefer and Swarup Bhunia and Soumyajit
Mandal",
title = "Authentication of medicines using nuclear quadrupole
resonance spectroscopy",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "417--430",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The production and sale of counterfeit and substandard
pharmaceutical products, such as essential medicines,
is an important global public health problem. We
describe a chemometric passport-based approach to
improve the security of the pharmaceutical supply
chain. Our method is based on applying nuclear
quadrupole resonance (NQR) spectroscopy to authenticate
the contents of medicine packets. NQR is a
non-invasive, non-destructive, and quantitative radio
frequency (RF) spectroscopic technique. It is sensitive
to subtle features of the solid-state chemical
environment and thus generates unique chemical
fingerprints that are intrinsically difficult to
replicate. We describe several advanced NQR techniques,
including two-dimensional measurements, polarization
enhancement, and spin density imaging, that further
improve the security of our authentication approach. We
also present experimental results that confirm the
specificity and sensitivity of NQR and its ability to
detect counterfeit medicines.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gong:2016:PDA,
author = "Yanmin Gong and Yuguang Fang and Yuanxiong Guo",
title = "Private data analytics on biomedical sensing data via
distributed computation",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "431--444",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Advances in biomedical sensors and mobile
communication technologies have fostered the rapid
growth of mobile health (mHealth) applications in the
past years. Users generate a high volume of biomedical
data during health monitoring, which can be used by the
mHealth server for training predictive models for
disease diagnosis and treatment. However, the
biomedical sensing data raise serious privacy concerns
because they reveal sensitive information such as
health status and lifestyles of the sensed subjects.
This paper proposes and experimentally studies a scheme
that keeps the training samples private while enabling
accurate construction of predictive models. We
specifically consider logistic regression models which
are widely used for predicting dichotomous outcomes in
healthcare, and decompose the logistic regression
problem into small subproblems over two types of
distributed sensing data, i.e., horizontally
partitioned data and vertically partitioned data. The
subproblems are solved using individual private data,
and thus mHealth users can keep their private data
locally and only upload (encrypted) intermediate
results to the mHealth server for model training.
Experimental results based on real datasets show that
our scheme is highly efficient and scalable to a large
number of mHealth users.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ali:2016:SAC,
author = "Sk Subidh Ali and Mohamed Ibrahim and Ozgur Sinanoglu
and Krishnendu Chakrabarty and Ramesh Karri",
title = "Security assessment of cyberphysical digital
microfluidic biochips",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "445--458",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A digital microfluidic biochip (DMFB) is an emerging
technology that enables miniaturized analysis systems
for point-of-care clinical diagnostics, DNA sequencing,
and environmental monitoring. A DMFB reduces the rate
of sample and reagent consumption, and automates the
analysis of assays. In this paper, we provide the first
assessment of the security vulnerabilities of DMFBs. We
identify result-manipulation attacks on a DMFB that
maliciously alter the assay outcomes. Two practical
result-manipulation attacks are shown on a DMFB
platform performing enzymatic glucose assay on serum.
In the first attack, the attacker adjusts the
concentration of the glucose sample and thereby
modifies the final result. In the second attack, the
attacker tampers with the calibration curve of the
assay operation. We then identify denial-of-service
attacks, where the attacker can disrupt the assay
operation by tampering either with the droplet-routing
algorithm or with the actuation sequence. We
demonstrate these attacks using a digital microfluidic
synthesis simulator. The results show that the attacks
are easy to implement and hard to detect. Therefore,
this work highlights the need for effective protections
against malicious modifications in DMFBs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Knox:2016:MFG,
author = "David A. Knox and Robin D. Dowell",
title = "A modeling framework for generation of positional and
temporal simulations of transcriptional regulation",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "459--471",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present a modeling framework aimed at capturing
both the positional and temporal behavior of
transcriptional regulatory proteins in eukaryotic
cells. There is growing evidence that transcriptional
regulation is the complex behavior that emerges not
solely from the individual components, but rather from
their collective behavior, including competition and
cooperation. Our framework describes individual
regulatory components using generic action oriented
descriptions of their biochemical interactions with a
DNA sequence. All the possible actions are based on the
current state of factors bound to the DNA. We developed
a rule builder to automatically generate the complete
set of biochemical interaction rules for any given DNA
sequence. Off-the-shelf stochastic simulation engines
can model the behavior of a system of rules and the
resulting changes in the configuration of bound factors
can be visualized. We compared our model to
experimental data at well-studied loci in yeast,
confirming that our model captures both the positional
and temporal behavior of transcriptional regulation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gu:2016:MSO,
author = "Xu Gu",
title = "A multi-state optimization framework for parameter
estimation in biological systems",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "472--482",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Parameter estimation is a key concern for reliable and
predictive models of biological systems. In this paper,
we propose a multi-objective, multi-state optimization
framework that allows multiple data sources to be
incorporated into the parameter estimation process.
This enables the model to better represent a diverse
range of data from both within and without the training
set; and to determine more biologically relevant
parameter values for the model parameters. The
framework is based on a multi-objective PSwarm
implementation (MoPSwarm) and is validated via a case
study on the ERK signalling pathway, in which
significant advantages over the conventional
single-state approach are demonstrated. Several
variants of the framework are analyzed to determine the
optimal configuration for convergence and solution
quality.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xie:2016:AHA,
author = "Jiang Xie and Chaojuan Xiang and Jin Ma and Jun Tan
and Tieqiao Wen and Jinzhi Lei and Qing Nie",
title = "An adaptive hybrid algorithm for global network
alignment",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "483--493",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "It is challenging to obtain reliable and optimal
mapping between networks for alignment algorithms when
both nodal and topological structures are taken into
consideration due to the underlying NP-hard problem.
Here, we introduce an adaptive hybrid algorithm that
combines the classical Hungarian algorithm and the
Greedy algorithm (HGA) for the global alignment of
biomolecular networks. With this hybrid algorithm,
every pair of nodes with one in each network is first
aligned based on node information (e.g., their sequence
attributes) and then followed by an adaptive and
convergent iteration procedure for aligning the
topological connections in the networks. For four
well-studied protein interaction networks, i.e.,
C.elegans, yeast, D.melanogaster, and human,
applications of HGA lead to improved alignments in
acceptable running time. The mapping between yeast and
human PINs obtained by the new algorithm has the
largest value of common Gene Ontology (GO) terms
compared to those obtained by other existing
algorithms, while it still has lower Mean normalized
entropy (MNE) and good performances on several other
measures. Overall, the adaptive HGA is effective and
capable of providing good mappings between aligned
networks in which the biological properties of both the
nodes and the connections are important.",
acknowledgement = ack-nhfb,
articleno = "3",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
xxpages = "3:1--3:??",
}
@Article{Al-Dalky:2016:AMC,
author = "Rami Al-Dalky and Kamal Taha and Dirar {Al Homouz} and
Murad Qasaimeh",
title = "Applying {Monte Carlo} simulation to biomedical
literature to approximate genetic network",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "494--504",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Biologists often need to know the set of genes
associated with a given set of genes or a given
disease. We propose in this paper a classifier system
called Monte Carlo for Genetic Network (MCforGN) that
can construct genetic networks, identify functionally
related genes, and predict gene-disease associations.
MCforGN identifies functionally related genes based on
their co-occurrences in the abstracts of biomedical
literature. For a given gene g, the system first
extracts the set of genes found within the abstracts of
biomedical literature associated with g. It then ranks
these genes to determine the ones with high
co-occurrences with g. It overcomes the limitations of
current approaches that employ analytical deterministic
algorithms by applying Monte Carlo Simulation to
approximate genetic networks. It does so by conducting
repeated random sampling to obtain numerical results
and to optimize these results. Moreover, it analyzes
results to obtain the probabilities of different genes'
co-occurrences using series of statistical tests.
MCforGN can detect gene-disease associations by
employing a combination of centrality measures (to
identify the central genes in disease-specific genetic
networks) and Monte Carlo Simulation. MCforGN aims at
enhancing state-of-the-art biological text mining by
applying novel extraction techniques. We evaluated
MCforGN by comparing it experimentally with nine
approaches. Results showed marked improvement.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pavelka:2016:CAA,
author = "Antonin Pavelka and Eva Sebestova and Barbora
Kozlikova and Jan Brezovsky and Jiri Sochor and Jiri
Damborsky",
title = "{CAVER}: algorithms for analyzing dynamics of tunnels
in macromolecules",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "505--517",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The biological function of a macromolecule often
requires that a small molecule or ion is transported
through its structure. The transport pathway often
leads through void spaces in the structure. The
properties of transport pathways change significantly
in time; therefore, the analysis of a trajectory from
molecular dynamics rather than of a single static
structure is needed for understanding the function of
pathways. The identification and analysis of transport
pathways are challenging because of the high complexity
and diversity of macromolecular shapes, the thermal
motion of their atoms, and the large amount of
conformations needed to properly describe
conformational space of protein structure. In this
paper, we describe the principles of the CAVER 3.0
algorithms for the identification and analysis of
properties of transport pathways both in static and
dynamic structures. Moreover, we introduce the improved
clustering solution for finding tunnels in
macromolecules, which is included in the latest CAVER
3.02 version. Voronoi diagrams are used to identify
potential pathways in each snapshot of a molecular
dynamics trajectory and clustering is then used to find
the correspondence between tunnels from different
snapshots. Furthermore, the geometrical properties of
pathways and their evolution in time are computed and
visualized.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Puljiz:2016:DGV,
author = "Zrinka Puljiz and Haris Vikalo",
title = "Decoding genetic variations: communications-inspired
haplotype assembly",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "518--530",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "High-throughput DNA sequencing technologies allow fast
and affordable sequencing of individual genomes and
thus enable unprecedented studies of genetic
variations. Information about variations in the genome
of an individual is provided by haplotypes, ordered
collections of single nucleotide polymorphisms.
Knowledge of haplotypes is instrumental in finding
genes associated with diseases, drug development, and
evolutionary studies. Haplotype assembly from
high-throughput sequencing data is challenging due to
errors and limited lengths of sequencing reads. The key
observation made in this paper is that the minimum
error-correction formulation of the haplotype assembly
problem is identical to the task of deciphering a coded
message received over a noisy channel---a classical
problem in the mature field of communication theory.
Exploiting this connection, we develop novel haplotype
assembly schemes that rely on the bit-flipping and
belief propagation algorithms often used in
communication systems. The latter algorithm is then
adapted to the haplotype assembly of polyploids. We
demonstrate on both simulated and experimental data
that the proposed algorithms compare favorably with
state-of-the-art haplotype assembly methods in terms of
accuracy, while being scalable and computationally
efficient.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2016:EDP,
author = "Cong Li and Can Yang and Greg Hather and Ray Liu and
Hongyu Zhao",
title = "Efficient drug-pathway association analysis via
integrative penalized matrix decomposition",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "531--540",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/matlab.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Traditional drug discovery practice usually follows
the ``one drug --- one target'' approach, seeking to
identify drug molecules that act on individual targets,
which ignores the systemic nature of human diseases.
Pathway-based drug discovery recently emerged as an
appealing approach to overcome this limitation. An
important first step of such pathway-based drug
discovery is to identify associations between drug
molecules and biological pathways. This task has been
made feasible by the accumulating data from
high-throughput transcription and drug sensitivity
profiling. In this paper, we developed ``iPaD'', an
integrative Penalized Matrix Decomposition method to
identify drug-pathway associations through jointly
modeling of such high-throughput transcription and drug
sensitivity data. A scalable biconvex optimization
algorithm was implemented and gave iPaD tremendous
advantage in computational efficiency over current
state-of-the-art method, which allows it to handle the
evergrowing large-scale data sets that current method
cannot afford to. On two widely used real data sets,
iPaD also significantly outperformed the current method
in terms of the number of validated drug-pathway
associations that were identified. The Matlab code of
our algorithm publicly available at
http://licong-jason.github.io/iPaD/",
acknowledgement = ack-nhfb,
articleno = "2",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
xxpages = "2:1--2:??",
}
@Article{Macwan:2016:RSC,
author = "Isaac G. Macwan and Zihe Zhao and Omar T. Sobh and
Ishita Mukerji and Bhushan Dharmadhikari and Prabir K.
Patra",
title = "Residue specific and chirality dependent interactions
between carbon nanotubes and flagellin",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "541--548",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Flagellum is a lash-like cellular appendage found in
many single-celled living organisms. The flagellin
protofilaments contain 11-helix dual turn structure in
a single flagellum. Each flagellin consists of four
sub-domains --- two inner domains (D0, D1) and two
outer domains (D2, D3). While inner domains
predominantly consist of $ \alpha $-helices, the outer
domains are primarily beta sheets with D3. In
flagellum, the outermost sub-domain is the only one
that is exposed to the native environment. This study
focuses on the interactions of the residues of D3 of an
R-type flagellin with 5nm long chiral (5, 15) and
arm-chair (12, 12) single-walled carbon nanotubes
(SWNT) using molecular dynamics simulation. It presents
the interactive forces between the SWNT and the
residues of D3 from the perspectives of size and
chirality of the SWNT. It is found that the metallic
(arm-chair) SWNT interacts the most with glycine and
threonine residues through van der Waals and
hydrophobic interactions, whereas the semiconducting
(chiral) SWNT interacts largely with the area of
protein devoid of glycine by van der Waals, hydrophobic
interactions, and hydrogen bonding. This indicates a
crucial role that glycine plays in distinguishing
metallic from semiconducting SWNTs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liang:2016:NMD,
author = "Cheng Liang and Yue Li and Jiawei Luo",
title = "A novel method to detect functional {microRNA}
regulatory modules by bicliques merging",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "549--556",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "MicroRNAs (miRNAs) are post-transcriptional regulators
that repress the expression of their targets. They are
known to work cooperatively with genes and play
important roles in numerous cellular processes.
Identification of miRNA regulatory modules (MRMs) would
aid deciphering the combinatorial effects derived from
the many-to-many regulatory relationships in complex
cellular systems. Here, we develop an effective method
called BiCliques Merging (BCM) to predict MRMs based on
bicliques merging. By integrating the miRNA/mRNA
expression profiles from The Cancer Genome Atlas (TCGA)
with the computational target predictions, we construct
a weighted miRNA regulatory network for module
discovery. The maximal bicliques detected in the
network are statistically evaluated and filtered
accordingly. We then employed a greedy-based strategy
to iteratively merge the remaining bicliques according
to their overlaps together with edge weights and the
gene-gene interactions. Comparing with existing methods
on two cancer datasets from TCGA, we showed that the
modules identified by our method are more densely
connected and functionally enriched. Moreover, our
predicted modules are more enriched for miRNA families
and the miRNA-mRNA pairs within the modules are more
negatively correlated. Finally, several potential
prognostic modules are revealed by Kaplan--Meier
survival analysis and breast cancer subtype analysis.
Availability: BCM is implemented in Java and available
for download in the supplementary materials, which can
be found on the Computer Society Digital Library at
http://doi.ieeecomputersociety.org/10.1109/TCBB.2015.2462370.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Teng:2016:EGP,
author = "Ben Teng and Can Yang and Jiming Liu and Zhipeng Cai
and Xiang Wan",
title = "Exploring the genetic patterns of complex diseases via
the integrative genome-wide approach",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "557--564",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/matlab.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genome-wide association studies (GWASs), which assay
more than a million single nucleotide polymorphisms
(SNPs) in thousands of individuals, have been widely
used to identify genetic risk variants for complex
diseases. However, most of the variants that have been
identified contribute relatively small increments of
risk and only explain a small portion of the genetic
variation in complex diseases. This is the so-called
missing heritability problem. Evidence has indicated
that many complex diseases are genetically related,
meaning these diseases share common genetic risk
variants. Therefore, exploring the genetic correlations
across multiple related studies could be a promising
strategy for removing spurious associations and
identifying underlying genetic risk variants, and
thereby uncovering the mystery of missing heritability
in complex diseases. We present a general and robust
method to identify genetic patterns from multiple
large-scale genomic datasets. We treat the summary
statistics as a matrix and demonstrate that genetic
patterns will form a low-rank matrix plus a sparse
component. Hence, we formulate the problem as a matrix
recovering problem, where we aim to discover risk
variants shared by multiple diseases/traits and those
for each individual disease/trait. We propose a convex
formulation for matrix recovery and an efficient
algorithm to solve the problem. We demonstrate the
advantages of our method using both synthesized
datasets and real datasets. The experimental results
show that our method can successfully reconstruct both
the shared and the individual genetic patterns from
summary statistics and achieve comparable performances
compared with alternative methods under a wide range of
scenarios. The MATLAB code is available
at:http://www.comp.hkbu.edu.hk/~xwan/iga.zip.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mirzaei:2016:FCN,
author = "Sajad Mirzaei and Yufeng Wu",
title = "Fast construction of near parsimonious hybridization
networks for multiple phylogenetic trees",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "565--570",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Hybridization networks represent plausible
evolutionary histories of species that are affected by
reticulate evolutionary processes. An established
computational problem on hybridization networks is
constructing the most parsimonious hybridization
network such that each of the given phylogenetic trees
(called gene trees) is ``displayed'' in the network.
There have been several previous approaches, including
an exact method and several heuristics, for this
NP-hard problem. However, the exact method is only
applicable to a limited range of data, and heuristic
methods can be less accurate and also slow sometimes.
In this paper, we develop a new algorithm for
constructing near parsimonious networks for multiple
binary gene trees. This method is more efficient for
large numbers of gene trees than previous heuristics.
This new method also produces more parsimonious results
on many simulated datasets as well as a real biological
dataset than a previous method. We also show that our
method produces topologically more accurate networks
for many datasets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ren:2016:FRA,
author = "Hai-Peng Ren and Xiao-Na Huang and Jia-Xuan Hao",
title = "Finding robust adaptation gene regulatory networks
using multi-objective genetic algorithm",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "571--577",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Robust adaptation plays a key role in gene regulatory
networks, and it is thought to be an important
attribute for the organic or cells to survive in
fluctuating conditions. In this paper, a simplified
three-node enzyme network is modeled by the
Michaelis--Menten rate equations for all possible
topologies, and a family of topologies and the
corresponding parameter sets of the network with
satisfactory adaptation are obtained using the
multi-objective genetic algorithm. The proposed
approach improves the computation efficiency
significantly as compared to the time consuming
exhaustive searching method. This approach provides a
systemic way for searching the feasible topologies and
the corresponding parameter sets to make the gene
regulatory networks have robust adaptation. The
proposed methodology, owing to its universality and
simplicity, can be used to address more complex issues
in biological networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{He:2016:MMI,
author = "Dan He and Irina Rish and David Haws and Laxmi
Parida",
title = "{MINT}: mutual information based transductive feature
selection for genetic trait prediction",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "578--583",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Whole genome prediction of complex phenotypic traits
using high-density genotyping arrays has attracted a
lot of attention, as it is relevant to the fields of
plant and animal breeding and genetic epidemiology.
Since the number of genotypes is generally much bigger
than the number of samples, predictive models suffer
from the curse of dimensionality. The curse of
dimensionality problem not only affects the
computational efficiency of a particular genomic
selection method, but can also lead to a poor
performance, mainly due to possible overfitting, or
un-informative features. In this work, we propose a
novel transductive feature selection method, called
MINT, which is based on the MRMR (Max-Relevance and
Min-Redundancy) criterion. We apply MINT on genetic
trait prediction problems and show that, in general,
MINT is a better feature selection method than the
state-of-the-art inductive method MRMR.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Stamoulis:2016:OSD,
author = "Catherine Stamoulis and Rebecca A. Betensky",
title = "Optimization of signal decomposition matched filtering
{(SDMF)} for improved detection of copy-number
variations",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "584--591",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We aim to improve the performance of the previously
proposed signal decomposition matched filtering (SDMF)
method [26] for the detection of copy-number variations
(CNV) in the human genome. Through simulations we show
that the modified SDMF is robust even at high noise
levels and outperforms the original SDMF method, which
indirectly depends on CNV frequency. Simulations are
also used to develop a systematic approach for
selecting relevant parameter thresholds in order to
optimize sensitivity, specificity and computational
efficiency. We apply the modified method to array CGH
data from normal samples in The Cancer Genome Atlas
(TCGA) and compare detected CNVs to those estimated
using Circular Binary Segmentation (CBS) [19], a Hidden
Markov Model (HMM)-based approach [11] and a subset of
CNVs in the Database of Genomic Variants. We show that
a substantial number of previously identified CNVs are
detected by the optimized SDMF, which also outperforms
all other methods.",
abstract = "We aim to improve the performance of the previously
proposed signal decomposition matched filtering (SDMF)
method [26] for the detection of copy-number variations
(CNV) in the human genome. Through simulations, we show
that the modified SDMF is robust even at high noise
levels and outperforms the original SDMF method, which
indirectly depends on CNV frequency. Simulations are
also used to develop a systematic approach for
selecting relevant parameter thresholds in order to
optimize sensitivity, specificity and computational
efficiency. We apply the modified method to array CGH
data from normal samples in the cancer genome atlas
(TCGA) and compare detected CNVs to those estimated
using circular binary segmentation (CBS) [19], a hidden
Markov model (HMM)-based approach [11] and a subset of
CNVs in the Database of Genomic Variants. We show that
a substantial number of previously identified CNVs are
detected by the optimized SDMF, which also outperforms
the other two methods.",
acknowledgement = ack-nhfb,
articleno = "1",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
xxpages = "1:1--1:??",
}
@Article{Liu:2016:PSE,
author = "Yongchao Liu and Thomas Hankeln and Bertil Schmidt",
title = "Parallel and space-efficient construction of
{Burrows--Wheeler} transform and suffix array for big
genome data",
journal = j-TCBB,
volume = "13",
number = "3",
pages = "592--598",
month = may,
year = "2016",
CODEN = "ITCBCY",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Aug 29 06:50:39 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Next-generation sequencing technologies have led to
the sequencing of more and more genomes, propelling
related research into the era of big data. In this
paper, we present ParaBWT, a parallelized
Burrows--Wheeler transform (BWT) and suffix array
construction algorithm for big genome data. In ParaBWT,
we have investigated a progressive construction
approach to constructing the BWT of single genome
sequences in linear space complexity, but with a small
constant factor. This approach has been further
parallelized using multi-threading based on a
master-slave coprocessing model. After gaining the BWT,
the suffix array is constructed in a memory-efficient
manner. The performance of ParaBWT has been evaluated
using two sequences generated from two human genome
assemblies: the Ensembl Homo sapiens assembly and the
human reference genome. Our performance comparison to
FMD-index and Bwt-disk reveals that on 12 CPU cores,
ParaBWT runs up to $ 2.2 \times $ faster than FMD-index
and up to $ 99.0 \times $ faster than Bwt-disk. BWT
construction algorithms for very long genomic sequences
are time consuming and (due to their incremental
nature) inherently difficult to parallelize. Thus,
their parallelization is challenging and even
relatively small speedups like the ones of our method
over FMD-index are of high importance to research.
ParaBWT is written in C++, and is freely available at
http://parabwt.sourceforge.net.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2016:NEA,
author = "Biing-Feng Wang",
title = "A New Efficient Algorithm for the All Sorting
Reversals Problem with No Bad Components",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "599--609",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2476803",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The problem of finding all reversals that take a
permutation one step closer to a target permutation is
called the all sorting reversals problem the ASR
problem. For this problem, Siepel had an On3-time
algorithm. Most complications of his algorithm stem
from some peculiar structures called bad components.
Since bad components are very rare in both real and
simulated data, it is practical to study the ASR
problem with no bad components. For the ASR problem
with no bad components, Swenson et{\"\i}$ 3 / 4 $ al.
gave an On2-time algorithm. Very recently, Swenson
found that their algorithm does not always work. In
this paper, a new algorithm is presented for the ASR
problem with no bad components. The time complexity is
On2 in the worst case and is linear in the size of
input and output in practice.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ji:2016:DFM,
author = "Junzhong Ji and Jiawei Luo and Cuicui Yang and Aidong
Zhang",
title = "Detecting Functional Modules Based on a Multiple-Grain
Model in Large-Scale Protein-Protein Interaction
Networks",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "610--622",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2480066",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Detecting functional modules from a Protein-Protein
Interaction PPI network is a fundamental and hot issue
in proteomics research, where many computational
approaches have played an important role in recent
years. However, how to effectively and efficiently
detect functional modules in large-scale PPI networks
is still a challenging problem. We present a new
framework, based on a multiple-grain model of PPI
networks, to detect functional modules in PPI networks.
First, we give a multiple-grain representation model of
a PPI network, which has a smaller scale with super
nodes. Next, we design the protein grain partitioning
method, which employs a functional similarity or a
structural similarity to merge some proteins layer by
layer. Thirdly, a refining mechanism with border node
tests is proposed to address the protein overlapping of
different modules during the grain eliminating process.
Finally, systematic experiments are conducted on five
large-scale yeast and human networks. The results show
that the framework not only significantly reduces the
running time of functional module detection, but also
effectively identifies overlapping modules while
keeping some competitive performances, thus it is
highly competent to detect functional modules in
large-scale PPI networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tran:2016:AOP,
author = "Ngoc Hieu Tran and Xin Chen",
title = "{AMAS}: Optimizing the Partition and Filtration of
Adaptive Seeds to Speed up Read Mapping",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "623--633",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2465900",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Read mapping is a key task in next-generation
sequencing NGS data analysis. To achieve an optimal
combination of accuracy, speed, and low memory
footprint, popular mapping tools often focus on
identifying one or a few best mapping locations for
each read. However, for many downstream analyses such
as prediction of genomic variants or protein binding
motifs located in repeat regions, isoform expression
quantification, metagenomics analysis, it is more
desirable to have a comprehensive set of all possible
mapping locations of NGS reads. In this paper, we
introduce AMAS, a read mapping tool that exhaustively
searches for possible mapping locations of NGS reads in
a reference sequence within a given edit distance. AMAS
features improvements of the mapping, partition, and
filtration of adaptive seeds to speed up the read
mapping. Performance results on simulated and real data
sets show that AMAS run several times faster than other
state-of-the-art read mappers while achieving similar
sensitivity and accuracy. AMAS is implemented in C++
and is freely available at
https://sourceforge.net/projects/ngsamas/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jiang:2016:UGB,
author = "Zhenchao Jiang and Lishuang Li and Degen Huang",
title = "An Unsupervised Graph Based Continuous Word
Representation Method for Biomedical Text Mining",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "634--642",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2478467",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In biomedical text mining tasks, distributed word
representation has succeeded in capturing semantic
regularities, but most of them are shallow-window based
models, which are not sufficient for expressing the
meaning of words. To represent words using deeper
information, we make explicit the semantic regularity
to emerge in word relations, including dependency
relations and context relations, and propose a novel
architecture for computing continuous vector
representation by leveraging those relations. The
performance of our model is measured on word analogy
task and Protein-Protein Interaction Extraction PPIE
task. Experimental results show that our method
performs overall better than other word representation
models on word analogy task and have many advantages on
biomedical text mining.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Catanzaro:2016:CPD,
author = "Daniele Catanzaro and Stanley E. Shackney and
Alejandro A. Sch{\"a}ffer and Russell Schwartz",
title = "Classifying the Progression of Ductal Carcinoma from
Single-Cell Sampled Data via Integer Linear
Programming: a Case Study",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "643--655",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2476808",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Ductal Carcinoma In Situ DCIS is a precursor lesion of
Invasive Ductal Carcinoma IDC of the breast.
Investigating its temporal progression could provide
fundamental new insights for the development of better
diagnostic tools to predict which cases of DCIS will
progress to IDC. We investigate the problem of
reconstructing a plausible progression from single-cell
sampled data of an individual with synchronous DCIS and
IDC. Specifically, by using a number of assumptions
derived from the observation of cellular atypia
occurring in IDC, we design a possible predictive model
using integer linear programming ILP. Computational
experiments carried out on a preexisting data set of 13
patients with simultaneous DCIS and IDC show that the
corresponding predicted progression models are
classifiable into categories having specific
evolutionary characteristics. The approach provides new
insights into mechanisms of clonal progression in
breast cancers and helps illustrate the power of the
ILP approach for similar problems in reconstructing
tumor evolution scenarios under complex sets of
constraints.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xi:2016:DRC,
author = "Jianing Xi and Ao Li",
title = "Discovering Recurrent Copy Number Aberrations in
Complex Patterns via Non-Negative Sparse Singular Value
Decomposition",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "656--668",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2474404",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recurrent copy number aberrations RCNAs in multiple
cancer samples are strongly associated with
tumorigenesis, and RCNA discovery is helpful to cancer
research and treatment. Despite the emergence of
numerous RCNA discovering methods, most of them are
unable to detect RCNAs in complex patterns that are
influenced by complicating factors including aberration
in partial samples, co-existing of gains and losses and
normal-like tumor samples. Here, we propose a novel
computational method, called non-negative sparse
singular value decomposition NN-SSVD, to address the
RCNA discovering problem in complex patterns. In
NN-SSVD, the measurement of RCNA is based on the
aberration frequency in a part of samples rather than
all samples, which can circumvent the complexity of
different RCNA patterns. We evaluate NN-SSVD on
synthetic dataset by comparison on detection scores and
Receiver Operating Characteristics curves, and the
results show that NN-SSVD outperforms existing methods
in RCNA discovery and demonstrate more robustness to
RCNA complicating factors. Applying our approach on a
breast cancer dataset, we successfully identify a
number of genomic regions that are strongly correlated
with previous studies, which harbor a bunch of known
breast cancer associated genes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2016:EBE,
author = "Lishuang Li and Shanshan Liu and Meiyue Qin and Yiwen
Wang and Degen Huang",
title = "Extracting Biomedical Event with Dual Decomposition
Integrating Word Embeddings",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "669--677",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2476876",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Extracting biomedical event from literatures has
attracted much attention recently. By now, most of the
state-of-the-art systems have been based on pipelines
which suffer from cascading errors, and the words
encoded by one-hot are unable to represent the semantic
information. Joint inference with dual decomposition
and novel word embeddings are adopted to address the
two problems, respectively, in this work. Word
embeddings are learnt from large scale unlabeled texts
and integrated as an unsupervised feature into other
rich features based on dependency parse graphs to
detect triggers and arguments. The proposed system
consists of four components: trigger detector, argument
detector, jointly inference with dual decomposition,
and rule-based semantic post-processing, and
outperforms the state-of-the-art systems. On the
development set of BioNLP'09, the F-score is 59.77
percent on the primary task, which is 0.96 percent
higher than the best system. On the test set of
BioNLP'11, the F-score is 56.09 and 0.89 percent higher
than the best published result that do not adopt
additional techniques. On the test set of BioNLP'13,
the F-score reaches 53.19 percent which is 2.22 percent
higher than the best result.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Guan:2016:EBH,
author = "Benjamin X. Guan and Bir Bhanu and Prue Talbot and
Nikki Jo-Hao Weng",
title = "Extraction of Blebs in Human Embryonic Stem Cell
Videos",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "678--688",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2480091",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Blebbing is an important biological indicator in
determining the health of human embryonic stem cells
hESC. Especially, areas of a bleb sequence in a video
are often used to distinguish two cell blebbing
behaviors in hESC: dynamic and apoptotic blebbings.
This paper analyzes various segmentation methods for
bleb extraction in hESC videos and introduces a
bio-inspired score function to improve the performance
in bleb extraction. Full bleb formation consists of
bleb expansion and retraction. Blebs change their size
and image properties dynamically in both processes and
between frames. Therefore, adaptive parameters are
needed for each segmentation method. A score function
derived from the change of bleb area and orientation
between consecutive frames is proposed which provides
adaptive parameters for bleb extraction in videos. In
comparison to manual analysis, the proposed method
provides an automated fast and accurate approach for
bleb sequence extraction.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Elmsallati:2016:GAP,
author = "Ahed Elmsallati and Connor Clark and Jugal Kalita",
title = "Global Alignment of Protein-Protein Interaction
Networks: a Survey",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "689--705",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2474391",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, we survey algorithms that perform
global alignment of networks or graphs. Global network
alignment aligns two or more given networks to find the
best mapping from nodes in one network to nodes in
other networks. Since graphs are a common method of
data representation, graph alignment has become
important with many significant applications.
Protein-protein interactions can be modeled as networks
and aligning these networks of protein interactions has
many applications in biological research. In this
survey, we review algorithms for global pairwise
alignment highlighting various proposed approaches, and
classify them based on their methodology. Evaluation
metrics that are used to measure the quality of the
resulting alignments are also surveyed. We discuss and
present a comparison between selected aligners on the
same datasets and evaluate using the same evaluation
metrics. Finally, a quick overview of the most popular
databases of protein interaction networks is presented
focusing on datasets that have been used recently.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wan:2016:MMP,
author = "Shibiao Wan and Man-Wai Mak and Sun-Yuan Kung",
title = "{Mem-mEN}: Predicting Multi-Functional Types of
Membrane Proteins by Interpretable Elastic Nets",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "706--718",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2474407",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Membrane proteins play important roles in various
biological processes within organisms. Predicting the
functional types of membrane proteins is indispensable
to the characterization of membrane proteins. Recent
studies have extended to predicting single- and
multi-type membrane proteins. However, existing
predictors perform poorly and more importantly, they
are often lack of interpretability. To address these
problems, this paper proposes an efficient predictor,
namely Mem-mEN, which can produce sparse and
interpretable solutions for predicting membrane
proteins with single- and multi-label functional types.
Given a query membrane protein, its associated gene
ontology GO information is retrieved by searching a
compact GO-term database with its homologous accession
number, which is subsequently classified by a
multi-label elastic net EN classifier. Experimental
results show that Mem-mEN significantly outperforms
existing state-of-the-art membrane-protein predictors.
Moreover, by using Mem-mEN, 338 out of more than 7,900
GO terms are found to play more essential roles in
determining the functional types. Based on these 338
essential GO terms, Mem-mEN can not only predict the
functional type of a membrane protein, but also explain
why it belongs to that type. For the reader's
convenience, the Mem-mEN server is available online at
http://bioinfo.eie.polyu.edu.hk/MemmENServer/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dalton:2016:ORB,
author = "Lori A. Dalton",
title = "Optimal {ROC}-Based Classification and Performance
Analysis under {Bayesian} Uncertainty Models",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "719--729",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2465966",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Popular tools to evaluate classifier performance are
the false positive rate FPR, true positive rate TPR,
receiver operator characteristic ROC curve, and area
under the curve AUC. Typically, these quantities are
estimated from training data using simple resampling
and counting methods, which have been shown to perform
poorly when the sample size is small, as is typical in
many applications. This work takes a model-based
approach in classifier training and performance
analysis, where we assume the true population densities
are members of an uncertainty class of distributions.
Given a prior over the uncertainty class and data, we
form a posterior and derive optimal mean-squared-error
MSE FPR and TPR estimators, as well as the
sample-conditioned MSE performance of these estimators.
The theory also naturally leads to optimal ROC and AUC
estimators. Finally, we develop a Neyman--Pearson-based
approach to optimal classifier design, which maximizes
the estimated TPR for a given estimated FPR. These
tools are optimal over the uncertainty class of
distributions given the sample, and are available in
closed form or can be easily approximated for many
models. Applications are demonstrated on both synthetic
and real genomic data. MATLAB code and simulations
results are available in the online supplementary
material.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Azuma:2016:PAC,
author = "Shun-ichi Azuma and Katsuya Owaki and Nobuhiro
Shinohara and Toshiharu Sugie",
title = "Performance Analysis of Chemotaxis Controllers: Which
has Better Chemotaxis Controller, \bioname{Escherichia
coli} or \bioname{Paramecium caudatum}?",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "730--741",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2474397",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Chemotaxis is the biological phenomenon in which
organisms move to a more favorable location in an
environment with a chemical attractant or repellent.
Since chemotaxis is a typical example of the
environmental response of organisms, it is a
fundamental topic in biology and related fields. We
discuss the performance of the internal controllers
that generate chemotaxis. We first propose performance
indices to evaluate the controllers. Based on these
indices, we evaluate the performance of two controller
models of Escherichia coli and Paramecium caudatum. As
a result, it is disclosed that the E. coli-type
controller achieves chemotaxis quickly but roughly,
whereas the P. caudatum-type controller achieves it
slowly but precisely. This result will be a biological
contribution from a control theoretic point of view.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Marhon:2016:PPC,
author = "Sajid A. Marhon and Stefan C. Kremer",
title = "Prediction of Protein Coding Regions Using a
Wide-Range Wavelet Window Method",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "742--753",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2476789",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Prediction of protein coding regions is an important
topic in the field of genomic sequence analysis.
Several spectrum-based techniques for the prediction of
protein coding regions have been proposed. However, the
outstanding issue in most of the proposed techniques is
that these techniques depend on an
experimentally-selected, predefined value of the window
length. In this paper, we propose a new Wide-Range
Wavelet Window WRWW method for the prediction of
protein coding regions. The analysis of the proposed
wavelet window shows that its frequency response can
adapt its width to accommodate the change in the window
length so that it can allow or prevent frequencies
other than the basic frequency in the analysis of DNA
sequences. This feature makes the proposed window
capable of analyzing DNA sequences with a wide range of
the window lengths without degradation in the
performance. The experimental analysis of applying the
WRWW method and other spectrum-based methods to five
benchmark datasets has shown that the proposed method
outperforms other methods along a wide range of the
window lengths. In addition, the experimental analysis
has shown that the proposed method is dominant in the
prediction of both short and long exons.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhao:2016:PBN,
author = "Yun-Bo Zhao and J. Krishnan",
title = "Probabilistic {Boolean} Network Modelling and Analysis
Framework for {mRNA} Translation",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "754--766",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2478477",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "mRNA translation is a complex process involving the
progression of ribosomes on the mRNA, resulting in the
synthesis of proteins, and is subject to multiple
layers of regulation. This process has been modelled
using different formalisms, both stochastic and
deterministic. Recently, we introduced a Probabilistic
Boolean modelling framework for mRNA translation, which
possesses the advantage of tools for numerically exact
computation of steady state probability distribution,
without requiring simulation. Here, we extend this
model to incorporate both random sequential and
parallel update rules, and demonstrate its
effectiveness in various settings, including its
flexibility in accommodating additional static and
dynamic biological complexities and its role in
parameter sensitivity analysis. In these applications,
the results from the model analysis match those of
TASEP model simulations. Importantly, the proposed
modelling framework maintains the stochastic aspects of
mRNA translation and provides a way to exactly
calculate probability distributions, providing
additional tools of analysis in this context. Finally,
the proposed modelling methodology provides an
alternative approach to the understanding of the mRNA
translation process, by bridging the gap between
existing approaches, providing new analysis tools, and
contributing to a more robust platform for modelling
and understanding translation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chang:2016:RGR,
author = "Young Hwan Chang and Roel Dobbe and Palak Bhushan and
Joe W. Gray and Claire J. Tomlin",
title = "Reconstruction of Gene Regulatory Networks Based on
Repairing Sparse Low-Rank Matrices",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "767--777",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2465952",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "With the growth of high-throughput proteomic data, in
particular time series gene expression data from
various perturbations, a general question that has
arisen is how to organize inherently heterogeneous data
into meaningful structures. Since biological systems
such as breast cancer tumors respond differently to
various treatments, little is known about exactly how
these gene regulatory networks GRNs operate under
different stimuli. Challenges due to the lack of
knowledge not only occur in modeling the dynamics of a
GRN but also cause bias or uncertainties in identifying
parameters or inferring the GRN structure. This paper
describes a new algorithm which enables us to estimate
bias error due to the effect of perturbations and
correctly identify the common graph structure among
biased inferred graph structures. To do this, we
retrieve common dynamics of the GRN subject to various
perturbations. We refer to the task as ``repairing''
inspired by ``image repairing'' in computer vision. The
method can automatically correctly repair the common
graph structure across perturbed GRNs, even without
precise information about the effect of the
perturbations. We evaluate the method on synthetic data
sets and demonstrate an application to the DREAM data
sets and discuss its implications to experiment
design.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tang:2016:RMC,
author = "Yang Tang and Huijun Gao and Wei Du and Jianquan Lu
and Athanasios V. Vasilakos and J{\"u}rgen Kurths",
title = "Robust Multiobjective Controllability of Complex
Neuronal Networks",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "778--791",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2485226",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper addresses robust multiobjective
identification of driver nodes in the neuronal network
of a cat's brain, in which uncertainties in
determination of driver nodes and control gains are
considered. A framework for robust multiobjective
controllability is proposed by introducing interval
uncertainties and optimization algorithms. By
appropriate definitions of robust multiobjective
controllability, a robust nondominated sorting adaptive
differential evolution NSJaDE is presented by means of
the nondominated sorting mechanism and the adaptive
differential evolution JaDE. The simulation
experimental results illustrate the satisfactory
performance of NSJaDE for robust multiobjective
controllability, in comparison with six statistical
methods and two multiobjective evolutionary algorithms
MOEAs: nondominated sorting genetic algorithms II
NSGA-II and nondominated sorting composite differential
evolution. It is revealed that the existence of
uncertainties in choosing driver nodes and designing
control gains heavily affects the controllability of
neuronal networks. We also unveil that driver nodes
play a more drastic role than control gains in robust
controllability. The developed NSJaDE and obtained
results will shed light on the understanding of
robustness in controlling realistic complex networks
such as transportation networks, power grid networks,
biological networks, etc.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2016:MMH,
author = "Yifeng Li and Haifen Chen and Jie Zheng and Alioune
Ngom",
title = "The Max-Min High-Order Dynamic {Bayesian} Network for
Learning Gene Regulatory Networks with Time-Delayed
Regulations",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "792--803",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2474409",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Accurately reconstructing gene regulatory network GRN
from gene expression data is a challenging task in
systems biology. Although some progresses have been
made, the performance of GRN reconstruction still has
much room for improvement. Because many regulatory
events are asynchronous, learning gene interactions
with multiple time delays is an effective way to
improve the accuracy of GRN reconstruction. Here, we
propose a new approach, called Max-Min high-order
dynamic Bayesian network MMHO-DBN by extending the
Max-Min hill-climbing Bayesian network technique
originally devised for learning a Bayesian network's
structure from static data. Our MMHO-DBN can explicitly
model the time lags between regulators and targets in
an efficient manner. It first uses constraint-based
ideas to limit the space of potential structures, and
then applies search-and-score ideas to search for an
optimal HO-DBN structure. The performance of MMHO-DBN
to GRN reconstruction was evaluated using both
synthetic and real gene expression time-series data.
Results show that MMHO-DBN is more accurate than
current time-delayed GRN learning methods, and has an
intermediate computing performance. Furthermore, it is
able to learn long time-delayed relationships between
genes. We applied sensitivity analysis on our model to
study the performance variation along different
parameter settings. The result provides hints on the
setting of parameters of MMHO-DBN.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jian:2016:MKF,
author = "Ling Jian and Zhonghang Xia and Xinnan Niu and Xijun
Liang and Parimal Samir and Andrew J. Link",
title = "$ \ell_2 $ Multiple Kernel Fuzzy {SVM}-Based Data
Fusion for Improving Peptide Identification",
journal = j-TCBB,
volume = "13",
number = "4",
pages = "804--809",
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2480084",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Oct 8 09:42:35 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "SEQUEST is a database-searching engine, which
calculates the correlation score between observed
spectrum and theoretical spectrum deduced from protein
sequences stored in a flat text file, even though it is
not a relational and object-oriental repository.
Nevertheless, the SEQUEST score functions fail to
discriminate between true and false PSMs accurately.
Some approaches, such as PeptideProphet and Percolator,
have been proposed to address the task of
distinguishing true and false PSMs. However, most of
these methods employ time-consuming learning algorithms
to validate peptide assignments [1]. In this paper, we
propose a fast algorithm for validating peptide
identification by incorporating heterogeneous
information from SEQUEST scores and peptide digested
knowledge. To automate the peptide identification
process and incorporate additional information, we
employ $ \ell_2 $ multiple kernel learning MKL to
implement the current peptide identification task.
Results on experimental datasets indicate that compared
with state-of-the-art methods, i.e., PeptideProphet and
Percolator, our data fusing strategy has comparable
performance but reduces the running time
significantly.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2016:GES,
author = "Chao Wang and Hong Yu and Aili Wang and Kai Xia",
title = "Guest Editorial for Special Section on Big Data
Computing and Processing in Computational Biology and
Bioinformatics",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "810--811",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2581460",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The papers in this special section focus on big data
computing in the field of bioinformatics and
biocomputing. Big data has emerged as an important
application field which has shown its huge impact in
different scientific research domains. In particular,
the big data bioinformatics applications such as DNA
sequence analysis have posed significant challenges to
the state-of-the-art processing and computing systems.
With the growing explosive data scale, the collection,
storage, retrieval, processing, scheduling, and
visualization are key big data issues to be tackled. Up
to now, many researchers have been seeking high-level
parallelism using novel big data computing
architectures and processing mechanisms.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Moore:2016:EMF,
author = "Erin Jessica Moore and Thirmachos Bourlai",
title = "Expectation Maximization of Frequent Patterns, a
Specific, Local, Pattern-Based Biclustering Algorithm
for Biological Datasets",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "812--824",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2510011",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Currently, binary biclustering algorithms are too slow
and non-specific to handle biological datasets that
have a large number of attributes, which is essential
for the computational biology problem of microarray
analysis. Specialized computers may be needed to
execute an algorithm, and may fail to produce a
solution, due to its large resource needs. The
biclusters also include too many false positives, the
type I error, which hinders biological discovery. We
propose an algorithm that can analyze datasets with a
large attribute set at different densities, and can
operate on a laptop, which makes it accessible to
practitioners. EMFP produces biclusters that have a
very low Root Mean Squared Error and false positive
rate, with very few type II errors. Our binary
biclustering algorithm is a hybrid, axis-parallel,
pattern-based algorithm that finds multiple,
non-overlapping, near-constant, deterministic, binary
submatrices, with a variable confidence threshold, and
the novel use of local density comparisons versus the
standard global threshold. EMFP introduces a new, and
intuitive way to calculate internal measures for binary
biclustering methods. We also introduce a framework to
ease comparison with other algorithms, and compare to
both binary and general biclustering algorithms using
two real, and 80 synthetic databases.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2016:IGM,
author = "Ya Zhang and Ao Li and Chen Peng and Minghui Wang",
title = "Improve Glioblastoma Multiforme Prognosis Prediction
by Using Feature Selection and Multiple Kernel
Learning",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "825--835",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2551745",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Glioblastoma multiforme GBM is a highly aggressive
type of brain cancer with very low median survival. In
order to predict the patient's prognosis, researchers
have proposed rules to classify different glioma cancer
cell subtypes. However, survival time of different
subtypes of GBM is often various due to different
individual basis. Recent development in gene testing
has evolved classic subtype rules to more specific
classification rules based on single biomolecular
features. These classification methods are proven to
perform better than traditional simple rules in GBM
prognosis prediction. However, the real power behind
the massive data is still under covered. We believe a
combined prediction model based on more than one data
type could perform better, which will contribute
further to clinical treatment of GBM. The Cancer Genome
Atlas TCGA database provides huge dataset with various
data types of many cancers that enables us to inspect
this aggressive cancer in a new way. In this research,
we have improved GBM prognosis prediction accuracy
further by taking advantage of the minimum redundancy
feature selection method mRMR and Multiple Kernel
Machine MKL learning method. Our goal is to establish
an integrated model which could predict GBM prognosis
with high accuracy.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2016:IDA,
author = "Xiaoyi Xu and Minghui Wang",
title = "Inferring Disease Associated Phosphorylation Sites via
Random Walk on Multi-Layer Heterogeneous Network",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "836--844",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2498548",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "As protein phosphorylation plays an important role in
numerous cellular processes, many studies have been
undertaken to analyze phosphorylation-related
activities for drug design and disease treatment.
However, although progresses have been made in
illustrating the relationship between phosphorylation
and diseases, no existing method focuses on
disease-associated phosphorylation sites prediction. In
this work, we proposed a multi-layer heterogeneous
network model that makes use of the kinase information
to infer disease-phosphorylation site relationship and
implemented random walk on the heterogeneous network.
Experimental results reveal that multi-layer
heterogeneous network model with kinase layer is
superior in inferring disease-phosphorylation site
relationship when comparing with existing random walk
model and common used classification methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ling:2016:MTH,
author = "Cheng Ling and Tsuyoshi Hamada and Jingyang Gao and
Guoguang Zhao and Donghong Sun and Weifeng Shi",
title = "{MrBayes tgMC 3++}: a High Performance and
Resource-Efficient {GPU}-Oriented Phylogenetic Analysis
Method",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "845--854",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2495202",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "MrBayes is a widespread phylogenetic inference tool
harnessing empirical evolutionary models and Bayesian
statistics. However, the computational cost on the
likelihood estimation is very expensive, resulting in
undesirably long execution time. Although a number of
multi-threaded optimizations have been proposed to
speed up MrBayes, there are bottlenecks that severely
limit the GPU thread-level parallelism of likelihood
estimations. This study proposes a high performance and
resource-efficient method for GPU-oriented
parallelization of likelihood estimations. Instead of
having to rely on empirical programming, the proposed
novel decomposition storage model implements high
performance data transfers implicitly. In terms of
performance improvement, a speedup factor of up to 178
can be achieved on the analysis of simulated datasets
by four Tesla K40 cards. In comparison to the other
publicly available GPU-oriented MrBayes, the tgMC$^3$
++ method proposed herein outperforms the tgMC$^3$
v1.0, nMC$^3$ v2.1.1 and oMC$^3$ v1.00 methods by
speedup factors of up to 1.6, 1.9 and 2.9,
respectively. Moreover, tgMC$^3$ ++ supports more
evolutionary models and gamma categories, which
previous GPU-oriented methods fail to take into
analysis.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2016:MCS,
author = "Jingsong Zhang and Yinglin Wang and Chao Zhang and
Yongyong Shi",
title = "Mining Contiguous Sequential Generators in Biological
Sequences",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "855--867",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2495132",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The discovery of conserved sequential patterns in
biological sequences is essential to unveiling common
shared functions. Mining sequential generators as well
as mining closed sequential patterns can contribute to
a more concise result set than mining all sequential
patterns, especially in the analysis of big data in
bioinformatics. Previous studies have also presented
convincing arguments that the generator is preferable
to the closed pattern in inductive inference and
classification. However, classic sequential generator
mining algorithms, due to the lack of consideration on
the contiguous constraint along with the lower-closed
one, still pose a great challenge at spawning a large
number of inefficient and redundant patterns, which is
too huge for effective usage. Driven by some extensive
applications of patterns with contiguous feature, we
propose ConSgen, an efficient algorithm for discovering
contiguous sequential generators. It adopts the n-gram
model, called shingles, to generate potential frequent
subsequences and leverages several pruning techniques
to prune the unpromising parts of search space. And
then, the contiguous sequential generators are
identified by using the equivalence class-based
lower-closure checking scheme. Our experiments on both
DNA and protein data sets demonstrate the compactness,
efficiency, and scalability of ConSgen.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hua:2016:GSM,
author = "Keru Hua and Qin Yu and Ruiming Zhang",
title = "A Guaranteed Similarity Metric Learning Framework for
Biological Sequence Comparison",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "868--877",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2495186",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Similarity of sequences is a key mathematical notion
for Classification and Phylogenetic studies in Biology.
The distance and similarity between two sequence are
very important and widely studied. During the last
decades, Similaritydistance metric learning is one of
the hottest topics of machine learning/data mining as
well as their applications in the bioinformatics field.
It is feasible to introduce machine learning technology
to learn similarity metric from biological data. In
this paper, we propose a novel framework of guaranteed
similarity metric learning GMSL to perform alignment of
biology sequences in any feature vector space. It
introduces the $ \epsilon, \gamma, \tau $ -goodness
similarity theory to Mahalanobis metric learning. As a
theoretical guaranteed similarity metric learning
approach, GMSL guarantees that the learned similarity
function performs well in classification and
clustering. Our experiments on the most used datasets
demonstrate that our approach outperforms the
state-of-the-art biological sequences alignment methods
and other similarity metric learning algorithms in both
accuracy and stability.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Thorvaldsen:2016:MMF,
author = "Steinar Thorvaldsen",
title = "A Mutation Model from First Principles of the Genetic
Code",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "878--886",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2489641",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The paper presents a neutral Codons Probability
Mutations CPM model of molecular evolution and genetic
decay of an organism. The CPM model uses a Markov
process with a 20-dimensional state space of
probability distributions over amino acids. The
transition matrix of the Markov process includes the
mutation rate and those single point mutations
compatible with the genetic code. This is an
alternative to the standard Point Accepted Mutation PAM
and BLOcks of amino acid SUbstitution Matrix BLOSUM.
Genetic decay is quantified as a similarity between the
amino acid distribution of proteins from a group of
species on one hand, and the equilibrium distribution
of the Markov chain on the other. Amino acid data for
the eukaryote, bacterium, and archaea families are used
to illustrate how both the CPM and PAM models predict
their genetic decay towards the equilibrium value of 1.
A family of bacteria is studied in more detail. It is
found that warm environment organisms on average have a
higher degree of genetic decay compared to those
species that live in cold environments. The paper
addresses a new codon-based approach to quantify
genetic decay due to single point mutations compatible
with the genetic code. The present work may be seen as
a first approach to use codon-based Markov models to
study how genetic entropy increases with time in an
effectively neutral biological regime. Various
extensions of the model are also discussed.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hao:2016:NMU,
author = "Xiao-Hu Hao and Gui-Jun Zhang and Xiao-Gen Zhou and
Xu-Feng Yu",
title = "A Novel Method Using Abstract Convex Underestimation
in Ab-Initio Protein Structure Prediction for Guiding
Search in Conformational Feature Space",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "887--900",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2497226",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "To address the searching problem of protein
conformational space in ab-initio protein structure
prediction, a novel method using abstract convex
underestimation ACUE based on the framework of
evolutionary algorithm was proposed. Computing such
conformations, essential to associate structural and
functional information with gene sequences, is
challenging due to the high-dimensionality and rugged
energy surface of the protein conformational space. As
a consequence, the dimension of protein conformational
space should be reduced to a proper level. In this
paper, the high-dimensionality original conformational
space was converted into feature space whose dimension
is considerably reduced by feature extraction
technique. And, the underestimate space could be
constructed according to abstract convex theory. Thus,
the entropy effect caused by searching in the
high-dimensionality conformational space could be
avoided through such conversion. The tight lower bound
estimate information was obtained to guide the
searching direction, and the invalid searching area in
which the global optimal solution is not located could
be eliminated in advance. Moreover, instead of
expensively calculating the energy of conformations in
the original conformational space, the estimate value
is employed to judge if the conformation is worth
exploring to reduce the evaluation time, thereby making
computational cost lower and the searching process more
efficient. Additionally, fragment assembly and the
Monte Carlo method are combined to generate a series of
metastable conformations by sampling in the
conformational space. The proposed method provides a
novel technique to solve the searching problem of
protein conformational space. Twenty small-to-medium
structurally diverse proteins were tested, and the
proposed ACUE method was compared with It Fix, HEA,
Rosetta and the developed method LEDE without
underestimate information. Test results show that the
ACUE method can more rapidly and more efficiently
obtain the near-native protein structure.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2016:SBD,
author = "Peng Chen and ShanShan Hu and Jun Zhang and Xin Gao
and Jinyan Li and Junfeng Xia and Bing Wang",
title = "A Sequence-Based Dynamic Ensemble Learning System for
Protein Ligand-Binding Site Prediction",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "901--912",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2505286",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Background: Proteins have the fundamental ability to
selectively bind to other molecules and perform
specific functions through such interactions, such as
protein-ligand binding. Accurate prediction of protein
residues that physically bind to ligands is important
for drug design and protein docking studies. Most of
the successful protein-ligand binding predictions were
based on known structures. However, structural
information is not largely available in practice due to
the huge gap between the number of known protein
sequences and that of experimentally solved structures.
Results: This paper proposes a dynamic ensemble
approach to identify protein-ligand binding residues by
using sequence information only. To avoid problems
resulting from highly imbalanced samples between the
ligand-binding sites and non ligand-binding sites, we
constructed several balanced data sets and we trained a
random forest classifier for each of them. We
dynamically selected a subset of classifiers according
to the similarity between the target protein and the
proteins in the training data set. The combination of
the predictions of the classifier subset to each query
protein target yielded the final predictions. The
ensemble of these classifiers formed a sequence-based
predictor to identify protein-ligand binding sites.
Conclusions: Experimental results on two Critical
Assessment of protein Structure Prediction datasets and
the ccPDB dataset demonstrated that of our proposed
method compared favorably with the state-of-the-art.
Availability:
http://www2.ahu.edu.cn/pchen/web/LigandDSES.htm",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Disanto:2016:APN,
author = "Filippo Disanto and Noah A. Rosenberg",
title = "Asymptotic Properties of the Number of Matching
Coalescent Histories for Caterpillar-Like Families of
Species Trees",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "913--925",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2485217",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Coalescent histories provide lists of species tree
branches on which gene tree coalescences can take
place, and their enumerative properties assist in
understanding the computational complexity of
calculations central in the study of gene trees and
species trees. Here, we solve an enumerative problem
left open by Rosenberg IEEE/ACM Transactions on
Computational Biology and Bioinformatics 10: 1253-1262,
2013 concerning the number of coalescent histories for
gene trees and species trees with a matching labeled
topology that belongs to a generic caterpillar-like
family. By bringing a generating function approach to
the study of coalescent histories, we prove that for
any caterpillar-like family with seed tree $t$ , the
sequence $ h_{n_{n \ge 0}}$ describing the number of
matching coalescent histories of the $n$ th tree of the
family grows asymptotically as a constant multiple of
the Catalan numbers. Thus, $ h_n \sim \beta_t c_n$ ,
where the asymptotic constant $ \beta_t > 0$ depends on
the shape of the seed tree $t$. The result extends a
claim demonstrated only for seed trees with at most
eight taxa to arbitrary seed trees, expanding the set
of cases for which detailed enumerative properties of
coalescent histories can be determined. We introduce a
procedure that computes from $t$ the constant $
\beta_t$ as well as the algebraic expression for the
generating function of the sequence $ h_n_{n \ge 0}$.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nogueira:2016:BBW,
author = "David Nogueira and Pedro Tomas and Nuno Roma",
title = "{BowMapCL}: {Burrows--Wheeler} Mapping on Multiple
Heterogeneous Accelerators",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "926--938",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2495149",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The computational demand of exact-search procedures
has pressed the exploitation of parallel processing
accelerators to reduce the execution time of many
applications. However, this often imposes strict
restrictions in terms of the problem size and
implementation efforts, mainly due to their possibly
distinct architectures. To circumvent this limitation,
a new exact-search alignment tool BowMapCL based on the
Burrows--Wheeler Transform and FM-Index is presented.
Contrasting to other alternatives, BowMapCL is based on
a unified implementation using OpenCL, allowing the
exploitation of multiple and possibly different devices
e.g., NVIDIA, AMD/ATI, and Intel GPUs/APUs.
Furthermore, to efficiently exploit such heterogeneous
architectures, BowMapCL incorporates several techniques
to promote its performance and scalability, including
multiple buffering, work-queue task-distribution, and
dynamic load-balancing, together with index
partitioning, bit-encoding, and sampling. When compared
with state-of-the-art tools, the attained results
showed that BowMapCL using a single GPU is $ 2 \times $
to $ 7.5 \times $ faster than mainstream multi-threaded
CPU BWT-based aligners, like Bowtie, BWA, and SOAP2;
and up to $ 4 \times $ faster than the best performing
state-of-the-art GPU implementations namely, SOAP3 and
HPG-BWT. When multiple and completely distinct devices
are considered, BowMapCL efficiently scales the offered
throughput, ensuring a convenient load-balance of the
involved processing in the several distinct devices.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Shi:2016:OEM,
author = "Yan Shi and Jinglong Niu and Zhixin Cao and Maolin Cai
and Jian Zhu and Weiqing Xu",
title = "Online Estimation Method for Respiratory Parameters
Based on a Pneumatic Model",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "939--946",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2497225",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Mechanical ventilation is an important method to help
people breathe. Respiratory parameters of ventilated
patients are usually tracked for pulmonary diagnostics
and respiratory treatment assessment. In this paper, to
improve the estimation accuracy of respiratory
parameters, a pneumatic model for mechanical
ventilation was proposed. Furthermore, based on the
mathematical model, a recursive least-squares algorithm
was adopted to estimate the respiratory parameters.
Finally, through experimental and numerical study, it
was demonstrated that the proposed estimation method
was effective and the method can be used in pulmonary
diagnostics and treatment.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2016:PSS,
author = "Liqi Li and Jinhui Li and Weidong Xiao and Yongsheng
Li and Yufang Qin and Shiwen Zhou and Hua Yang",
title = "Prediction the Substrate Specificities of Membrane
Transport Proteins Based on Support Vector Machine and
Hybrid Features",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "947--953",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2495140",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Membrane transport proteins and their substrate
specificities play crucial roles in a variety of
cellular functions. Identifying the substrate
specificities of membrane transport proteins is closely
related to the protein-target interaction prediction,
drug design, membrane recruitment, and dysregulation
analysis. However, experimental methods to this aim are
time consuming, labor intensive, and costly. Therefore,
we proposed a novel method basing on support vector
machine SVM to predict substrate specificities of
membrane transport proteins by integrating features
from position-specific score matrix PSSM, PROFEAT, and
Gene Ontology GO. Finally, jackknife cross-validation
tests were adopted on a benchmark and independent
datasets to measure the performance of the proposed
method. The overall accuracy of 96.16 and 80.45 percent
were obtained for two datasets, which are higher from
2.12 to 20.44 percent than that by the state-of-the-art
tool. Comparison results indicate that the proposed
model is more reliable and efficient for accurate
prediction the substrate specificities of membrane
transport proteins.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Axenopoulos:2016:SSF,
author = "Apostolos Axenopoulos and Dimitrios Rafailidis and
Georgios Papadopoulos and Elias N. Houstis and Petros
Daras",
title = "Similarity Search of Flexible {$3$D} Molecules
Combining Local and Global Shape Descriptors",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "954--970",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2498553",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, a framework for shape-based similarity
search of 3D molecular structures is presented. The
proposed framework exploits simultaneously the
discriminative capabilities of a global, a local, and a
hybrid local-global shape feature to produce a
geometric descriptor that achieves higher retrieval
accuracy than each feature does separately. Global and
hybrid features are extracted using pairwise
computations of diffusion distances between the points
of the molecular surface, while the local feature is
based on accumulating pairwise relations among oriented
surface points into local histograms. The local
features are integrated into a global descriptor vector
using the bag-of-features approach. Due to the
intrinsic property of its constituting shape features
to be invariant to articulations of the 3D objects, the
framework is appropriate for similarity search of
flexible 3D molecules, while at the same time it is
also accurate in retrieving rigid 3D molecules. The
proposed framework is evaluated in flexible and rigid
shape matching of 3D protein structures as well as in
shape-based virtual screening of large ligand databases
with quite promising results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ang:2016:SUS,
author = "Jun Chin Ang and Andri Mirzal and Habibollah Haron and
Haza Nuzly Abdull Hamed",
title = "Supervised, Unsupervised, and Semi-Supervised Feature
Selection: a Review on Gene Selection",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "971--989",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2478454",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recently, feature selection and dimensionality
reduction have become fundamental tools for many data
mining tasks, especially for processing
high-dimensional data such as gene expression
microarray data. Gene expression microarray data
comprises up to hundreds of thousands of features with
relatively small sample size. Because learning
algorithms usually do not work well with this kind of
data, a challenge to reduce the data dimensionality
arises. A huge number of gene selection are applied to
select a subset of relevant features for model
construction and to seek for better cancer
classification performance. This paper presents the
basic taxonomy of feature selection, and also reviews
the state-of-the-art gene selection methods by grouping
the literatures into three categories: supervised,
unsupervised, and semi-supervised. The comparison of
experimental results on top 5 representative gene
expression datasets indicates that the classification
accuracy of unsupervised and semi-supervised feature
selection is competitive with supervised feature
selection.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2016:NMP,
author = "Yuanning Liu and Qi Zhao and Hao Zhang and Rui Xu and
Yang Li and Liyan Wei",
title = "A New Method to Predict {RNA} Secondary Structure
Based on {RNA} Folding Simulation",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "990--995",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2496347",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "RNA plays an important role in various biological
processes; hence, it is essential when determining the
functions of RNA to research its secondary structures.
So far, the accuracy of RNA secondary structure
prediction remains an area in need of improvement. This
paper presents a novel method for predicting RNA
secondary structure based on an RNA folding simulation
model. This model assumes that the process of RNA
folding from the random coil state to full structure is
staged and in every stage of folding, the final state
of an RNA is determined by the optimal combination of
helical regions, which are urgently essential to
dynamics of RNA formation. This paper proposes the
First Large Free Energy Difference FLED in order to
find the helical regions most urgently needed for
optimal final state formation among all the possible
helical regions. Tests on the datasets with known
structures from public databases demonstrate that our
method can outperform other current RNA secondary
structure prediction methods in terms of prediction
accuracy.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kwon:2016:DRA,
author = "Yung-Keun Kwon and Junil Kim and Kwang-Hyun Cho",
title = "Dynamical Robustness against Multiple Mutations in
Signaling Networks",
journal = j-TCBB,
volume = "13",
number = "5",
pages = "996--1002",
month = sep,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2495251",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Dec 30 16:19:30 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "It has been known that the robust behavior of a
cellular signaling network is strongly related to the
structural characteristics of the network, such as
connectivity, the number of feedback loops, and the
number of feed-forward loops. Previous studies proved
such relationships through dynamical simulations of
various random network models. Most of them, however,
focused on robustness against a single node mutation.
Considering that complex diseases such as cancer are
mostly caused by simultaneous dysfunction of multiple
genes, it is needed to investigate the robustness of a
network against multiple node mutations. In this paper,
we investigated the robustness of a network against
multiple node mutations through extensive simulations
on the basis of Boolean network models. We found that
the robustness against multiple mutations is, in most
cases, weaker than the robustness against a single node
mutation on average. Moreover, we found that the
robustness against multiple mutations is strongly
positively correlated with the robustness against
single mutation. The difference between the multiple-
and single-mutation robustness became larger as the
number of mutated nodes increased or the number of
nodes that are robust to single-mutation decreased. We
further found that a node of relatively large
connectivity or being involved with many feedback loops
tends to be non-robust against multiple mutations. This
finding is supported by the observation that poly-genic
disease genes have high connectivity and are involved
with a large number of feedback loops than mono-genic
disease genes in a human signaling network. Together,
our study shows that previous studies for a single node
mutation can be extended to understand the network
dynamics for multiple node mutations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhao:2016:DMS,
author = "Xing-Ming Zhao",
title = "Data Mining in Systems Biology",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1003--1003",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2617698",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tang:2016:NAF,
author = "Jian Tang and Shuigeng Zhou",
title = "A New Approach for Feature Selection from Microarray
Data Based on Mutual Information",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1004--1015",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2515582",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Mutual information MI is a powerful concept for
correlation-centric applications. It has been used for
feature selection from microarray gene expression data
in many works. One of the merits of MI is that, unlike
many other heuristic methods, it is based on a mature
theoretic foundation. When applied to microarray data,
however, it faces some challenges. First, due to the
large number of features i.e., genes present in
microarray data, the true distributions for the
expression values of some genes may be distorted by
noise. Second, evaluating inter-group mutual
information requires estimating multi-variate
distributions, which is quite difficult if not
impossible. To address these problems, in this paper,
we propose a new MI-based feature selection approach
for microarray data. Our approach relies on two
strategies: one is relevance boosting, which requires a
desirable feature to show substantially additional
relevance with class labeling beyond the already
selected features, the other is feature interaction
enhancing, which probabilistically compensates for
feature interaction missing from simple
aggregation-based evaluation. We justify our approach
from both theoretical perspective and experimental
results. We use a synthetic dataset to show the
statistical significance of the proposed strategies,
and real-life datasets to show the improved performance
of our approach over the existing methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yin:2016:EPT,
author = "Xi Yin and Ying-Ying Xu and Hong-Bin Shen",
title = "Enhancing the Prediction of Transmembrane $ \beta
$-Barrel Segments with Chain Learning and Feature
Sparse Representation",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1016--1026",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2528000",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Transmembrane $ \beta $-barrels TMBs are one important
class of membrane proteins that play crucial functions
in the cell. Membrane proteins are difficult wet-lab
targets of structural biology, which call for accurate
computational prediction approaches. Here, we developed
a novel method named MemBrain-TMB to predict the
spanning segments of transmembrane $ \beta $-barrel
from amino acid sequence. MemBrain-TMB is a statistical
machine learning-based model, which is constructed
using a new chain learning algorithm with input
features encoded by the image sparse representation
approach. We considered the relative status information
between neighboring residues for enhancing the
performance, and the matrix of features was translated
into feature image by sparse coding algorithm for noise
and dimension reduction. To deal with the diverse loop
length problem, we applied a dynamic threshold method,
which is particularly useful for enhancing the
recognition of short loops and tight turns. Our
experiments demonstrate that the new protocol designed
in MemBrain-TMB effectively helps improve prediction
performance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Qin:2016:IDA,
author = "Gui-Min Qin and Rui-Yi Li and Xing-Ming Zhao",
title = "Identifying Disease Associated {miRNAs} Based on
Protein Domains",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1027--1035",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2515608",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "MicroRNAs miRNAs are a class of small endogenous
non-coding genes, acting as regulators in the
post-transcriptional processes. Recently, the miRNAs
are found to be widely involved in different types of
diseases. Therefore, the identification of disease
associated miRNAs can help understand the mechanisms
that underlie the disease and identify new biomarkers.
However, it is not easy to identify the miRNAs related
to diseases due to its extensive involvements in
various biological processes. In this work, we present
a new approach to identify disease associated miRNAs
based on domains, the functional and structural blocks
of proteins. The results on real datasets demonstrate
that our method can effectively identify disease
related miRNAs with high precision.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wu:2016:NBM,
author = "Hao Wu and Lin Gao and Nikola K. Kasabov",
title = "Network-Based Method for Inferring Cancer Progression
at the Pathway Level from Cross-Sectional Mutation
Data",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1036--1044",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2520934",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Large-scale cancer genomics projects are providing a
wealth of somatic mutation data from a large number of
cancer patients. However, it is difficult to obtain
several samples with a temporal order from one patient
in evaluating the cancer progression. Therefore, one of
the most challenging problems arising from the data is
to infer the temporal order of mutations across many
patients. To solve the problem efficiently, we present
a Network-based method NetInf to Infer cancer
progression at the pathway level from cross-sectional
data across many patients, leveraging on the exclusive
property of driver mutations within a pathway and the
property of linear progression between pathways. To
assess the robustness of NetInf, we apply it on
simulated data with the addition of different levels of
noise. To verify the performance of NetInf, we apply it
to analyze somatic mutation data from three real cancer
studies with large number of samples. Experimental
results reveal that the pathways detected by NetInf
show significant enrichment. Our method reduces
computational complexity by constructing gene networks
without assigning the number of pathways, which also
provides new insights on the temporal order of somatic
mutations at the pathway level rather than at the gene
level.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fabris:2016:EEC,
author = "Fabio Fabris and Alex A. Freitas and Jennifer M. A.
Tullet",
title = "An Extensive Empirical Comparison of Probabilistic
Hierarchical Classifiers in Datasets of Ageing-Related
Genes",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1045--1058",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2505288",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This study comprehensively evaluates the performance
of five types of probabilistic hierarchical
classification methods used for predicting Gene
Ontology GO terms related to ageing. Of those tested, a
new hybrid of a Local Hierarchical Classifier LHC and
the Predictive Clustering Tree algorithm LHC-PCT had
the best predictive accuracy results. We also tested
the impact of two types of variations in most
hierarchical classification algorithms, namely: a
changing the base algorithm we tested Naive Bayes and
Support Vector Machines, and the impact of b using or
not the Correlation based Feature Selection CFS
algorithm in a pre-processing step. In total, we
evaluated the predictive performance of 17 variations
of hierarchical classifiers across 15 datasets of
ageing and longevity-related genes. We conclude that
the LHC-PCT algorithm ranks better across several tests
seven out of 12. In addition, we interpreted the models
generated by the PCT algorithm to show how hierarchical
classification algorithms can be used to extract
biological insights out of the ageing-related datasets
that we compiled.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2016:CGS,
author = "Dong Wang and Jin-Xing Liu and Ying-Lian Gao and
Chun-Hou Zheng and Yong Xu",
title = "Characteristic Gene Selection Based on Robust Graph
Regularized Non-Negative Matrix Factorization",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1059--1067",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2505294",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Many methods have been considered for gene selection
and analysis of gene expression data. Nonetheless,
there still exists the considerable space for improving
the explicitness and reliability of gene selection. To
this end, this paper proposes a novel method named
robust graph regularized non-negative matrix
factorization for characteristic gene selection using
gene expression data, which mainly contains two
aspects: Firstly, enforcing $ {L_{21}} $ -norm
minimization on error function which is robust to
outliers and noises in data points. Secondly, it
considers that the samples lie in low-dimensional
manifold which embeds in a high-dimensional ambient
space, and reveals the data geometric structure
embedded in the original data. To demonstrate the
validity of the proposed method, we apply it to gene
expression data sets involving various human normal and
tumor tissue samples and the results demonstrate that
the method is effective and feasible.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Raposo:2016:CAM,
author = "Adriano N. Raposo and Abel J. P. Gomes",
title = "Computational {$3$D} Assembling Methods for {DNA}: a
Survey",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1068--1085",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2510008",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "DNA encodes the genetic information of most living
beings, except viruses that use RNA. Unlike other types
of molecules, DNA is not usually described by its
atomic structure being instead usually described by its
base-pair sequence, i.e., the textual sequence of its
subsidiary molecules known as nucleotides adenine A,
cytosine C, guanine G, and thymine T. The
three-dimensional assembling of DNA molecules based on
its base-pair sequence has been, for decades, a topic
of interest for many research groups all over the
world. In this paper, we survey the major methods found
in the literature to assemble and visualize DNA
molecules from their base-pair sequences. We divided
these methods into three categories: predictive
methods, adaptive methods, and thermodynamic methods.
Predictive methods aim to predict a conformation of the
DNA from its base pair sequence, while the goal of
adaptive methods is to assemble DNA base-pairs
sequences along previously known conformations, as
needed in scenarios such as DNA Monte Carlo
simulations. Unlike these two geometric methods,
thermodynamic methods are energy-based and aim to
predict secondary structural motifs of DNA in cases
where hydrogen bonds between base pairs might be broken
because of temperature changes. We also present the
major software tools that implements predictive,
adaptive, and thermodynamic methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ray:2016:DCS,
author = "Sumanta Ray and Sanghamitra Bandyopadhyay",
title = "Discovering Condition Specific Topological Pattern
Changes in Coexpression Network: an Application to
{HIV-1} Progression",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1086--1099",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2505300",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The natural progression of HIV-1 begins with a short
acute retroviral syndrome which typically transit to
chronic and clinical latency stages and subsequently
progresses to a symptomatic, life-threatening
immunodeficiency disease known as AIDS. Microarray
analysis based on gene coexpression is widely used to
investigate the coregulation pattern of a group or
cluster of genes in a specific phenotype. Moreover, an
investigation on the topological patterns across
multiple phenotypes can facilitate the understanding of
stage specific infection pattern of HIV-1 virus. Here,
we develop a novel framework to identify topological
patterns of gene co-expression network and detect
changes of modular structure across different stages of
HIV progression. This is achieved by comparing the
topological and intramodular properties of HIV
infection modules. To capture the diversity in modular
structure, some topological, correlation based, and
eigengene based measures are utilized here. We have
applied a rank aggregation scheme to rank all the
modules to provide a good agreement between these
measures. Some novel transcription factors like
`FOXO1', `GATA3', `GFI1', `IRF1', `IRF7', `MAX',
`STAT1', `STAT3', `XBP1', and `YY1' that merge from the
modules show significant change in expression pattern
over HIV progression stages. Moreover, we have
performed an eigengene based analysis to reveal the
perturbation in modular structure across three stages
of HIV-1 progression.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Vilor-Tejedor:2016:EPM,
author = "Natalia Vilor-Tejedor and Juan R. Gonzalez and M. Luz
Calle",
title = "Efficient and Powerful Method for Combining
{$P$}-Values in Genome-Wide Association Studies",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1100--1106",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2509977",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The goal of Genome-wide Association Studies GWAS is
the identification of genetic variants, usually single
nucleotide polymorphisms SNPs, that are associated with
disease risk. However, SNPs detected so far with GWAS
for most common diseases only explain a small
proportion of their total heritability. Gene set
analysis GSA has been proposed as an alternative to
single-SNP analysis with the aim of improving the power
of genetic association studies. Nevertheless, most GSA
methods rely on expensive computational procedures that
make infeasible their implementation in GWAS. We
propose a new GSA method, referred as globalEVT, which
uses the extreme value theory to derive gene-level
p-values. GlobalEVT reduces dramatically the
computational requirements compared to other GSA
approaches. In addition, this new approach improves the
power by allowing different inheritance models for each
genetic variant as illustrated in the simulation study
performed and allows the existence of correlation
between the SNPs. Real data analysis of an
Attention-deficit/hyperactivity disorder ADHD study
illustrates the importance of using GSA approaches for
exploring new susceptibility genes. Specifically, the
globalEVT method is able to detect genes related to
Cyclophilin A like domain proteins which is known to
play an important role in the mechanisms of ADHD
development.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cheng:2016:EIS,
author = "Xiaoqing Cheng and Tomoya Mori and Yushan Qiu and
Wai-Ki Ching and Tatsuya Akutsu",
title = "Exact Identification of the Structure of a
Probabilistic {Boolean} Network from Samples",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1107--1116",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2505310",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We study the number of samples required to uniquely
determine the structure of a probabilistic Boolean
network PBN, where PBNs are probabilistic extensions of
Boolean networks. We show via theoretical analysis and
computational analysis that the structure of a PBN can
be exactly identified with high probability from a
relatively small number of samples for interesting
classes of PBNs of bounded indegree. On the other hand,
we also show that there exist classes of PBNs for which
it is impossible to uniquely determine the structure of
a PBN from samples.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gong:2016:GBN,
author = "Maoguo Gong and Zhenglin Peng and Lijia Ma and
Jiaxiang Huang",
title = "Global Biological Network Alignment by Using Efficient
Memetic Algorithm",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1117--1129",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2511741",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "High-throughput experimental screening techniques have
resulted in a large number of biological network data
such as protein-protein interactions PPI data. The
analysis of these data can enhance our understanding of
cellular processes. PPI network alignment is one of the
comparative analysis methods for analyzing biological
networks. Research on PPI networks can identify
conserved subgraphs and help us to understand
evolutionary trajectories across species. Some
evolutionary algorithms have been proposed for coping
with PPI network alignment, but most of them are
limited by the lower search efficiency due to the lack
of the priori knowledge. In this paper, we propose a
memetic algorithm, denoted as MeAlgn, to solve the
biological network alignment by optimizing an objective
function which introduces topological structure and
sequence similarities. MeAlign combines genetic
algorithm with a local search refinement. The genetic
algorithm is to find interesting alignment solution
regions, and the local search is to find optimal
solutions around the regions. The proposed algorithm
first develops a coarse similarity score matrix for
initialization and then it uses a specific neighborhood
heuristic local search strategy to find an optimal
alignment. In MeAlign, the information of topological
structure and sequence similarities is used to guide
our mapping. Experimental results demonstrate that our
algorithm can achieve a better mapping than some
state-of-the-art algorithms and it makes a better
balance between the network topology and nodes sequence
similarities.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ray:2016:IAC,
author = "Meredith Ray and Jian Kang and Hongmei Zhang",
title = "Identifying Activation Centers with Spatial {Cox}
Point Processes Using {fMRI} Data",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1130--1141",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2510007",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We developed a Bayesian clustering method to identify
significant regions of brain activation.
Coordinate-based meta data originating from functional
magnetic resonance imaging fMRI were of primary
interest. Individual fMRI has the ability to measure
the intensity of blood flow and oxygen to a location
within the brain that was activated by a given thought
or emotion. The proposed method performed clustering on
two levels, latent foci center and study activation
center, with a spatial Cox point process utilizing the
Dirichlet process to describe the distribution of foci.
Intensity was modeled as a function of distance between
the focus and the center of the cluster of foci using a
Gaussian kernel. Simulation studies were conducted to
evaluate the sensitivity and robustness of the method
with respect to cluster identification and underlying
data distributions. We applied the method to a meta
data set to identify emotion foci centers.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2016:KBP,
author = "Hong Wang and Xicheng Wang and Zheng Li and Keqiu Li",
title = "Kriging-Based Parameter Estimation Algorithm for
Metabolic Networks Combined with Single-Dimensional
Optimization and Dynamic Coordinate Perturbation",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1142--1154",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2505291",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The metabolic network model allows for an in-depth
insight into the molecular mechanism of a particular
organism. Because most parameters of the metabolic
network cannot be directly measured, they must be
estimated by using optimization algorithms. However,
three characteristics of the metabolic network model,
i.e., high nonlinearity, large amount parameters, and
huge variation scopes of parameters, restrict the
application of many traditional optimization
algorithms. As a result, there is a growing demand to
develop efficient optimization approaches to address
this complex problem. In this paper, a Kriging-based
algorithm aiming at parameter estimation is presented
for constructing the metabolic networks. In the
algorithm, a new infill sampling criterion, named
expected improvement and mutual information EI\&MI, is
adopted to improve the modeling accuracy by selecting
multiple new sample points at each cycle, and the
domain decomposition strategy based on the principal
component analysis is introduced to save computing
time. Meanwhile, the convergence speed is accelerated
by combining a single-dimensional optimization method
with the dynamic coordinate perturbation strategy when
determining the new sample points. Finally, the
algorithm is applied to the arachidonic acid metabolic
network to estimate its parameters. The obtained
results demonstrate the effectiveness of the proposed
algorithm in getting precise parameter values under a
limited number of iterations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wei:2016:MDD,
author = "Guanyun Wei and Sheng Qin and Wenjuan Li and Liming
Chen and Fei Ma",
title = "{MDTE DB}: a Database for {MicroRNAs} Derived from
Transposable Element",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1155--1160",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2511767",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "MicroRNAs miRNAs are crucial regulators of gene
expression at post-transcriptional level. Understanding
origin and evolution of miRNAs will improve the current
available algorithm for the prediction of novel miRNAs
and their functions. Transposable elements TEs provide
a natural mechanism for the origin of new miRNAs. In
the paper, 2,583 miRNAs derived from TEs MDTEs were
collected to construct a database named MDTE database
MDTE DB for storing, searching, and analyzing MDTEs.
The database provides a convenient source for studying
the origin and evolution of miRNAs. Database URL:
http://bioinf.njnu.edu.cn/MDTE/MDTE.php.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cheng:2016:MDL,
author = "Shuang Cheng and Maozu Guo and Chunyu Wang and Xiaoyan
Liu and Yang Liu and Xuejian Wu",
title = "{MiRTDL}: a Deep Learning Approach for {miRNA} Target
Prediction",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1161--1169",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2510002",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "MicroRNAs miRNAs regulate genes that are associated
with various diseases. To better understand miRNAs, the
miRNA regulatory mechanism needs to be investigated and
the real targets identified. Here, we present miRTDL, a
new miRNA target prediction algorithm based on
convolutional neural network CNN. The CNN automatically
extracts essential information from the input data
rather than completely relying on the input dataset
generated artificially when the precise miRNA target
mechanisms are poorly known. In this work, the
constraint relaxing method is first used to construct a
balanced training dataset to avoid inaccurate
predictions caused by the existing unbalanced dataset.
The miRTDL is then applied to 1,606 experimentally
validated miRNA target pairs. Finally, the results show
that our miRTDL outperforms the existing target
prediction algorithms and achieves significantly higher
sensitivity, specificity and accuracy of 88.43, 96.44,
and 89.98 percent, respectively. We also investigate
the miRNA target mechanism, and the results show that
the complementation features are more important than
the others.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Qi:2016:PEP,
author = "Yi Qi and Jiawei Luo",
title = "Prediction of Essential Proteins Based on Local
Interaction Density",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1170--1182",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2509989",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Prediction of essential proteins which is aided by
computer science and supported from high throughput
data is a more efficient method compared with time
consuming and expensive experimental approaches. There
are many computational approaches reported, however
they are usually sensitive to various network
structures so that their robustness are generally poor.
In this paper, a novel topological centrality measure
for predicting essential proteins based on local
interaction density, named as LID, is proposed. It is
different from previous measures that LID takes the
essentiality of a node from interaction densities among
its neighbors through topological analyses of real
proteins in a protein complex set first time at the
viewpoint of biological modules. LID is applied to four
different yeast protein interaction networks, which are
obtained, respectively, from the DIP database and the
BioGRID database. The experimental results show that
the number of essential proteins detected by LID
universally exceeds or approximates the best
performance of other 10 topological centrality measures
in all 24 comparisons of four networks: DC, BC,
ClusterC, CloseC, MNC, SoECCNC, LAC, SC, EigC, and
InfoC. The better robustness of LID for multiple data
sets will make it to be a new core topological
centrality measure to improve the performance of
prediction for more species protein interaction
networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ancherbak:2016:TDG,
author = "Sergiy Ancherbak and Ercan E. Kuruoglu and Martin
Vingron",
title = "Time-Dependent Gene Network Modelling by Sequential
{Monte Carlo}",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1183--1193",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2496301",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Most existing methods used for gene regulatory network
modeling are dedicated to inference of steady state
networks, which are prevalent over all time instants.
However, gene interactions evolve over time.
Information about the gene interactions in different
stages of the life cycle of a cell or an organism is of
high importance for biology. In the statistical
graphical models literature, one can find a number of
methods for studying steady-state network structures
while the study of time varying networks is rather
recent. A sequential Monte Carlo method, namely
particle filtering PF, provides a powerful tool for
dynamic time series analysis. In this work, the PF
technique is proposed for dynamic network inference and
its potentials in time varying gene expression data
tracking are demonstrated. The data used for validation
are synthetic time series data available from the
DREAM4 challenge, generated from known network
topologies and obtained from transcriptional regulatory
networks of S. cerevisiae. We model the gene
interactions over the course of time with multivariate
linear regressions where the parameters of the
regressive process are changing over time.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2016:DDS,
author = "Yang Liu and Bowen Li and Jungang Lou",
title = "Disturbance Decoupling of Singular {Boolean} Control
Networks",
journal = j-TCBB,
volume = "13",
number = "6",
pages = "1194--1200",
month = nov,
year = "2016",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2509969",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 13 12:30:49 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper investigates the controller designing for
disturbance decoupling problem DDP of singular Boolean
control networks SBCNs. Using semi-tensor product STP
of matrices and the Implicit Function Theorem, a SBCN
is converted into the standard BCN. Based on the
redundant variable separation technique, both state
feedback and output feedback controllers are designed
to solve the DDP of the SBCN. Sufficient conditions are
also given to analyze the invariance of controllers
concerning the DDP of the SBCN with function
perturbation. Two illustrative examples are presented
to support the effectiveness of these obtained
results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tan:2017:ESS,
author = "Ying Tan and Yuhui Shi",
title = "Editorial: Special Section on Bio-Inspired Swarm
Computing and Engineering",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "1--3",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2566438",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Presents the introductory editorial for this issue of
the publication.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Niu:2017:SBA,
author = "Ben Niu and Huali Huang and Lijing Tan and Qiqi Duan",
title = "Symbiosis-Based Alternative Learning Multi-Swarm
Particle Swarm Optimization",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "4--14",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2459690",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Inspired by the ideas from the mutual cooperation of
symbiosis in natural ecosystem, this paper proposes a
new variant of PSO, named Symbiosis-based Alternative
Learning Multi-swarm Particle Swarm Optimization
SALMPSO. A learning probability to select one exemplar
out of the center positions, the local best position,
and the historical best position including the
experience of internal and external multiple swarms, is
used to keep the diversity of the population. Two
different levels of social interaction within and
between multiple swarms are proposed. In the search
process, particles not only exchange social experience
with others that are from their own sub-swarms, but
also are influenced by the experience of particles from
other fellow sub-swarms. According to the different
exemplars and learning strategy, this model is
instantiated as four variants of SALMPSO and a set of
15 test functions are conducted to compare with some
variants of PSO including 10, 30 and 50 dimensions,
respectively. Experimental results demonstrate that the
alternative learning strategy in each SALMPSO version
can exhibit better performance in terms of the
convergence speed and optimal values on most multimodal
functions in our simulation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mo:2017:NMB,
author = "Hongwei Mo and Lili Liu and Jiao Zhao",
title = "A New Magnetotactic Bacteria Optimization Algorithm
Based on Moment Migration",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "15--26",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2453949",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Magnetotactic bacteria is a kind of polyphyletic group
of prokaryotes with the characteristics of magnetotaxis
that make them orient and swim along geomagnetic field
lines. Its distinct biology characteristics are useful
to design new optimization technology. In this paper, a
new bionic optimization algorithm named Magnetotactic
Bacteria Moment Migration Algorithm MBMMA is proposed.
In the proposed algorithm, the moments of a chain of
magnetosomes are considered as solutions. The moments
of relative good solutions can migrate each other to
enhance the diversity of the MBMMA. It is compared with
variants of PSO on standard functions problems. The
experiment results show that the MBMMA is effective in
solving optimization problems. It shows better or
competitive performance compared with the variants of
PSO on most of the tested functions in this paper.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zheng:2017:CFF,
author = "Shaoqiu Zheng and Junzhi Li and Andreas Janecek and
Ying Tan",
title = "A Cooperative Framework for Fireworks Algorithm",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "27--41",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2497227",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper presents a cooperative framework for
fireworks algorithm CoFFWA. A detailed analysis of
existing fireworks algorithm FWA and its recently
developed variants has revealed that $i$ the current
selection strategy has the drawback that the
contribution of the firework with the best fitness
denoted as core firework overwhelms the contributions
of all other fireworks non-core fireworks in the
explosion operator, $ i i$ the Gaussian mutation
operator is not as effective as it is designed to be.
To overcome these limitations, the CoFFWA is proposed,
which significantly improves the exploitation
capability by using an independent selection method and
also increases the exploration capability by
incorporating a crowdness-avoiding cooperative strategy
among the fireworks. Experimental results on the
CEC2013 benchmark functions indicate that CoFFWA
outperforms the state-of-the-art FWA variants,
artificial bee colony, differential evolution, and the
standard particle swarm optimization SPSO2007/SPSO2011
in terms of convergence performance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2017:FAE,
author = "Bei Zhang and Yu-Jun Zheng and Min-Xia Zhang and
Sheng-Yong Chen",
title = "Fireworks Algorithm with Enhanced Fireworks
Interaction",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "42--55",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2446487",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "As a relatively new metaheuristic in swarm
intelligence, fireworks algorithm FWA has exhibited
promising performance on a wide range of optimization
problems. This paper aims to improve FWA by enhancing
fireworks interaction in three aspects: 1 Developing a
new Gaussian mutation operator to make sparks learn
from more exemplars; 2 Integrating the regular
explosion operator of FWA with the migration operator
of biogeography-based optimization BBO to increase
information sharing; 3 Adopting a new population
selection strategy that enables high-quality solutions
to have high probabilities of entering the next
generation without incurring high computational cost.
The combination of the three strategies can
significantly enhance fireworks interaction and thus
improve solution diversity and suppress premature
convergence. Numerical experiments on the CEC 2015
single-objective optimization test problems show the
effectiveness of the proposed algorithm. The
application to a high-speed train scheduling problem
also demonstrates its feasibility in real-world
optimization problems.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cheng:2017:EAB,
author = "Shan Cheng and Long-Long Zhao and Xiao-Yu Jiang",
title = "An Effective Application of Bacteria Quorum Sensing
and Circular Elimination in {MOPSO}",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "56--63",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2446484",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, an approach that incorporates a
turbulence mechanism and a circular elimination
strategy is presented to strengthen the performance of
multi-objective particle swarm optimization MOPSO. For
convergence enhancement, the turbulence mechanism
derived from bacteria quorum sensing behavior is
introduced to MOPSO to preserve the swarm diversity.
Meanwhile, the circular elimination strategy is used to
select particles for next iteration for better
distribution of the Pareto-optimal solutions. The
improved MOPSO algorithm has been tested on a set of
benchmark functions and compared with representative
multi-objective optimization algorithms. Simulation
results illustrate that the algorithm outperforms the
other algorithms on convergence while keep good spread
performance, and could be used as an effective global
optimization tool.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2017:MOP,
author = "Yong Zhang and Dun-wei Gong and Jian Cheng",
title = "Multi-Objective Particle Swarm Optimization Approach
for Cost-Based Feature Selection in Classification",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "64--75",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2476796",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Feature selection is an important data-preprocessing
technique in classification problems such as
bioinformatics and signal processing. Generally, there
are some situations where a user is interested in not
only maximizing the classification performance but also
minimizing the cost that may be associated with
features. This kind of problem is called cost-based
feature selection. However, most existing feature
selection approaches treat this task as a
single-objective optimization problem. This paper
presents the first study of multi-objective particle
swarm optimization PSO for cost-based feature selection
problems. The task of this paper is to generate a
Pareto front of nondominated solutions, that is,
feature subsets, to meet different requirements of
decision-makers in real-world applications. In order to
enhance the search capability of the proposed
algorithm, a probability-based encoding technology and
an effective hybrid operator, together with the ideas
of the crowding distance, the external archive, and the
Pareto domination relationship, are applied to PSO. The
proposed PSO-based multi-objective feature selection
algorithm is compared with several multi-objective
feature selection algorithms on five benchmark
datasets. Experimental results show that the proposed
algorithm can automatically evolve a set of
nondominated solutions, and it is a highly competitive
feature selection method for solving cost-based feature
selection problems.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ni:2017:NCH,
author = "Qingjian Ni and Qianqian Pan and Huimin Du and Cen Cao
and Yuqing Zhai",
title = "A Novel Cluster Head Selection Algorithm Based on
Fuzzy Clustering and Particle Swarm Optimization",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "76--84",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2446475",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "An important objective of wireless sensor network is
to prolong the network life cycle, and topology control
is of great significance for extending the network life
cycle. Based on previous work, for cluster head
selection in hierarchical topology control, we propose
a solution based on fuzzy clustering preprocessing and
particle swarm optimization. More specifically, first,
fuzzy clustering algorithm is used to initial
clustering for sensor nodes according to geographical
locations, where a sensor node belongs to a cluster
with a determined probability, and the number of
initial clusters is analyzed and discussed.
Furthermore, the fitness function is designed
considering both the energy consumption and distance
factors of wireless sensor network. Finally, the
cluster head nodes in hierarchical topology are
determined based on the improved particle swarm
optimization. Experimental results show that, compared
with traditional methods, the proposed method achieved
the purpose of reducing the mortality rate of nodes and
extending the network life cycle.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Han:2017:GSM,
author = "Fei Han and Chun Yang and Ya-Qi Wu and Jian-Sheng Zhu
and Qing-Hua Ling and Yu-Qing Song and De-Shuang
Huang",
title = "A Gene Selection Method for Microarray Data Based on
Binary {PSO} Encoding Gene-to-Class Sensitivity
Information",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "85--96",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2465906",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Traditional gene selection methods for microarray data
mainly considered the features' relevance by evaluating
their utility for achieving accurate predication or
exploiting data variance and distribution, and the
selected genes were usually poorly explicable. To
improve the interpretability of the selected genes as
well as prediction accuracy, an improved gene selection
method based on binary particle swarm optimization BPSO
and prior information is proposed in this paper. In the
proposed method, BPSO encoding gene-to-class
sensitivity GCS information is used to perform gene
selection. The gene-to-class sensitivity information,
extracted from the samples by extreme learning machine
ELM, is encoded into the selection process in four
aspects: initializing particles, updating the
particles, modifying maximum velocity, and adopting
mutation operation adaptively. Constrained by the
gene-to-class sensitivity information, the new method
can select functional gene subsets which are
significantly sensitive to the samples' classes. With
the few discriminative genes selected by the proposed
method, ELM, K-nearest neighbor and support vector
machine classifiers achieve much high prediction
accuracy on five public microarray data, which in turn
verifies the efficiency and effectiveness of the
proposed gene selection method.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2017:TDP,
author = "Bo Zhang and Haibin Duan",
title = "Three-Dimensional Path Planning for Uninhabited Combat
Aerial Vehicle Based on Predator-Prey Pigeon-Inspired
Optimization in Dynamic Environment",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "97--107",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2443789",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Three-dimension path planning of uninhabited combat
aerial vehicle UCAV is a complicated optimal problem,
which mainly focused on optimizing the flight route
considering the different types of constrains under
complex combating environment. A novel predator-prey
pigeon-inspired optimization PPPIO is proposed to solve
the UCAV three-dimension path planning problem in
dynamic environment. Pigeon-inspired optimization PIO
is a new bio-inspired optimization algorithm. In this
algorithm, map and compass operator model and landmark
operator model are used to search the best result of a
function. The prey-predator concept is adopted to
improve global best properties and enhance the
convergence speed. The characteristics of the optimal
path are presented in the form of a cost function. The
comparative simulation results show that our proposed
PPPIO algorithm is more efficient than the basic PIO,
particle swarm optimization PSO, and different
evolution DE in solving UCAV three-dimensional path
planning problems.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2017:SNH,
author = "Yuxin Liu and Chao Gao and Zili Zhang and Yuxiao Lu
and Shi Chen and Mingxin Liang and Li Tao",
title = "Solving {NP}-Hard Problems with
\bioname{Physarum}-Based Ant Colony System",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "108--120",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2462349",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "NP-hard problems exist in many real world
applications. Ant colony optimization ACO algorithms
can provide approximate solutions for those NP-hard
problems, but the performance of ACO algorithms is
significantly reduced due to premature convergence and
weak robustness, etc. With these observations in mind,
this paper proposes a \bioname{Physarum}-based
pheromone matrix optimization strategy in ant colony
system ACS for solving NP-hard problems such as
traveling salesman problem TSP and 0/1 knapsack problem
0/1 KP. In the \bioname{Physarum}-inspired mathematical
model, one of the unique characteristics is that
critical tubes can be reserved in the process of
network evolution. The optimized updating strategy
employs the unique feature and accelerates the positive
feedback process in ACS, which contributes to the quick
convergence of the optimal solution. Some experiments
were conducted using both benchmark and real datasets.
The experimental results show that the optimized ACS
outperforms other meta-heuristic algorithms in accuracy
and robustness for solving TSPs. Meanwhile, the
convergence rate and robustness for solving 0/1 KPs are
better than those of classical ACS.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Czeizler:2017:GTA,
author = "Elena Czeizler and Tommi Hirvola and Kalle Karhu",
title = "A Graph-Theoretical Approach for {Motif} Discovery in
Protein Sequences",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "121--130",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2511750",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Motif recognition is a challenging problem in
bioinformatics due to the diversity of protein motifs.
Many existing algorithms identify motifs of a given
length, thus being either not applicable or not
efficient when searching simultaneously for motifs of
various lengths. Searching for gapped motifs, although
very important, is a highly time-consuming task due to
the combinatorial explosion of possible combinations
implied by the consideration of long gaps. We introduce
a new graph theoretical approach to identify motifs of
various lengths, both with and without gaps. We compare
our approach with two widely used methods: MEME and
GLAM2 analyzing both the quality of the results and the
required computational time. Our method provides
results of a slightly higher level of quality than MEME
but at a much faster rate, i.e., one eighth of MEME's
query time. By using similarity indexing, we drop the
query times down to an average of approximately one
sixth of the ones required by GLAM2, while achieving a
slightly higher level of quality of the results. More
precisely, for sequence collections smaller than 50,000
bytes GLAM2 is 13 times slower, while being at least as
fast as our method on larger ones. The source code of
our C++ implementation is freely available in GitHub:
https://github.com/hirvolt1/debruijn-motif.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jamil:2017:VIQ,
author = "Hasan M. Jamil",
title = "A Visual Interface for Querying Heterogeneous
Phylogenetic Databases",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "131--144",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2520943",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Despite the recent growth in the number of
phylogenetic databases, access to these wealth of
resources remain largely tool or form-based interface
driven. It is our thesis that the flexibility afforded
by declarative query languages may offer the
opportunity to access these repositories in a better
way, and to use such a language to pose truly powerful
queries in unprecedented ways. In this paper, we
propose a substantially enhanced closed visual query
language, called PhyQL, that can be used to query
phylogenetic databases represented in a canonical form.
The canonical representation presented helps capture
most phylogenetic tree formats in a convenient way, and
is used as the storage model for our PhyloBase database
for which PhyQL serves as the query language. We have
implemented a visual interface for the end users to
pose PhyQL queries using visual icons, and drag and
drop operations defined over them. Once a query is
posed, the interface translates the visual query into a
Datalog query for execution over the canonical
database. Responses are returned as hyperlinks to
phylogenies that can be viewed in several formats using
the tree viewers supported by PhyloBase. Results cached
in PhyQL buffer allows secondary querying on the
computed results making it a truly powerful querying
architecture.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2017:CPP,
author = "Lin Zhang and Hui Liu and Yufei Huang and Xuesong Wang
and Yidong Chen and Jia Meng",
title = "Cancer Progression Prediction Using Gene Interaction
Regularized Elastic Net",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "145--154",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2511758",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Different types of genomic aberration may
simultaneously contribute to tumorigenesis. To obtain a
more accurate prognostic assessment to guide
therapeutic regimen choice for cancer patients, the
heterogeneous multi-omics data should be integrated
harmoniously, which can often be difficult. For this
purpose, we propose a Gene Interaction Regularized
Elastic Net GIREN model that predicts clinical outcome
by integrating multiple data types. GIREN conveniently
embraces both gene measurements and gene-gene
interaction information under an elastic net
formulation, enforcing structure sparsity, and the
``grouping effect'' in solution to select the
discriminate features with prognostic value. An
iterative gradient descent algorithm is also developed
to solve the model with regularized optimization. GIREN
was applied to human ovarian cancer and breast cancer
datasets obtained from The Cancer Genome Atlas,
respectively. Result shows that, the proposed GIREN
algorithm obtained more accurate and robust performance
over competing algorithms LASSO, Elastic Net, and
Semi-supervised PCA, with or without average pathway
expression features in predicting cancer progression on
both two datasets in terms of median area under curve
AUC and interquartile range IQR, suggesting a promising
direction for more effective integration of gene
measurement and gene interaction information.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hu:2017:ECF,
author = "Lun Hu and Keith C. C. Chan",
title = "Extracting Coevolutionary Features from Protein
Sequences for Predicting Protein--Protein
Interactions",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "155--166",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2520923",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Knowing the ways proteins interact with each other are
crucial to our understanding of the functional
mechanisms of proteins. It is for this reason that
different approaches have been developed in attempts to
predict protein-protein interactions PPIs
computationally. Among them, the sequence-based
approaches are preferred to the others as they do not
require any information about protein properties to
perform their tasks. Instead, most sequence-based
approaches make use of feature extraction methods to
extract features directly from protein sequences so
that for each protein sequence, we can construct a
feature vector. The feature vectors of every pair of
proteins are then concatenated to form two classes of
interacting and non-interacting proteins. The
prediction of whether or not two proteins interact with
each other is then formulated as a classification
problem. How accurate PPI predictions can be made
therefore depends on how good the features are that can
be extracted from the protein sequences to allow
interacting or non-interacting to be best
distinguished. To do so, instead of extracting such
features from individual protein sequences
independently of the other protein in the same pair, we
propose to jointly consider features from both
sequences in a protein pair during the feature
extraction process through using a novel coevolutionary
feature extraction approach called CoFex.
Coevolutionary features extracted by CoFex refer to the
covariations found at coevolving positions. Based on
the presence and absence of these coevolutionary
features in the sequences of two proteins, feature
vectors can be composed for pairs of proteins rather
than individual proteins. The experiment results show
that CoFex is a promising feature extraction approach
and can improve the performance of PPI prediction.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gangeh:2017:FSF,
author = "Mehrdad J. Gangeh and Hadi Zarkoob and Ali Ghodsi",
title = "Fast and Scalable Feature Selection for Gene
Expression Data Using {Hilbert--Schmidt} Independence
Criterion",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "167--181",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2631164",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Goal: In computational biology, selecting a small
subset of informative genes from microarray data
continues to be a challenge due to the presence of
thousands of genes. This paper aims at quantifying the
dependence between gene expression data and the
response variables and to identifying a subset of the
most informative genes using a fast and scalable
multivariate algorithm. Methods: A novel algorithm for
feature selection from gene expression data was
developed. The algorithm was based on the
Hilbert--Schmidt independence criterion HSIC, and was
partly motivated by singular value decomposition SVD.
Results: The algorithm is computationally fast and
scalable to large datasets. Moreover, it can be applied
to problems with any type of response variables
including, biclass, multiclass, and continuous response
variables. The performance of the proposed algorithm in
terms of accuracy, stability of the selected genes,
speed, and scalability was evaluated using both
synthetic and real-world datasets. The simulation
results demonstrated that the proposed algorithm
effectively and efficiently extracted stable genes with
high predictive capability, in particular for datasets
with multiclass response variables.
Conclusion/Significance: The proposed method does not
require the whole microarray dataset to be stored in
memory, and thus can easily be scaled to large
datasets. This capability is an important attribute in
big data analytics, where data can be large and
massively distributed.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hu:2017:GRS,
author = "Hailong Hu and Zhong Li and Hongwei Dong and Tianhe
Zhou",
title = "Graphical Representation and Similarity Analysis of
Protein Sequences Based on Fractal Interpolation",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "182--192",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2511731",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A new graphical representation of protein sequences is
introduced in this paper. Nine main physicochemical
properties of amino acids were used to obtain a 2D
discrete point set for protein sequences by applying
principal component analysis. The fractal method was
then employed to interpolate discrete points in
constructing a graphical representation of protein
sequences. Fractal dimension of the protein curve was
used to analyze the similarity of protein sequences by
comparing the distance of vectors representing segments
of protein sequences. The Jeffrey's and Matusita
distance was modified in the similarity comparison of
protein sequences with different lengths. Nine
different species from Nicotinamide adenine
dinucleotide NADH dehydrogenase 5 ND5 protein sequences
were tested as an example to demonstrate our method.
Finally, a linear correlation and significance analysis
was used to compare our results with other graphical
representations referring to the ClustalW result. To
confirm the validity of our method, eight species in
NADH dehydrogenase 6 ND6 protein families and
twenty-seven species in beta-globin protein families
were also analyzed. Experimental results show that the
proposed method is effective for the similarity
analysis of proteins.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bonnici:2017:VOS,
author = "Vincenzo Bonnici and Rosalba Giugno",
title = "On the Variable Ordering in Subgraph Isomorphism
Algorithms",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "193--203",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2515595",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Graphs are mathematical structures to model several
biological data. Applications to analyze them require
to apply solutions for the subgraph isomorphism
problem, which is NP-complete. Here, we investigate the
existing strategies to reduce the subgraph isomorphism
algorithm running time with emphasis on the importance
of the order with which the graph vertices are taken
into account during the search, called variable
ordering, and its incidence on the total running time
of the algorithms. We focus on two recent solutions,
which are based on an effective variable ordering
strategy. We discuss their comparison both with the
variable ordering strategies reviewed in the paper and
the other algorithms present in the ICPR2014 contest on
graph matching algorithms for pattern search in
biological databases.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sinha:2017:PDV,
author = "Arvind Kumar Sinha and Pradeep Singh and Anand Prakash
and Dharm Pal and Anuradha Dube and Awanish Kumar",
title = "Putative Drug and Vaccine Target Identification in
\bioname{Leishmania donovani} Membrane Proteins Using
Na{\"\i}ve {Bayes} Probabilistic Classifier",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "204--211",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2570217",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Predicting the role of protein is one of the most
challenging problems. There are few approaches
available for the prediction of role of unknown protein
in terms of drug target or vaccine candidate. We
propose here Na{\"\i}ve Bayes probabilistic classifier,
a promising method for reliable predictions. This
method is tested on the proteins identified in our mass
spectrometry based membrane protemics study of
\bioname{Leishmania donovani} parasite that causes a
fatal disease Visceral Leishmaniasis in humans all
around the world. Most of the vaccine/drug targets
belonging to membrane proteins are represented as key
players in the pathogenesis of \bioname{Leishmania}
infection. Analyses of our previous results, using
Na{\"\i}ve Bayes probabilistic classifier, indicate
that this method predicts the role of
unknown/hypothetical protein as drug target/vaccine
candidate significantly with higher precision. We have
employed this method in order to provide probabilistic
predictions of unknown/hypothetical proteins as
targets. This study reports the unknown/hypothetical
proteins of \bioname{Leishmania} membrane fraction as a
potential drug targets and vaccine candidate which is
vital information for this parasite. Future molecular
studies and characterization of these potent targets
may produce a recombinant therapeutic/prophylactic tool
against Visceral Leishmaniasis. These
unknown/hypothetical proteins may open a vast research
field to be exploited for novel treatment strategies.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wan:2017:TLM,
author = "Shibiao Wan and Man-Wai Mak and Sun-Yuan Kung",
title = "Transductive Learning for Multi-Label Protein
Subchloroplast Localization Prediction",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "212--224",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2527657",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Predicting the localization of chloroplast proteins at
the sub-subcellular level is an essential yet
challenging step to elucidate their functions. Most of
the existing subchloroplast localization predictors are
limited to predicting single-location proteins and
ignore the multi-location chloroplast proteins. While
recent studies have led to some multi-location
chloroplast predictors, they usually perform poorly.
This paper proposes an ensemble transductive learning
method to tackle this multi-label classification
problem. Specifically, given a protein in a dataset,
its composition-based sequence information and
profile-based evolutionary information are respectively
extracted. These two kinds of features are respectively
compared with those of other proteins in the dataset.
The comparisons lead to two similarity vectors which
are weighted-combined to constitute an ensemble feature
vector. A transductive learning model based on the
least squares and nearest neighbor algorithms is
proposed to process the ensemble features. We refer to
the resulting predictor as as EnTrans-Chlo.
Experimental results on a stringent benchmark dataset
and a novel dataset demonstrate that EnTrans-Chlo
significantly outperforms state-of-the-art predictors
and particularly gains more than 4 percent absolute
improvement on the overall actual accuracy. For
readers' convenience, EnTrans-Chlo is freely available
online at
http://bioinfo.eie.polyu.edu.hk/EnTransChloServer/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2017:AIB,
author = "Kuize Zhang and Lijun Zhang and Shaoshuai Mou",
title = "An Application of Invertibility of {Boolean} Control
Networks to the Control of the Mammalian Cell Cycle",
journal = j-TCBB,
volume = "14",
number = "1",
pages = "225--229",
month = jan,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2515600",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Mar 25 07:42:59 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In Faur{\'e} et al. 2006, the dynamics of the core
network regulating the mammalian cell cycle is
formulated as a Boolean control network BCN model
consisting of nine proteins as state nodes and a tenth
protein protein CycD as the control input node. In this
model, one of the state nodes, protein Cdc20, plays a
central role in the separation of sister chromatids.
Hence, if any Cdc20 sequence can be obtained, fully
controlling the mammalian cell cycle is feasible.
Motivated by this fact, we study whether any Cdc20
sequence can be obtained theoretically. We formulate
the foregoing problem as the invertibility of BCNs,
that is, whether one can obtain any Cdc20 sequence by
designing input i.e., protein CycD sequences. We give
an algorithm to verify the invertibility of any BCN,
and find that the BCN model for the core network
regulating the mammalian cell cycle is not invertible,
that is, one cannot obtain any Cdc20 sequence. We
further present another algorithm to test whether a
finite Cdc20 sequence can be generated by the BCN
model, which leads to a series of periodic infinite
Cdc20 sequences with alternately active and inactive
Cdc20 segments. States of these sequences are
alternated between the two attractors in the proposed
model, which reproduces correctly how a cell exits the
cell cycle to enter the quiescent state, or the
opposite.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2017:ENE,
author = "Aidong Zhang",
title = "Editorial from the New {Editor-in-Chief}",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "251--251",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2673898",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Presents the introductory editorial for this issue of
the publication.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yoo:2017:GES,
author = "Illhoi Yoo and Amarda Shehu",
title = "Guest Editorial for Special Section on {BIBM} 2014",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "252--253",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2567998",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The papers in this special section were presented at
the Eighth Annual IEEE International Conference on
Bioinformatics and Biomedicine BIBM 2014 held in
Belfast, UK, 2-5, November 2014.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lee:2017:DPD,
author = "En-Shiun Annie Lee and Ho-Yin Antonio Sze-To and
Man-Hon Wong and Kwong-Sak Leung and Terrence Chi-Kong
Lau and Andrew K. C. Wong",
title = "Discovering Protein-{DNA} Binding Cores by Aligned
Pattern Clustering",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "254--263",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2474376",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Understanding binding cores is of fundamental
importance in deciphering Protein-DNA TF-TFBS binding
and gene regulation. Limited by expensive experiments,
it is promising to discover them with variations
directly from sequence data. Although existing
computational methods have produced satisfactory
results, they are one-to-one mappings with no
site-specific information on residue/nucleotide
variations, where these variations in binding cores may
impact binding specificity. This study presents a new
representation for modeling binding cores by
incorporating variations and an algorithm to discover
them from only sequence data. Our algorithm takes
protein and DNA sequences from TRANSFAC a Protein-DNA
Binding Database as input; discovers from both sets of
sequences conserved regions in Aligned Pattern Clusters
APCs; associates them as Protein-DNA Co-Occurring APCs;
ranks the Protein-DNA Co-Occurring APCs according to
their co-occurrence, and among the top ones, finds
three-dimensional structures to support each binding
core candidate. If successful, candidates are verified
as binding cores. Otherwise, homology modeling is
applied to their close matches in PDB to attain new
chemically feasible binding cores. Our algorithm
obtains binding cores with higher precision and much
faster runtime $ \geq $ 1,600x than that of its
contemporaries, discovering candidates that do not
co-occur as one-to-one associated patterns in the raw
data. Availability:
http://www.pami.uwaterloo.ca/~ealee/files/tcbbPnDna2015/Release.zip.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2017:MVC,
author = "Yong Zhang and Xiaohua Hu and Xingpeng Jiang",
title = "Multi-View Clustering of Microbiome Samples by Robust
Similarity Network Fusion and Spectral Clustering",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "264--271",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2474387",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Microbiome datasets are often comprised of different
representations or views which provide complementary
information, such as genes, functions, and taxonomic
assignments. Integration of multi-view information for
clustering microbiome samples could create a
comprehensive view of a given microbiome study.
Similarity network fusion SNF can efficiently integrate
similarities built from each view of data into a unique
network that represents the full spectrum of the
underlying data. Based on this method, we develop a
Robust Similarity Network Fusion RSNF approach which
combines the strength of random forest and the
advantage of SNF at data aggregation. The experimental
results indicate the strength of the proposed strategy.
The method substantially improves the clustering
performance significantly comparing to several
state-of-the-art methods in several datasets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kifer:2017:OAD,
author = "Ilona Kifer and Rui M. Branca and Amir Ben-Dor and
Linhui Zhai and Ping Xu and Janne Lehtio and Zohar
Yakhini",
title = "Optimizing Analytical Depth and Cost Efficiency of
{IEF-LC\slash MS} Proteomics",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "272--281",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2452901",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "IEF LC-MS/MS is an analytical method that incorporates
a two-step sample separation prior to MS identification
of proteins. When analyzing complex samples this
preparatory separation allows for higher analytical
depth and improved quantification accuracy of proteins.
However, cost and analysis time are greatly increased
as each analyzed IEF fraction is separately profiled
using LC-MS/MS. We propose an approach that selects a
subset of IEF fractions for LC-MS/MS analysis that is
highly informative in the context of a group of
proteins of interest. Specifically, our method allows a
significant reduction in cost and instrument time as
compared to the standard protocol of running all
fractions, with little compromise to coverage. We
develop algorithmics to optimize the selection of the
IEF fractions on which to run LC-MS/MS. We translate
the fraction optimization task to Minimum Set Cover, a
well-studied NP-hard problem. We develop heuristic
solutions and compare them in terms of effectiveness
and running times. We provide examples to demonstrate
advantages and limitations of each algorithmic
approach. Finally, we test our methodology by applying
it to experimental data obtained from IEF LC-MS/MS
analysis of yeast and human samples. We demonstrate the
benefit of this approach for analyzing complex samples
with a focus on different protein sets of interest.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zheng:2017:AOI,
author = "Huiru Zheng and Chaoyang Wang and Haiying Wang",
title = "Analysis of Organization of the Interactome Using
Dominating Sets: a Case Study on Cell Cycle Interaction
Networks",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "282--289",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2459712",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this study, a minimum dominating set based approach
was developed and implemented as a Cytoscape plugin to
identify critical and redundant proteins in a protein
interaction network. We focused on the investigation of
the properties associated with critical proteins in the
context of the analysis of interaction networks
specific to cell cycle in both yeast and human. A total
of 132 yeast genes and 129 human proteins have been
identified as critical nodes while 950 in yeast and 980
in human have been categorized as redundant nodes. A
clear distinction between critical and redundant
proteins was observed when examining their topological
parameters including betweenness centrality, suggesting
a central role of critical proteins in the control of a
network. The significant differences in terms of gene
coexpression and functional similarity were observed
between the two sets of proteins in yeast. Critical
proteins were found to be enriched with essential genes
in both networks and have a more deleterious effect on
the network integrity than their redundant
counterparts. Furthermore, we obtained statistically
significant enrichments of proteins that govern human
diseases including cancer-related and virus-targeted
genes in the corresponding set of critical proteins.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Becker:2017:MTL,
author = "Matthias Becker and Nadia Magnenat-Thalmann",
title = "Muscle Tissue Labeling of Human Lower Limb in
Multi-Channel {mDixon MR} Imaging: Concepts and
Applications",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "290--299",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2459679",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "With increasing resolutions and number of
acquisitions, medical imaging more and more requires
computer support for interpretation as currently not
all imaging data is fully used. In our work, we show
how multi-channel images can be used for robust air
masking and reliable muscle tissue detection in the
human lower limb. We exploit additional channels that
are usually discarded in clinical routine. We use the
common mDixon acquisition protocol for MR imaging. A
series of thresholding, morphological, and connectivity
operations is used for processing. We demonstrate our
fully automated approach on four subjects and present a
comparison with manual labeling. We discuss how this
work is used for advanced and intuitive visualization,
the quantification of tissue types, pose estimation,
initialization of further segmentation methods, and how
it could be used in clinical environments.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Veltri:2017:IRA,
author = "Daniel Veltri and Uday Kamath and Amarda Shehu",
title = "Improving Recognition of Antimicrobial Peptides and
Target Selectivity through Machine Learning and Genetic
Programming",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "300--313",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2462364",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Growing bacterial resistance to antibiotics is
spurring research on utilizing naturally-occurring
antimicrobial peptides AMPs as templates for novel drug
design. While experimentalists mainly focus on
systematic point mutations to measure the effect on
antibacterial activity, the computational community
seeks to understand what determines such activity in a
machine learning setting. The latter seeks to identify
the biological signals or features that govern
activity. In this paper, we advance research in this
direction through a novel method that constructs and
selects complex sequence-based features which capture
information about distal patterns within a peptide.
Comparative analysis with state-of-the-art methods in
AMP recognition reveals our method is not only among
the top performers, but it also provides transparent
summarizations of antibacterial activity at the
sequence level. Moreover, this paper demonstrates for
the first time the capability not only to recognize
that a peptide is an AMP or not but also to predict its
target selectivity based on models of activity against
only Gram-positive, only Gram-negative, or both types
of bacteria. The work described in this paper is a step
forward in computational research seeking to facilitate
AMP design or modification in the wet laboratory.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Basu:2017:GEI,
author = "Mitra Basu and Yi Pan and Jianxin Wang",
title = "Guest {Editors} Introduction to the Special Section on
{ISBRA 2014}",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "314--315",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2676859",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The papers in this special section were presented at
the 10th International Symposium on Bioinformatics
Research and Applications ISBRA 2014, which was held at
Zhangjiajie, China, June 28-30, 2014.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2017:TPI,
author = "Fa Zhang and Yu Chen and Fei Ren and Xuan Wang and
Zhiyong Liu and Xiaohua Wan",
title = "A Two-Phase Improved Correlation Method for Automatic
Particle Selection in {Cryo-EM}",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "316--325",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2415787",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Particle selection from cryo-electron microscopy
Cryo-EM images is very important for high-resolution
reconstruction of macromolecular structure. The methods
of particle selection can be roughly grouped into two
classes, template-matching methods and feature-based
methods. In general, template-matching methods usually
generate better results than feature-based methods.
However, the accuracy of template-matching methods is
restricted by the noise and low contrast of Cryo-EM
images. Moreover, the processing speed of
template-matching methods, restricted by the random
orientation of particles, further limits their
practical applications. In this paper, combining the
advantages of feature-based methods and
template-matching methods, we present a two-phase
improved correlation method for automatic, fast
particle selection. In Phase I, we generate a
preliminary particle set using rotation-invariant
features of particles. In Phase II, we filter the
preliminary particle set using a correlation method to
reduce the interference of the high noise background
and improve the precision of particle selection. We
apply several optimization strategies, including a
modified adaboost algorithm, Divide and Conquer
technique, cascade strategy and graphics processing
unit parallel technique, to improve feature recognition
ability and reduce processing time. In addition, we
developed two correlation score functions for different
correlation situations. Experimental results on the
benchmark of Cryo-EM images show that our method can
improve the accuracy and processing speed of particle
selection significantly.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2017:API,
author = "Yi Liu and Bin Ma and Kaizhong Zhang and Gilles
Lajoie",
title = "An Approach for Peptide Identification by {De Novo}
Sequencing of Mixture Spectra",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "326--336",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2407401",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Mixture spectra occur quite frequently in a typical
wet-lab mass spectrometry experiment, which result from
the concurrent fragmentation of multiple precursors.
The ability to efficiently and confidently identify
mixture spectra is essential to alleviate the existent
bottleneck of low mass spectra identification rate.
However, most of the traditional computational methods
are not suitable for interpreting mixture spectra,
because they still take the assumption that the
acquired spectra come from the fragmentation of a
single precursor. In this manuscript, we formulate the
mixture spectra de novo sequencing problem
mathematically, and propose a dynamic programming
algorithm for the problem. Additionally, we use both
simulated and real mixture spectra data sets to verify
the merits of the proposed algorithm.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yan:2017:NNP,
author = "Yan and Anthony J. Kusalik and Fang-Xiang Wu",
title = "{NovoExD}: {De} novo Peptide Sequencing for {ETD\slash
ECD} Spectra",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "337--344",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2389813",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "De novo peptide sequencing using tandem mass
spectrometry MS/MS data has become a major
computational method for sequence identification in
recent years. With the development of new instruments
and technology, novel computational methods have
emerged with enhanced performance. However, there are
only a few methods focusing on ECD/ETD spectra, which
mainly contain variants of $c$ -ions and $z$ -ions.
Here, a de novo sequencing method for ECD/ETD spectra,
NovoExD, is presented. NovoExD applies a new form of
spectrum graph with multiple edge types called a GMET,
considers multiple peptide tags, and integrates amino
acid combination AAC and fragment ion charge
information. Its performance is compared with another
successful de novo sequencing method, pNovo+, which has
an option for ECD/ETD spectra. Experiments conducted on
three different datasets show that the average full
length peptide identification accuracy of NovoExD is as
high as 88.70 percent, and that NovoExD's average
accuracy is more than 20 percent greater on all
datasets than that of pNovo+.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhu:2017:ISI,
author = "Lin Zhu and Su-Ping Deng and Zhu-Hong You and
De-Shuang Huang",
title = "Identifying Spurious Interactions in the
Protein-Protein Interaction Networks Using Local
Similarity Preserving Embedding",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "345--352",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2407393",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In recent years, a remarkable amount of
protein-protein interaction PPI data are being
available owing to the advance made in experimental
high-throughput technologies. However, the
experimentally detected PPI data usually contain a
large amount of spurious links, which could contaminate
the analysis of the biological significance of protein
links and lead to incorrect biological discoveries,
thereby posing new challenges to both computational and
biological scientists. In this paper, we develop a new
embedding algorithm called local similarity preserving
embedding LSPE to rank the interaction possibility of
protein links. By going beyond limitations of current
geometric embedding methods for network denoising and
emphasizing the local information of PPI networks, LSPE
can avoid the unstableness of previous methods. We
demonstrate experimental results on benchmark PPI
networks and show that LSPE was the overall leader,
outperforming the state-of-the-art methods in
topological false links elimination problems.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jiang:2017:MDR,
author = "Xingpeng Jiang and Xiaohua Hu and Weiwei Xu",
title = "Microbiome Data Representation by Joint Nonnegative
Matrix Factorization with {Laplacian} Regularization",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "353--359",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2440261",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Microbiome datasets are often comprised of different
representations or views which provide complementary
information to understand microbial communities, such
as metabolic pathways, taxonomic assignments, and gene
families. Data integration methods including approaches
based on nonnegative matrix factorization NMF combine
multi-view data to create a comprehensive view of a
given microbiome study by integrating multi-view
information. In this paper, we proposed a novel variant
of NMF which called Laplacian regularized joint
non-negative matrix factorization LJ-NMF for
integrating functional and phylogenetic profiles from
HMP. We compare the performance of this method to other
variants of NMF. The experimental results indicate that
the proposed method offers an efficient framework for
microbiome data analysis.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Peng:2017:PPF,
author = "Wei Peng and Min Li and Lu Chen and Lusheng Wang",
title = "Predicting Protein Functions by Using Unbalanced
Random Walk Algorithm on Three Biological Networks",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "360--369",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2394314",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "With the gap between the sequence data and their
functional annotations becomes increasing wider, many
computational methods have been proposed to annotate
functions for unknown proteins. However, designing
effective methods to make good use of various
biological resources is still a big challenge for
researchers due to function diversity of proteins. In
this work, we propose a new method named ThrRW, which
takes several steps of random walking on three
different biological networks: protein interaction
network PIN, domain co-occurrence network DCN, and
functional interrelationship network FIN, respectively,
so as to infer functional information from neighbors in
the corresponding networks. With respect to the
topological and structural differences of the three
networks, the number of walking steps in the three
networks will be different. In the course of working,
the functional information will be transferred from one
network to another according to the associations
between the nodes in different networks. The results of
experiment on S. cerevisiae data show that our method
achieves better prediction performance not only than
the methods that consider both PIN data and GO term
similarities, but also than the methods using both PIN
data and protein domain information, which verifies the
effectiveness of our method on integrating multiple
biological data sources.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2017:UCC,
author = "Min Li and Yu Lu and Zhibei Niu and Fang-Xiang Wu",
title = "United Complex Centrality for Identification of
Essential Proteins from {PPI} Networks",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "370--380",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2394487",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Essential proteins are indispensable for the survival
or reproduction of an organism. Identification of
essential proteins is not only necessary for the
understanding of the minimal requirements for cellular
life, but also important for the disease study and drug
design. With the development of high-throughput
techniques, a large number of protein-protein
interaction data are available, which promotes the
studies of essential proteins from the network level.
Up to now, though a series of computational methods
have been proposed, the prediction precision still
needs to be improved. In this paper, we propose a new
method, United complex Centrality UC, to identify
essential proteins by integrating the protein complexes
with the topological features of protein-protein
interaction PPI networks. By analyzing the relationship
between the essential proteins and the known protein
complexes of S. cerevisiae and human, we find that the
proteins in complexes are more likely to be essential
compared with the proteins not included in any
complexes and the proteins appeared in multiple
complexes are more inclined to be essential compared to
those only appeared in a single complex. Considering
that some protein complexes generated by computational
methods are inaccurate, we also provide a modified
version of UC with parameter alpha, named UC-P. The
experimental results show that protein complex
information can help identify the essential proteins
more accurate both for the PPI network of S. cerevisiae
and that of human. The proposed method UC performs
obviously better than the eight previously proposed
methods DC, IC, EC, SC, BC, CC, NC, and LAC for
identifying essential proteins.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mahjani:2017:FCF,
author = "Behrang Mahjani and Salman Toor and Carl Nettelblad
and Sverker Holmgren",
title = "A Flexible Computational Framework Using {$R$} and
Map-Reduce for Permutation Tests of Massive Genetic
Analysis of Complex Traits",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "381--392",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2527639",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/s-plus.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In quantitative trait locus QTL mapping significance
of putative QTL is often determined using permutation
testing. The computational needs to calculate the
significance level are immense, $ 10^4 $ up to $ 10^8 $
or even more permutations can be needed. We have
previously introduced the PruneDIRECT algorithm for
multiple QTL scan with epistatic interactions. This
algorithm has specific strengths for permutation
testing. Here, we present a flexible, parallel
computing framework for identifying multiple
interacting QTL using the PruneDIRECT algorithm which
uses the map-reduce model as implemented in Hadoop. The
framework is implemented in R, a widely used software
tool among geneticists. This enables users to rearrange
algorithmic steps to adapt genetic models, search
algorithms, and parallelization steps to their needs in
a flexible way. Our work underlines the maturity of
accessing distributed parallel computing for
computationally demanding bioinformatics applications
through building workflows within existing scientific
environments. We investigate the PruneDIRECT algorithm,
comparing its performance to exhaustive search and
DIRECT algorithm using our framework on a public cloud
resource. We find that PruneDIRECT is vastly superior
for permutation testing, and perform $ 2 \times 10^5 $
permutations for a 2D QTL problem in $ 15 $ hours,
using $ 100 $ cloud processes. We show that our
framework scales out almost linearly for a 3D QTL
search.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nguyen:2017:NSC,
author = "Van-Nui Nguyen and Kai-Yao Huang and Chien-Hsun Huang
and K. Robert Lai and Tzong-Yi Lee",
title = "A New Scheme to Characterize and Identify Protein
Ubiquitination Sites",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "393--403",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2520939",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein ubiquitination, involving the conjugation of
ubiquitin on lysine residue, serves as an important
modulator of many cellular functions in eukaryotes.
Recent advancements in proteomic technology have
stimulated increasing interest in identifying
ubiquitination sites. However, most computational tools
for predicting ubiquitination sites are focused on
small-scale data. With an increasing number of
experimentally verified ubiquitination sites, we were
motivated to design a predictive model for identifying
lysine ubiquitination sites for large-scale proteome
dataset. This work assessed not only single features,
such as amino acid composition AAC, amino acid pair
composition AAPC and evolutionary information, but also
the effectiveness of incorporating two or more features
into a hybrid approach to model construction. The
support vector machine SVM was applied to generate the
prediction models for ubiquitination site
identification. Evaluation by five-fold
cross-validation showed that the SVM models learned
from the combination of hybrid features delivered a
better prediction performance. Additionally, a motif
discovery tool, MDDLogo, was adopted to characterize
the potential substrate motifs of ubiquitination sites.
The SVM models integrating the MDDLogo-identified
substrate motifs could yield an average accuracy of
68.70 percent. Furthermore, the independent testing
result showed that the MDDLogo-clustered SVM models
could provide a promising accuracy 78.50 percent and
perform better than other prediction tools. Two cases
have demonstrated the effective prediction of
ubiquitination sites with corresponding substrate
motifs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Matsieva:2017:RSF,
author = "Julia Matsieva and Steven Kelk and Celine Scornavacca
and Chris Whidden and Dan Gusfield",
title = "A Resolution of the Static Formulation Question for
the Problem of Computing the History Bound",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "404--417",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2527645",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Evolutionary data has been traditionally modeled via
phylogenetic trees; however, branching alone cannot
model conflicting phylogenetic signals, so networks are
used instead. Ancestral recombination graphs ARGs are
used to model the evolution of incompatible sets of SNP
data, allowing each site to mutate only once. The model
often aims to minimize the number of recombinations.
Similarly, incompatible cluster data can be represented
by a reticulation network that minimizes reticulation
events. The ARG literature has traditionally been
disjoint from the reticulation network literature. By
building on results from the reticulation network
literature, we resolve an open question of interest to
the ARG community. We explicitly prove that the History
Bound, a lower bound on the number of recombinations in
an ARG for a binary matrix, which was previously only
defined procedurally, is equal to the minimum number of
reticulation nodes in a network for the corresponding
cluster data. To facilitate the proof, we give an
algorithm that constructs this network using
intermediate values from the procedural History Bound
definition. We then develop a top-down algorithm for
computing the History Bound, which has the same
worst-case runtime as the known dynamic program, and
show that it is likely to run faster in typical
cases.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rajaraman:2017:ACR,
author = "Ashok Rajaraman and Joao Paulo Pereira Zanetti and Jan
Manuch and Cedric Chauve",
title = "Algorithms and Complexity Results for Genome Mapping
Problems",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "418--430",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2528239",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genome mapping algorithms aim at computing an ordering
of a set of genomic markers based on local ordering
information such as adjacencies and intervals of
markers. In most genome mapping models, markers are
assumed to occur uniquely in the resulting map. We
introduce algorithmic questions that consider repeats,
i.e., markers that can have several occurrences in the
resulting map. We show that, provided with an upper
bound on the copy number of repeated markers and with
intervals that span full repeat copies, called repeat
spanning intervals, the problem of deciding if a set of
adjacencies and repeat spanning intervals admits a
genome representation is tractable if the target genome
can contain linear and/or circular chromosomal
fragments. We also show that extracting a maximum
cardinality or weight subset of repeat spanning
intervals given a set of adjacencies that admits a
genome realization is NP-hard but fixed-parameter
tractable in the maximum copy number and the number of
adjacent repeats, and tractable if intervals contain a
single repeated marker.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Alden:2017:EAS,
author = "Kieran Alden and Jon Timmis and Paul S. Andrews and
Henrique Veiga-Fernandes and Mark Coles",
title = "Extending and Applying {Spartan} to Perform Temporal
Sensitivity Analyses for Predicting Changes in
Influential Biological Pathways in Computational
Models",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "431--442",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2527654",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Through integrating real time imaging, computational
modelling, and statistical analysis approaches,
previous work has suggested that the induction of and
response to cell adhesion factors is the key initiating
pathway in early lymphoid tissue development, in
contrast to the previously accepted view that the
process is triggered by chemokine mediated cell
recruitment. These model derived hypotheses were
developed using spartan, an open-source sensitivity
analysis toolkit designed to establish and understand
the relationship between a computational model and the
biological system that model captures. Here, we extend
the functionality available in spartan to permit the
production of statistical analyses that contrast the
behavior exhibited by a computational model at various
simulated time-points, enabling a temporal analysis
that could suggest whether the influence of biological
mechanisms changes over time. We exemplify this
extended functionality by using the computational model
of lymphoid tissue development as a time-lapse tool. By
generating results at twelve- hour intervals, we show
how the extensions to spartan have been used to suggest
that lymphoid tissue development could be biphasic, and
predict the time-point when a switch in the influence
of biological mechanisms might occur.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dias:2017:GWS,
author = "Oscar Dias and Daniel Gomes and Paulo Vilaca and Joao
Cardoso and Miguel Rocha and Eugenio C. Ferreira and
Isabel Rocha",
title = "Genome-Wide Semi-Automated Annotation of Transporter
Systems",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "443--456",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2527647",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Usually, transport reactions are added to genome-scale
metabolic models GSMMs based on experimental data and
literature. This approach does not allow associating
specific genes with transport reactions, which impairs
the ability of the model to predict effects of gene
deletions. Novel methods for systematic genome-wide
transporter functional annotation and their integration
into GSMMs are therefore necessary. In this work, an
automatic system to detect and classify all potential
membrane transport proteins for a given genome and
integrate the related reactions into GSMMs is proposed,
based on the identification and classification of genes
that encode transmembrane proteins. The Transport
Reactions Annotation and Generation TRIAGE tool
identifies the metabolites transported by each
transmembrane protein and its transporter family. The
localization of the carriers is also predicted and,
consequently, their action is confined to a given
membrane. The integration of the data provided by
TRIAGE with highly curated models allowed the
identification of new transport reactions. TRIAGE is
included in the new release of merlin, a software tool
previously developed by the authors, which expedites
the GSMM reconstruction processes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mkrtchyan:2017:OLS,
author = "Katya Mkrtchyan and Anirban Chakraborty and Amit K.
Roy-Chowdhury",
title = "Optimal Landmark Selection for Registration of {$4$D}
Confocal Image Stacks in \bioname{Arabidopsis}",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "457--467",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2527655",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Technologically advanced imaging techniques have
allowed us to generate and study the internal part of a
tissue over time by capturing serial optical images
that contain spatio-temporal slices of hundreds of
tightly packed cells. Image registration of such
live-imaging datasets of developing multicelluar
tissues is one of the essential components of all image
analysis pipelines. In this paper, we present a fully
automated 4DX-Y-Z-T registration method of live imaging
stacks that takes care of both temporal and spatial
misalignments. We present a novel landmark selection
methodology where the shape features of individual
cells are not of high quality and highly
distinguishable. The proposed registration method finds
the best image slice correspondence from consecutive
image stacks to account for vertical growth in the
tissue and the discrepancy in the choice of the
starting focal point. Then, it uses local graph-based
approach to automatically find corresponding landmark
pairs, and finally the registration parameters are used
to register the entire image stack. The proposed
registration algorithm combined with an existing
tracking method is tested on multiple image stacks of
tightly packed cells of Arabidopsis shoot apical
meristem and the results show that it significantly
improves the accuracy of cell lineages and division
statistics.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hayamizu:2017:CMS,
author = "Momoko Hayamizu and Hiroshi Endo and Kenji Fukumizu",
title = "A Characterization of Minimum Spanning Tree-Like
Metric Spaces",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "468--471",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2550431",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recent years have witnessed a surge of biological
interest in the minimum spanning tree MST problem for
its relevance to automatic model construction using the
distances between data points. Despite the increasing
use of MST algorithms for this purpose, the
goodness-of-fit of an MST to the data is often elusive
because no quantitative criteria have been developed to
measure it. Motivated by this, we provide a necessary
and sufficient condition to ensure that a metric space
on $n$ points can be represented by a fully labeled
tree on $n$ vertices, and thereby determine when an MST
preserves all pairwise distances between points in a
finite metric space.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Boes:2017:LBN,
author = "Olivier Boes and Mareike Fischer and Steven Kelk",
title = "A Linear Bound on the Number of States in Optimal
Convex Characters for Maximum Parsimony Distance",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "472--477",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2543727",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Given two phylogenetic trees on the same set of taxa
$X$ , the maximum parsimony distance $ d_\mathrm {MP}$
is defined as the maximum, ranging over all characters
$ \chi $ on $X$ , of the absolute difference in
parsimony score induced by $ \chi $ on the two trees.
In this note, we prove that for binary trees there
exists a character achieving this maximum that is
convex on one of the trees i.e., the parsimony score
induced on that tree is equal to the number of states
in the character minus 1 and such that the number of
states in the character is at most $ 7 d_\mathrm {MP} -
5$ . This is the first non-trivial bound on the number
of states required by optimal characters, convex or
otherwise. The result potentially has algorithmic
significance because, unlike general characters, convex
characters with a bounded number of states can be
enumerated in polynomial time.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nguyen:2017:BAR,
author = "Thao Thi Phuong Nguyen and Vinh Sy Le and Hai Bich Ho
and Quang Si Le",
title = "Building Ancestral Recombination Graphs for Whole
Genomes",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "478--483",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2542801",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We propose a heuristic algorithm, called ARG4WG, to
build plausible ancestral recombination graphs ARGs
from thousands of whole genome samples. By using the
longest shared end for recombination inference, ARG4WG
constructs ARGs with small numbers of recombination
events that perform well in association mapping on
genome-wide association studies.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Athanasiadis:2017:DMR,
author = "Emmanouil Athanasiadis and Marilena Bourdakou and
George Spyrou",
title = "{D-Map}: Random Walking on Gene Network Inference Maps
Towards differential Avenue Discovery",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "484--490",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2535267",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Differential rewiring of cellular interaction networks
between disease and healthy state is of great
importance. Through a systems level approach,
malfunctioned mechanisms that are absent in the normal
cases, may enlighten the key-players in terms of genes
and their interaction chains related to disease. We
have developed D-Map, a publicly available
user-friendly web application, capable of generating
and manipulating advanced differential networks by
combining state-of-the-art inference reconstruction
methods with random walk simulations. The inputs are
expression profiles obtained from the Gene Expression
Omnibus and a gene list under investigation.
Differential networks may be visualized and interpreted
through the use of D-Map interface, where display of
the disease, the normal and the common state can be
performed, interactively. A case study scenario
concerning Alzheimer's disease, as well as breast,
lung, and bladder cancer was conducted in order to
demonstrate the usefulness of the proposed methodology
to different disease types. Findings were consistent
with the current bibliography, and the provided
interaction lists may be further explored towards novel
biological insights of the investigated diseases. The
DMap web-application is available at:
http://bioserver-3.bioacademy.gr/Bioserver/DMap/index.php.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mottelet:2017:MFA,
author = "Stephane Mottelet and Gil Gaullier and Georges
Sadaka",
title = "Metabolic Flux Analysis in Isotope Labeling
Experiments Using the Adjoint Approach",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "491--497",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2544299",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Comprehension of metabolic pathways is considerably
enhanced by metabolic flux analysis MFA-ILE in isotope
labeling experiments. The balance equations are given
by hundreds of algebraic stationary MFA or ordinary
differential equations nonstationary MFA, and reducing
the number of operations is therefore a crucial part of
reducing the computation cost. The main bottleneck for
deterministic algorithms is the computation of
derivatives, particularly for nonstationary MFA. In
this article, we explain how the overall identification
process may be speeded up by using the adjoint approach
to compute the gradient of the residual sum of squares.
The proposed approach shows significant improvements in
terms of complexity and computation time when it is
compared with the usual direct approach. Numerical
results are obtained for the central metabolic pathways
of Escherichia coli and are validated against reference
software in the stationary case. The methods and
algorithms described in this paper are included in the
sysmetab software package distributed under an Open
Source license at
http://forge.scilab.org/index.php/p/sysmetab/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Goldweber:2017:PGB,
author = "Scott Goldweber and Jamal Theodore and John
Torcivia-Rodriguez and Vahan Simonyan and Raja
Mazumder",
title = "{Pubcast} and {Genecast}: Browsing and Exploring
Publications and Associated Curated Content in Biology
Through Mobile Devices",
journal = j-TCBB,
volume = "14",
number = "2",
pages = "498--500",
month = mar,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2542802",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Jun 5 18:41:07 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Services such as Facebook, Amazon, and eBay were once
solely accessed from stationary computers. These web
services are now being used increasingly on mobile
devices. We acknowledge this new reality by providing
users a way to access publications and a curated cancer
mutation database on their mobile device with daily
automated updates. Availability:
http://hive.biochemistry.gwu.edu/tools/HivePubcast.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2017:GES,
author = "Fei Wang and Xiao-Li Li and Jason T. L. Wang and
See-Kiong Ng",
title = "Guest Editorial: Special Section on Biological Data
Mining and Its Applications in Healthcare",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "501--502",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2612558",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Biologists are stepping up their efforts in
understanding the biological processes that underlie
disease pathways in the clinical contexts. This has
resulted in a flood of biological and clinical
data-genomic sequences, DNA microarrays, protein
interactions, biomedical images, disease pathways, etc.
The rapid adoption of Electronic Health Records EHRs
across healthcare systems, coupled with the capability
of linking EHRs to research biorepositories, provides a
unique opportunity for conducting large-scale Precision
Medicine research. As a result, data mining techniques,
for knowledge discovery and deriving data driven
insights from various data sources, are increasingly
important in modern biology and healthcare. The purpose
of this special section is to bring together the
researchers in bioinformatics, healthcare informatics,
and data mining to share about their current research,
and their visions on future directions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2017:PSP,
author = "Hua Wang and Lin Yan and Heng Huang and Chris Ding",
title = "From Protein Sequence to Protein Function via
Multi-Label Linear Discriminant Analysis",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "503--513",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2591529",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Sequence describes the primary structure of a protein,
which contains important structural, characteristic,
and genetic information and thereby motivates many
sequence-based computational approaches to infer
protein function. Among them, feature-base approaches
attract increased attention because they make
prediction from a set of transformed and more
biologically meaningful sequence features. However,
original features extracted from sequence are usually
of high dimensionality and often compromised by
irrelevant patterns, therefore dimension reduction is
necessary prior to classification for efficient and
effective protein function prediction. A protein
usually performs several different functions within an
organism, which makes protein function prediction a
multi-label classification problem. In machine
learning, multi-label classification deals with
problems where each object may belong to more than one
class. As a well-known feature reduction method, linear
discriminant analysis LDA has been successfully applied
in many practical applications. It, however, by nature
is designed for single-label classification, in which
each object can belong to exactly one class. Because
directly applying LDA in multi-label classification
causes ambiguity when computing scatters matrices, we
apply a new Multi-label Linear Discriminant Analysis
MLDA approach to address this problem and meanwhile
preserve powerful classification capability inherited
from classical LDA. We further extend MLDA by $
\ell_1$-normalization to overcome the problem of
over-counting data points with multiple labels. In
addition, we incorporate biological network data using
Laplacian embedding into our method, and assess the
reliability of predicted putative functions. Extensive
empirical evaluations demonstrate promising results of
our methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2017:TUG,
author = "Jianqiang Li and Fei Wang",
title = "Towards Unsupervised Gene Selection: a Matrix
Factorization Framework",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "514--521",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2591545",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The recent development of microarray gene expression
techniques have made it possible to offer phenotype
classification of many diseases. However, in gene
expression data analysis, each sample is represented by
quite a large number of genes, and many of them are
redundant or insignificant to clarify the disease
problem. Therefore, how to efficiently select the most
useful genes has been becoming one of the most hot
research topics in the gene expression data analysis.
In this paper, a novel unsupervised two-stage
coarse-fine gene selection method is proposed. In the
first stage, we apply the kmeans algorithm to
over-cluster the genes and discard some redundant
genes. In the second stage, we select the most
representative genes from the remaining ones based on
matrix factorization. Finally the experimental results
on several data sets are presented to show the
effectiveness of our method.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ju:2017:EAC,
author = "Chelsea J. -T. Ju and Zhuangtian Zhao and Wei Wang",
title = "Efficient Approach to Correct Read Alignment for
Pseudogene Abundance Estimates",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "522--533",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2591533",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "RNA-Sequencing has been the leading technology to
quantify expression of thousands of genes
simultaneously. The data analysis of an RNA-Seq
experiment starts from aligning short reads to the
reference genome/transcriptome or reconstructed
transcriptome. However, current aligners lack the
sensitivity to distinguish reads that come from
homologous regions of an genome. One group of these
homologies is the paralog pseudogenes. Pseudogenes
arise from duplication of a set of protein coding
genes, and have been considered as degraded paralogs in
the genome due to their lost of functionality. Recent
studies have provided evidence to support their novel
regulatory roles in biological processes. With the
growing interests in quantifying the expression level
of pseudogenes at different tissues or cell lines, it
is critical to have a sensitive method that can
correctly align ambiguous reads and accurately estimate
the expression level among homologous genes. Previously
in PseudoLasso, we proposed a linear regression
approach to learn read alignment behaviors, and to
leverage this knowledge for abundance estimation and
alignment correction. In this paper, we extend the work
of PseudoLasso by grouping the homologous genomic
regions into different communities using a community
detection algorithm, followed by building a linear
regression model separately for each community. The
results show that this approach is able to retain the
same accuracy as PseudoLasso. By breaking the genome
into smaller homologous communities, the running time
is improved from quadratic growth to linear with
respect to the number of genes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Walker:2017:ATS,
author = "Peter B. Walker and Jacob N. Norris and Anna E.
Tschiffely and Melissa L. Mehalick and Craig A.
Cunningham and Ian N. Davidson",
title = "Applications of Transductive Spectral Clustering
Methods in a Military Medical Concussion Database",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "534--544",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2591549",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Traumatic brain injury TBI is one of the most common
forms of neurotrauma that has affected more than
250,000 military service members over the last decade
alone. While in battle, service members who experience
TBI are at significant risk for the development of
normal TBI symptoms, as well as risk for the
development of psychological disorders such as
Post-Traumatic Stress Disorder PTSD. As such, these
service members often require intense bouts of
medication and therapy in order to resume full
return-to-duty status. The primary aim of this study is
to identify the relationship between the administration
of specific medications and reductions in symptomology
such as headaches, dizziness, or light-headedness.
Service members diagnosed with mTBI and seen at the
Concussion Restoration Care Center CRCC in Afghanistan
were analyzed according to prescribed medications and
symptomology. Here, we demonstrate that in such
situations with sparse labels and small feature sets,
classic analytic techniques such as logistic
regression, support vector machines, na{\"\i}ve Bayes,
random forest, decision trees, and k-nearest neighbor
are not well suited for the prediction of outcomes. We
attribute our findings to several issues inherent to
this problem setting and discuss several advantages of
spectral graph methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Stojanovic:2017:MHQ,
author = "Jelena Stojanovic and Djordje Gligorijevic and Vladan
Radosavljevic and Nemanja Djuric and Mihajlo Grbovic
and Zoran Obradovic",
title = "Modeling Healthcare Quality via Compact
Representations of Electronic Health Records",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "545--554",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2591523",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Increased availability of Electronic Health Record EHR
data provides unique opportunities for improving the
quality of health services. In this study, we couple
EHRs with the advanced machine learning tools to
predict three important parameters of healthcare
quality. More specifically, we describe how to learn
low-dimensional vector representations of patient
conditions and clinical procedures in an unsupervised
manner, and generate feature vectors of hospitalized
patients useful for predicting their length of stay,
total incurred charges, and mortality rates. In order
to learn vector representations, we propose to employ
state-of-the-art language models specifically designed
for modeling co-occurrence of diseases and applied
clinical procedures. The proposed model is trained on a
large-scale EHR database comprising more than 35
million hospitalizations in California over a period of
nine years. We compared the proposed approach to
several alternatives and evaluated their effectiveness
by measuring accuracy of regression and classification
models used for three predictive tasks considered in
this study. Our model outperformed the baseline models
on all tasks, indicating a strong potential of the
proposed approach for advancing quality of the
healthcare system.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Moskovitch:2017:PCO,
author = "Robert Moskovitch and Hyunmi Choi and George Hripcsak
and Nicholas Tatonetti",
title = "Prognosis of Clinical Outcomes with Temporal Patterns
and Experiences with One Class Feature Selection",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "555--563",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2591539",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Accurate prognosis of outcome events, such as clinical
procedures or disease diagnosis, is central in
medicine. The emergence of longitudinal clinical data,
like the Electronic Health Records EHR, represents an
opportunity to develop automated methods for predicting
patient outcomes. However, these data are highly
dimensional and very sparse, complicating the
application of predictive modeling techniques. Further,
their temporal nature is not fully exploited by current
methods, and temporal abstraction was recently used
which results in symbolic time intervals
representation. We present Maitreya, a framework for
the prediction of outcome events that leverages these
symbolic time intervals. Using Maitreya, learn
predictive models based on the temporal patterns in the
clinical records that are prognostic markers and use
these markers to train predictive models for eight
clinical procedures. In order to decrease the number of
patterns that are used as features, we propose the use
of three one class feature selection methods. We
evaluate the performance of Maitreya under several
parameter settings, including the one-class feature
selection, and compare our results to that of atemporal
approaches. In general, we found that the use of
temporal patterns outperformed the atemporal methods,
when representing the number of pattern occurrences.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2017:BCO,
author = "Xin Wang and Jinbo Bi",
title = "Bi-convex Optimization to Learn Classifiers from
Multiple Biomedical Annotations",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "564--575",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2576457",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The problem of constructing classifiers from multiple
annotators who provide inconsistent training labels is
important and occurs in many application domains. Many
existing methods focus on the understanding and
learning of the crowd behaviors. Several probabilistic
algorithms consider the construction of classifiers for
specific tasks using consensus of multiple labelers
annotations. These methods impose a prior on the
consensus and develop an expectation-maximization
algorithm based on logistic regression loss. We extend
the discussion to the hinge loss commonly used by
support vector machines. Our formulations form
bi-convex programs that construct classifiers and
estimate the reliability of each labeler
simultaneously. Each labeler is associated with a
reliability parameter, which can be a constant, or
class-dependent, or varies for different examples. The
hinge loss is modified by replacing the true labels by
the weighted combination of labelers' labels with
reliabilities as weights. Statistical justification is
discussed to motivate the use of linear combination of
labels. In parallel to the expectation-maximization
algorithm for logistic-based methods, efficient
alternating algorithms are developed to solve the
proposed bi-convex programs. Experimental results on
benchmark datasets and three real-world biomedical
problems demonstrate that the proposed methods either
outperform or are competitive to the state of the
art.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Harrison:2017:GEI,
author = "Robert W. Harrison and Ion I. Mandoiu and Alexander
Zelikovsky",
title = "Guest Editors' Introduction to the Special Section on
Bioinformatics Research and Applications",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "576--577",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2673738",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The papers from this special section were presented at
the 11th International Symposium on Bioinformatics
Research and Application ISBRA, which was held at Old
Dominion University in Norfolk, VA on May 7-10, 2015.
The ISBRA symposium provides a forum for the exchange
of ideas and results among researchers, developers, and
practitioners working on all aspects of bioinformatics
and computational biology and their applications.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Biswas:2017:ECM,
author = "Abhishek Biswas and Desh Ranjan and Mohammad Zubair
and Stephanie Zeil and Kamal {Al Nasr} and Jing He",
title = "An Effective Computational Method Incorporating
Multiple Secondary Structure Predictions in Topology
Determination for Cryo-{EM} Images",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "578--586",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2543721",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A key idea in de novo modeling of a medium-resolution
density image obtained from cryo-electron microscopy is
to compute the optimal mapping between the secondary
structure traces observed in the density image and
those predicted on the protein sequence. When secondary
structures are not determined precisely, either from
the image or from the amino acid sequence of the
protein, the computational problem becomes more
complex. We present an efficient method that addresses
the secondary structure placement problem in presence
of multiple secondary structure predictions and
computes the optimal mapping. We tested the method
using 12 simulated images from $ \alpha $-proteins and
two Cryo-EM images of $ \alpha $--$ \beta $ proteins.
We observed that the rank of the true topologies is
consistently improved by using multiple secondary
structure predictions instead of a single prediction.
The results show that the algorithm is robust and works
well even when errors/misses in the predicted secondary
structures are present in the image or the sequence.
The results also show that the algorithm is efficient
and is able to handle proteins with as many as 33
helices.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kordi:2017:CDT,
author = "Misagh Kordi and Mukul S. Bansal",
title = "On the Complexity of Duplication-Transfer-Loss
Reconciliation with Non-Binary Gene Trees",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "587--599",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2511761",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Duplication-Transfer-Loss DTL reconciliation has
emerged as a powerful technique for studying gene
family evolution in the presence of horizontal gene
transfer. DTL reconciliation takes as input a gene
family phylogeny and the corresponding species
phylogeny, and reconciles the two by postulating
speciation, gene duplication, horizontal gene transfer,
and gene loss events. Efficient algorithms exist for
finding optimal DTL reconciliations when the gene tree
is binary. However, gene trees are frequently
non-binary. With such non-binary gene trees, the
reconciliation problem seeks to find a binary
resolution of the gene tree that minimizes the
reconciliation cost. Given the prevalence of non-binary
gene trees, many efficient algorithms have been
developed for this problem in the context of the
simpler Duplication-Loss DL reconciliation model. Yet,
no efficient algorithms exist for DTL reconciliation
with non-binary gene trees and the complexity of the
problem remains unknown. In this work, we resolve this
open question by showing that the problem is, in fact,
NP-hard. Our reduction applies to both the dated and
undated formulations of DTL reconciliation. By
resolving this long-standing open problem, this work
will spur the development of both exact and heuristic
algorithms for this important problem.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Guo:2017:SGW,
author = "Xuan Guo and Jing Zhang and Zhipeng Cai and Ding-Zhu
Du and Yi Pan",
title = "Searching Genome-Wide Multi-Locus Associations for
Multiple Diseases Based on {Bayesian} Inference",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "600--610",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2527648",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Taking the advantage of high-throughput single
nucleotide polymorphism SNP genotyping technology,
large genome-wide association studies GWASs have been
considered to hold promise for unraveling complex
relationships between genotypes and phenotypes. Current
multi-locus-based methods are insufficient to detect
interactions with diverse genetic effects on
multifarious diseases. Also, statistic tests for
high-order epistasis $ \geq 2 $ SNPs raise huge
computational and analytical challenges because the
computation increases exponentially as the growth of
the cardinality of SNPs combinations. In this paper, we
provide a simple, fast and powerful method, named DAM,
using Bayesian inference to detect genome-wide
multi-locus epistatic interactions in multiple
diseases. Experimental results on simulated data
demonstrate that our method is powerful and efficient.
We also apply DAM on two GWAS datasets from WTCCC,
i.e., Rheumatoid Arthritis and Type 1 Diabetes, and
identify some novel findings. Therefore, we believe
that our method is suitable and efficient for the
full-scale analysis of multi-disease-related
interactions in GWASs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ji:2017:CIF,
author = "Hao Ji and Yaohang Li and Seth H. Weinberg",
title = "Calcium Ion Fluctuations Alter Channel Gating in a
Stochastic Luminal Calcium Release Site Model",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "611--619",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2498552",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Stochasticity and small system size effects in complex
biochemical reaction networks can greatly alter
transient and steady-state system properties. A common
approach to modeling reaction networks, which accounts
for system size, is the chemical master equation that
governs the dynamics of the joint probability
distribution for molecular copy number. However,
calculation of the stationary distribution is often
prohibitive, due to the large state-space associated
with most biochemical reaction networks. Here, we
analyze a network representing a luminal calcium
release site model and investigate to what extent small
system size effects and calcium fluctuations, driven by
ion channel gating, influx and diffusion, alter
steady-state ion channel properties including open
probability. For a physiological ion channel gating
model and number of channels, the state-space may be
between approximately $ 10^6 - 10^8 $ elements, and a
novel modified block power method is used to solve the
associated dominant eigenvector problem required to
calculate the stationary distribution. We demonstrate
that both small local cytosolic domain volume and a
small number of ion channels drive calcium fluctuations
that result in deviation from the corresponding model
that neglects small system size effects.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Galvao:2017:SCP,
author = "Gustavo Rodrigues Galvao and Christian Baudet and
Zanoni Dias",
title = "Sorting Circular Permutations by Super Short
Reversals",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "620--633",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2515594",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We consider the problem of sorting a circular
permutation by super short reversals i.e., reversals of
length at most 2, a problem that finds application in
comparative genomics. Polynomial-time solutions to the
unsigned version of this problem are known, but the
signed version remained open. In this paper, we present
the first polynomial-time solution to the signed
version of this problem. Moreover, we perform
experiments for inferring phylogenies of two different
groups of bacterial species and compare our results
with the phylogenies presented in previous works.
Finally, to facilitate phylogenetic studies based on
the methods studied in this paper, we present a web
tool for rearrangement-based phylogenetic inference
using short operations, such as super short
reversals.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zheng:2017:MMA,
author = "Weihua Zheng and Kenli Li and Keqin Li and Hing Cheung
So",
title = "A Modified Multiple Alignment {Fast Fourier Transform}
with Higher Efficiency",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "634--645",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2530064",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Multiple sequence alignment MSA is the most common
task in bioinformatics. Multiple alignment fast Fourier
transform MAFFT is the fastest MSA program among those
the accuracy of the resulting alignments can be
comparable with the most accurate MSA programs. In this
paper, we modify the correlation computation scheme of
the MAFFT for further efficiency improvement in three
aspects. First, novel complex number based amino acid
and nucleotide expressions are utilized in the modified
correlation. Second, linear convolution with a
limitation is proposed for computing the correlation of
amino acid and nucleotide sequences. Third, we devise a
fast Fourier transform FFT algorithm for computing
linear convolution. The FFT algorithm is based on
conjugate pair split-radix FFT and does not require the
permutation of order, and it is new as only real parts
of the final outputs are required. Simulation results
show that the speed of the modified scheme is 107.58 to
365.74 percent faster than that of the original MAFFT
for one execution of the function Falign of MAFFT,
indicating its faster realization.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ezzat:2017:DTI,
author = "Ali Ezzat and Peilin Zhao and Min Wu and Xiao-Li Li
and Chee-Keong Kwoh",
title = "Drug-Target Interaction Prediction with Graph
Regularized Matrix Factorization",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "646--656",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2530062",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Experimental determination of drug-target interactions
is expensive and time-consuming. Therefore, there is a
continuous demand for more accurate predictions of
interactions using computational techniques. Algorithms
have been devised to infer novel interactions on a
global scale where the input to these algorithms is a
drug-target network i.e., a bipartite graph where edges
connect pairs of drugs and targets that are known to
interact. However, these algorithms had difficulty
predicting interactions involving new drugs or targets
for which there are no known interactions i.e.,
``orphan'' nodes in the network. Since data usually lie
on or near to low-dimensional non-linear manifolds, we
propose two matrix factorization methods that use graph
regularization in order to learn such manifolds. In
addition, considering that many of the non-occurring
edges in the network are actually unknown or missing
cases, we developed a preprocessing step to enhance
predictions in the ``new drug'' and ``new target''
cases by adding edges with intermediate interaction
likelihood scores. In our cross validation experiments,
our methods achieved better results than three other
state-of-the-art methods in most cases. Finally, we
simulated some ``new drug'' and ``new target'' cases
and found that GRMF predicted the left-out interactions
reasonably well.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Thanh:2017:ECT,
author = "Vo Hong Thanh and Roberto Zunino and Corrado Priami",
title = "Efficient Constant-Time Complexity Algorithm for
Stochastic Simulation of Large Reaction Networks",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "657--667",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2530066",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Exact stochastic simulation is an indispensable tool
for a quantitative study of biochemical reaction
networks. The simulation realizes the time evolution of
the model by randomly choosing a reaction to fire and
update the system state according to a probability that
is proportional to the reaction propensity. Two
computationally expensive tasks in simulating large
biochemical networks are the selection of next reaction
firings and the update of reaction propensities due to
state changes. We present in this work a new exact
algorithm to optimize both of these simulation
bottlenecks. Our algorithm employs the
composition-rejection on the propensity bounds of
reactions to select the next reaction firing. The
selection of next reaction firings is independent of
the number reactions while the update of propensities
is skipped and performed only when necessary. It
therefore provides a favorable scaling for the
computational complexity in simulating large reaction
networks. We benchmark our new algorithm with the state
of the art algorithms available in literature to
demonstrate its applicability and efficiency.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Arram:2017:LFA,
author = "James Arram and Thomas Kaplan and Wayne Luk and
Peiyong Jiang",
title = "Leveraging {FPGAs} for Accelerating Short Read
Alignment",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "668--677",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2535385",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "One of the key challenges facing genomics today is how
to efficiently analyze the massive amounts of data
produced by next-generation sequencing platforms. With
general-purpose computing systems struggling to address
this challenge, specialized processors such as the
Field-Programmable Gate Array FPGA are receiving
growing interest. The means by which to leverage this
technology for accelerating genomic data analysis is
however largely unexplored. In this paper, we present a
runtime reconfigurable architecture for accelerating
short read alignment using FPGAs. This architecture
exploits the reconfigurability of FPGAs to allow the
development of fast yet flexible alignment designs. We
apply this architecture to develop an alignment design
which supports exact and approximate alignment with up
to two mismatches. Our design is based on the FM-index,
with optimizations to improve the alignment
performance. In particular, the $n$-step FM-index,
index oversampling, a seed-and-compare stage, and
bi-directional backtracking are included. Our design is
implemented and evaluated on a 1U Maxeler MPC-X2000
dataflow node with eight Altera Stratix-V FPGAs.
Measurements show that our design is 28 times faster
than Bowtie2 running with 16 threads on dual Intel Xeon
E5-2640 CPUs, and nine times faster than Soap3-dp
running on an NVIDIA Tesla C2070 GPU.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{He:2017:PNA,
author = "Feng He and Guanghui Zhu and Yin-Ying Wang and
Xing-Ming Zhao and De-Shuang Huang",
title = "{PCID}: a Novel Approach for Predicting Disease
Comorbidity by Integrating Multi-Scale Data",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "678--686",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2550443",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Disease comorbidity is the presence of one or more
diseases along with a primary disorder, which causes
additional pain to patients and leads to the failure of
standard treatments compared with single diseases.
Therefore, the identification of potential comorbidity
can help prevent those comorbid diseases when treating
a primary disease. Unfortunately, most of current known
disease comorbidities are discovered occasionally in
clinic, and our knowledge about comorbidity is far from
complete. Despite the fact that many efforts have been
made to predict disease comorbidity, the prediction
accuracy of existing computational approaches needs to
be improved. By investigating the factors underlying
disease comorbidity, e.g., mutated genes and rewired
protein-protein interactions PPIs, we here present a
novel algorithm to predict disease comorbidity by
integrating multi-scale data ranging from genes to
phenotypes. Benchmark results on real data show that
our approach outperforms existing algorithms, and some
of our novel predictions are validated with those
reported in literature, indicating the effectiveness
and predictive power of our approach. In addition, we
identify some pathway and PPI patterns that underlie
the co-occurrence between a primary disease and certain
disease classes, which can help explain how the
comorbidity is initiated from molecular perspectives.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zeng:2017:PVD,
author = "Xiangxiang Zeng and Yuanlu Liao and Yuansheng Liu and
Quan Zou",
title = "Prediction and Validation of Disease Genes Using
{HeteSim} Scores",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "687--695",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2520947",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Deciphering the gene disease association is an
important goal in biomedical research. In this paper,
we use a novel relevance measure, called HeteSim, to
prioritize candidate disease genes. Two methods based
on heterogeneous networks constructed using
protein-protein interaction, gene-phenotype
associations, and phenotype-phenotype similarity, are
presented. In HeteSim_MultiPath HSMP, HeteSim scores of
different paths are combined with a constant that
dampens the contributions of longer paths. In
HeteSim_SVM HSSVM, HeteSim scores are combined with a
machine learning method. The 3-fold experiments show
that our non-machine learning method HSMP performs
better than the existing non-machine learning methods,
our machine learning method HSSVM obtains similar
accuracy with the best existing machine learning method
CATAPULT. From the analysis of the top 10 predicted
genes for different diseases, we found that HSSVM avoid
the disadvantage of the existing machine learning based
methods, which always predict similar genes for
different diseases. The data sets and Matlab code for
the two methods are freely available for download at
\url{http://lab.malab.cn/data/HeteSim/index.jsp}.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Shabash:2017:RVR,
author = "Boris Shabash and Kay C. Wiese",
title = "{RNA} Visualization: Relevance and the Current
State-of-the-Art Focusing on Pseudoknots",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "696--712",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2522421",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "RNA visualization is crucial in order to understand
the relationship that exists between RNA structure and
its function, as well as the development of better RNA
structure prediction algorithms. However, in the
context of RNA visualization, one key structure remains
difficult to visualize: Pseudoknots. Pseudoknots occur
in RNA folding when two secondary structural components
form base-pairs between them. The three-dimensional
nature of these components makes them challenging to
visualize in two-dimensional media, such as print media
or screens. In this review, we focus on the
advancements that have been made in the field of RNA
visualization in two-dimensional media in the past two
decades. The review aims at presenting all relevant
aspects of pseudoknot visualization. We start with an
overview of several pseudoknotted structures and their
relevance in RNA function. Next, we discuss the
theoretical basis for RNA structural topology
classification and present RNA classification systems
for both pseudoknotted and non-pseudoknotted RNAs. Each
description of RNA classification system is followed by
a discussion of the software tools and algorithms
developed to date to visualize RNA, comparing the
different tools' strengths and shortcomings.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Peng:2017:HNB,
author = "Chen Peng and Ao Li",
title = "A Heterogeneous Network Based Method for Identifying
{GBM}-Related Genes by Integrating Multi-Dimensional
Data",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "713--720",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2555314",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The emergence of multi-dimensional data offers
opportunities for more comprehensive analysis of the
molecular characteristics of human diseases and
therefore improving diagnosis, treatment, and
prevention. In this study, we proposed a heterogeneous
network based method by integrating multi-dimensional
data HNMD to identify GBM-related genes. The novelty of
the method lies in that the multi-dimensional data of
GBM from TCGA dataset that provide comprehensive
information of genes, are combined with protein-protein
interactions to construct a weighted heterogeneous
network, which reflects both the general and
disease-specific relationships between genes. In
addition, a propagation algorithm with resistance is
introduced to precisely score and rank GBM-related
genes. The results of comprehensive performance
evaluation show that the proposed method significantly
outperforms the network based methods with
single-dimensional data and other existing approaches.
Subsequent analysis of the top ranked genes suggests
they may be functionally implicated in GBM, which
further corroborates the superiority of the proposed
method. The source code and the results of HNMD can be
downloaded from the following URL:
http://bioinformatics.ustc.edu.cn/hnmd/ .",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Vasylchenkova:2017:CMA,
author = "Anastasiia Vasylchenkova and Miha Mraz and Nikolaj
Zimic and Miha Moskon",
title = "Classical Mechanics Approach Applied to Analysis of
Genetic Oscillators",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "721--727",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2550456",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Biological oscillators present a fundamental part of
several regulatory mechanisms that control the response
of various biological systems. Several analytical
approaches for their analysis have been reported
recently. They are, however, limited to only specific
oscillator topologies and/or to giving only qualitative
answers, i.e., is the dynamics of an oscillator given
the parameter space oscillatory or not. Here, we
present a general analytical approach that can be
applied to the analysis of biological oscillators. It
relies on the projection of biological systems to
classical mechanics systems. The approach is able to
provide us with relatively accurate results in the
meaning of type of behavior system reflects i.e.,
oscillatory or not and periods of potential
oscillations without the necessity to conduct expensive
numerical simulations. We demonstrate and verify the
proposed approach on three different implementations of
amplified negative feedback oscillator.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{daSilva:2017:GDH,
author = "Poly H. da Silva and Raphael Machado and Simone Dantas
and Marilia D. V. Braga",
title = "Genomic Distance with High Indel Costs",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "728--732",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2555301",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We determine complexity of computing the DCJ-indel
distance, when DCJ and indel operations have distinct
constant costs, by showing an exact formula that can be
computed in linear time for any choice of constant
costs for DCJ and indel operations. We additionally
consider the problem of triangular inequality
disruption and propose an algorithmically efficient
correction on each member of the family of DCJ-indel.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wu:2017:PCD,
author = "Min Wu and Le Ou-Yang and Xiao-Li Li",
title = "Protein Complex Detection via Effective Integration of
Base Clustering Solutions and Co-Complex Affinity
Scores",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "733--739",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2552176",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "With the increasing availability of protein
interaction data, various computational methods have
been developed to predict protein complexes. However,
different computational methods may have their own
advantages and limitations. Ensemble clustering has
thus been studied to minimize the potential bias and
risk of individual methods and generate prediction
results with better coverage and accuracy. In this
paper, we extend the traditional ensemble clustering by
taking into account the co-complex affinity scores and
present an Ensemble H ierarchical Clustering framework
EnsemHC to detect protein complexes. First, we
construct co-cluster matrices by integrating the
clustering results with the co-complex evidences.
Second, we sum up the constructed co-cluster matrices
to derive a final ensemble matrix via a novel iterative
weighting scheme. Finally, we apply the hierarchical
clustering to generate protein complexes from the final
ensemble matrix. Experimental results demonstrate that
our EnsemHC performs better than its base clustering
methods and various existing integrative methods. In
addition, we also observed that integrating the
clusters and co-complex affinity scores from different
data sources will improve the prediction performance,
e.g., integrating the clusters from TAP data and
co-complex affinities from binary PPI data achieved the
best performance in our experiments.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2017:RRB,
author = "Fangfei Li and Yang Tang",
title = "Robust Reachability of {Boolean} Control Networks",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "740--745",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2555302",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Boolean networks serve a powerful tool in analysis of
genetic regulatory networks since it emphasizes the
fundamental principles and establishes a nature
framework for capturing the dynamics of regulation of
cellular states. In this paper, the robust reachability
of Boolean control networks is investigated by means of
semi-tensor product. Necessary and sufficient
conditions for the robust reachability of Boolean
control networks are provided, in which control inputs
relying on disturbances or not are considered,
respectively. Besides, the corresponding control
algorithms are developed for these two cases. A reduced
model of the lac operon in the Escherichia coli is
presented to show the effectiveness of the presented
results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yan:2017:TNW,
author = "Zhangming Yan and Ke Liu and Shunian Xiang and Zhirong
Sun",
title = "{txCoords}: a Novel {Web} Application for
Transcriptomic Peak Re-Mapping",
journal = j-TCBB,
volume = "14",
number = "3",
pages = "746--748",
month = may,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2568178",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Since the development of new technologies such as
RIP-Seq and m6A-seq, peak calling has become an
important step in transcriptomic sequencing data
analysis. However, many of the reported genomic
coordinates of transcriptomic peaks are incorrect owing
to negligence of the introns. There is currently a lack
of a convenient tool to address this problem. Here, we
present txCoords, a novel and easy-to-use web
application for transcriptomic peak re-mapping.
txCoords can be used to correct the incorrectly
reported transcriptomic peaks and retrieve the true
sequences. It also supports visualization of the
re-mapped peaks in a schematic figure or from the UCSC
Genome Browser. Our web server is freely available at
http://www.bioinfo.tsinghua.edu.cn/txCoords.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jha:2017:GLB,
author = "Manjari Jha and Raunaq Malhotra and Raj Acharya",
title = "A Generalized Lattice Based Probabilistic Approach for
Metagenomic Clustering",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "749--761",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2563422",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Metagenomics involves the analysis of genomes of
microorganisms sampled directly from their environment.
Next Generation Sequencing allows a high-throughput
sampling of small segments from genomes in the
metagenome to generate reads. To study the properties
and relationships of the microorganisms present,
clustering can be performed based on the inherent
composition of the sampled reads for unknown species.
We propose a two-dimensional lattice based
probabilistic model for clustering metagenomic
datasets. The occurrence of a species in the metagenome
is estimated using a lattice of probabilistic
distributions over small sized genomic sequences. The
two dimensions denote distributions for different sizes
and groups of words, respectively. The lattice
structure allows for additional support for a node from
its neighbors when the probabilistic support for the
species using the parameters of the current node is
deemed insufficient. We also show convergence for our
algorithm. We test our algorithm on simulated
metagenomic data containing bacterial species and
observe more than 85\% precision. We also evaluate our
algorithm on an in vitro-simulated bacterial metagenome
and on human patient data, and show a better clustering
than other algorithms even for short reads and varied
abundance. The software and datasets can be downloaded
from
\url{https://github.com/lattclus/lattice-metage}.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bandyopadhyay:2017:NFV,
author = "Sanghamitra Bandyopadhyay and Koushik Mallick",
title = "A New Feature Vector Based on Gene Ontology Terms for
Protein--Protein Interaction Prediction",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "762--770",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2555304",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein-protein interaction PPI plays a key role in
understanding cellular mechanisms in different
organisms. Many supervised classifiers like Random
Forest RF and Support Vector Machine SVM have been used
for intra or inter-species interaction prediction. For
improving the prediction performance, in this paper we
propose a novel set of features to represent a protein
pair using their annotated Gene Ontology GO terms,
including their ancestors. In our approach, a protein
pair is treated as a document bag of words, where the
terms annotating the two proteins represent the words.
Feature value of each word is calculated using
information content of the corresponding term
multiplied by a coefficient, which represents the
weight of that term inside a document i.e., a protein
pair. We have tested the performance of the classifier
using the proposed feature on different well known data
sets of different species like S. cerevisiae, H.
Sapiens, E. Coli, and D. melanogaster. We compare it
with the other GO based feature representation
technique, and demonstrate its competitive
performance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Park:2017:NAP,
author = "Heewon Park and Yuichi Shiraishi and Seiya Imoto and
Satoru Miyano",
title = "A Novel Adaptive Penalized Logistic Regression for
Uncovering Biomarker Associated with Anti-Cancer Drug
Sensitivity",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "771--782",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2561937",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We propose a novel adaptive penalized logistic
regression modeling strategy based on Wilcoxon rank sum
test WRST to effectively uncover driver genes in
classification. In order to incorporate significance of
gene in classification, we first measure significance
of each gene by gene ranking method based on WRST, and
then the adaptive L $_1$-type penalty is discriminately
imposed on each gene depending on the measured
importance degree of gene. The incorporating
significance of genes into adaptive logistic regression
enables us to impose a large amount of penalty on low
ranking genes, and thus noise genes are easily deleted
from the model and we can effectively identify driver
genes. Monte Carlo experiments and real world example
are conducted to investigate effectiveness of the
proposed approach. In Sanger data analysis, we
introduce a strategy to identify expression modules
indicating gene regulatory mechanisms via the principal
component analysis PCA, and perform logistic regression
modeling based on not a single gene but gene expression
modules. We can see through Monte Carlo experiments and
real world example that the proposed adaptive penalized
logistic regression outperforms feature selection and
classification compared with existing L $_1$ -type
regularization. The discriminately imposed penalty
based on WRST effectively performs crucial gene
selection, and thus our method can improve
classification accuracy without interruption of noise
genes. Furthermore, it can be seen through Sanger data
analysis that the method for gene expression modules
based on principal components and their loading scores
provides interpretable results in biological
viewpoints.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2017:SAL,
author = "Chao Wang and Dong Dai and Xi Li and Aili Wang and
Xuehai Zhou",
title = "{SuperMIC}: Analyzing Large Biological Datasets in
Bioinformatics with Maximal Information Coefficient",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "783--795",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2550430",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The maximal information coefficient MIC has been
proposed to discover relationships and associations
between pairs of variables. It poses significant
challenges for bioinformatics scientists to accelerate
the MIC calculation, especially in genome sequencing
and biological annotations. In this paper, we explore a
parallel approach which uses MapReduce framework to
improve the computing efficiency and throughput of the
MIC computation. The acceleration system includes
biological data storage on HDFS, preprocessing
algorithms, distributed memory cache mechanism, and the
partition of MapReduce jobs. Based on the acceleration
approach, we extend the traditional two-variable
algorithm to multiple variables algorithm. The
experimental results show that our parallel solution
provides a linear speedup comparing with original
algorithm without affecting the correctness and
sensitivity.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nawab:2017:IBA,
author = "Rao Muhammad Adeel Nawab and Mark Stevenson and Paul
Clough",
title = "An {IR}-Based Approach Utilizing Query Expansion for
Plagiarism Detection in {MEDLINE}",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "796--804",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2542803",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The identification of duplicated and plagiarized
passages of text has become an increasingly active area
of research. In this paper, we investigate methods for
plagiarism detection that aim to identify potential
sources of plagiarism from MEDLINE, particularly when
the original text has been modified through the
replacement of words or phrases. A scalable approach
based on Information Retrieval is used to perform
candidate document selection-the identification of a
subset of potential source documents given a suspicious
text-from MEDLINE. Query expansion is performed using
the ULMS Metathesaurus to deal with situations in which
original documents are obfuscated. Various approaches
to Word Sense Disambiguation are investigated to deal
with cases where there are multiple Concept Unique
Identifiers CUIs for a given term. Results using the
proposed IR-based approach outperform a
state-of-the-art baseline based on Kullback--Leibler
Distance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Murugesan:2017:BMI,
author = "Sugeerth Murugesan and Kristopher Bouchard and Jesse
A. Brown and Bernd Hamann and William W. Seeley and
Andrew Trujillo and Gunther H. Weber",
title = "Brain Modulyzer: Interactive Visual Analysis of
Functional Brain Connectivity",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "805--818",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2564970",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present Brain Modulyzer, an interactive visual
exploration tool for functional magnetic resonance
imaging fMRI brain scans, aimed at analyzing the
correlation between different brain regions when
resting or when performing mental tasks. Brain
Modulyzer combines multiple coordinated views-such as
heat maps, node link diagrams, and anatomical
views-using brushing and linking to provide an
anatomical context for brain connectivity data.
Integrating methods from graph theory and analysis,
e.g., community detection and derived graph measures,
makes it possible to explore the modular and
hierarchical organization of functional brain networks.
Providing immediate feedback by displaying analysis
results instantaneously while changing parameters gives
neuroscientists a powerful means to comprehend complex
brain structure more effectively and efficiently and
supports forming hypotheses that can then be validated
via statistical analysis. To demonstrate the utility of
our tool, we present two case studies-exploring
progressive supranuclear palsy, as well as memory
encoding and retrieval.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Barragan:2017:COA,
author = "Sandra Barragan and Cristina Rueda and Miguel A.
Fernandez",
title = "Circular Order Aggregation and Its Application to
Cell-Cycle Genes Expressions",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "819--829",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2565469",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The aim of circular order aggregation is to find a
circular order on a set of $n$ items using angular
values from $p$ heterogeneous data sets. This problem
is new in the literature and has been motivated by the
biological question of finding the order among the peak
expression of a group of cell cycle genes. In this
paper, two very different approaches to solve the
problem that use pairwise and triplewise information
are proposed. Both approaches are analyzed and compared
using theoretical developments and numerical studies,
and applied to the cell cycle data that motivated the
problem.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2017:CFG,
author = "Jing Zhang and Hao Wang and Wu-chun Feng",
title = "{cuBLASTP}: Fine-Grained Parallelization of Protein
Sequence Search on {CPU + GPU}",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "830--843",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2489662",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "BLAST, short for Basic Local Alignment Search Tool, is
a ubiquitous tool used in the life sciences for
pairwise sequence search. However, with the advent of
next-generation sequencing NGS, whether at the outset
or downstream from NGS, the exponential growth of
sequence databases is outstripping our ability to
analyze the data. While recent studies have utilized
the graphics processing unit GPU to speedup the BLAST
algorithm for searching protein sequences i.e., BLASTP,
these studies use coarse-grained parallelism, where one
sequence alignment is mapped to only one thread. Such
an approach does not efficiently utilize the
capabilities of a GPU, particularly due to the
irregularity of BLASTP in both execution paths and
memory-access patterns. To address the above
shortcomings, we present a fine-grained approach to
parallelize BLASTP, where each individual phase of
sequence search is mapped to many threads on a GPU.
This approach, which we refer to as cuBLASTP, reorders
data-access patterns and reduces divergent branches of
the most time-consuming phases i.e., hit detection and
ungapped extension. In addition, cuBLASTP optimizes the
remaining phases i.e., gapped extension and alignment
with trace back on a multicore CPU and overlaps their
execution with the phases running on the GPU.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xie:2017:DFO,
author = "Lu Xie and Gregory R. Smith and Russell Schwartz",
title = "Derivative-Free Optimization of Rate Parameters of
Capsid Assembly Models from Bulk in Vitro Data",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "844--855",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2563421",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The assembly of virus capsids proceeds by a
complicated cascade of association and dissociation
steps, the great majority of which cannot be directly
experimentally observed. This has made capsid assembly
a rich field for computational models, but there are
substantial obstacles to model inference for such
systems. Here, we describe progress on fitting kinetic
rate constants defining capsid assembly models to
experimental data, a difficult data-fitting problem
because of the high computational cost of simulating
assembly trajectories, the stochastic noise inherent to
the models, and the limited and noisy data available
for fitting. We evaluate the merits of data-fitting
methods based on derivative-free optimization DFO
relative to gradient-based methods used in prior work.
We further explore the advantages of alternative data
sources through simulation of a model of time-resolved
mass spectrometry data, a technology for monitoring
bulk capsid assembly that can be expected to provide
much richer data than previously used static light
scattering approaches. The results show that advances
in both the data and the algorithms can improve model
inference. More informative data sources lead to
high-quality fits for all methods, but DFO methods show
substantial advantages on less informative data sources
that better represent current experimental practice.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2017:DCN,
author = "Yue Zhang and Yiu-ming Cheung and Bo Xu and Weifeng
Su",
title = "Detection Copy Number Variants from {NGS} with Sparse
and Smooth Constraints",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "856--867",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2561933",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "It is known that copy number variations CNVs are
associated with complex diseases and particular tumor
types, thus reliable identification of CNVs is of great
potential value. Recent advances in next generation
sequencing NGS data analysis have helped manifest the
richness of CNV information. However, the performances
of these methods are not consistent. Reliably finding
CNVs in NGS data in an efficient way remains a
challenging topic, worthy of further investigation.
Accordingly, we tackle the problem by formulating CNVs
identification into a quadratic optimization problem
involving two constraints. By imposing the constraints
of sparsity and smoothness, the reconstructed read
depth signal from NGS is anticipated to fit the CNVs
patterns more accurately. An efficient numerical
solution tailored from alternating direction
minimization ADM framework is elaborated. We
demonstrate the advantages of the proposed method,
namely ADM-CNV, by comparing it with six popular CNV
detection methods using synthetic, simulated, and
empirical sequencing data. It is shown that the
proposed approach can successfully reconstruct CNV
patterns from raw data, and achieve superior or
comparable performance in detection of the CNVs
compared to the existing counterparts.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fan:2017:FTS,
author = "Xiaofei Fan and Xian Zhang and Ligang Wu and Michael
Shi",
title = "Finite-Time Stability Analysis of Reaction-Diffusion
Genetic Regulatory Networks with Time-Varying Delays",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "868--879",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2552519",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper is concerned with the finite-time stability
problem of the delayed genetic regulatory networks GRNs
with reaction-diffusion terms under Dirichlet boundary
conditions. By constructing a Lyapunov-Krasovskii
functional including quad-slope integrations, we
establish delay-dependent finite-time stability
criteria by employing the Wirtinger-type integral
inequality, Gronwall inequality, convex technique, and
reciprocally convex technique. In addition, the
obtained criteria are also
reaction-diffusion-dependent. Finally, a numerical
example is provided to illustrate the effectiveness of
the theoretical results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pouyan:2017:ICP,
author = "Maziyar Baran Pouyan and Mehrdad Nourani",
title = "Identifying Cell Populations in Flow Cytometry Data
Using Phenotypic Signatures",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "880--891",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2550428",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Single-cell flow cytometry is a technology that
measures the expression of several cellular markers
simultaneously for a large number of cells.
Identification of homogeneous cell populations,
currently done by manual biaxial gating, is highly
subjective and time consuming. To overcome the
shortcomings of manual gating, automatic algorithms
have been proposed. However, the performance of these
methods highly depends on the shape of populations and
the dimension of the data. In this paper, we have
developed a time-efficient method that accurately
identifies cellular populations. This is done based on
a novel technique that estimates the initial number of
clusters in high dimension and identifies the final
clusters by merging clusters using their phenotypic
signatures in low dimension. The proposed method is
called SigClust. We have applied SigClust to four
public datasets and compared it with five well known
methods in the field. The results are promising and
indicate higher performance and accuracy compared to
similar approaches reported in literature.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Keller:2017:ISL,
author = "Anne Florence Keller and Nicolas Ambert and Arnaud
Legendre and Mathieu Bedez and Jean-Marie Bouteiller
and Serge Bischoff and Michel Baudry and Saliha
Moussaoui",
title = "Impact of Synaptic Localization and Subunit
Composition of Ionotropic Glutamate Receptors on
Synaptic Function: Modeling and Simulation Studies",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "892--904",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2561932",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Ionotropic NMDA and AMPA glutamate receptors iGluRs
play important roles in synaptic function under
physiological and pathological conditions. iGluRs
sub-synaptic localization and subunit composition are
dynamically regulated by activity-dependent insertion
and internalization. However, understanding the impact
on synaptic transmission of changes in composition and
localization of iGluRs is difficult to address
experimentally. To address this question, we developed
a detailed computational model of glutamatergic
synapses, including spine and dendritic compartments,
elementary models of subtypes of NMDA and AMPA
receptors, glial glutamate transporters, intracellular
calcium, and a calcium-dependent signaling cascade
underlying the development of long-term potentiation
LTP. These synapses were distributed on a neuron model
and numerical simulations were performed to assess the
impact of changes in composition and localization
synaptic versus extrasynaptic of iGluRs on synaptic
transmission and plasticity following various patterns
of presynaptic stimulation. In addition, the effects of
various pharmacological compounds targeting NMDARs or
AMPARs were determined. Our results showed that changes
in NMDAR localization have a greater impact on synaptic
plasticity than changes in AMPARs. Moreover, the
results suggest that modulators of AMPA and NMDA
receptors have differential effects on restoring
synaptic plasticity under different experimental
situations mimicking various human diseases.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2017:IMD,
author = "Yuansheng Liu and Xiangxiang Zeng and Zengyou He and
Quan Zou",
title = "Inferring {MicroRNA}-Disease Associations by Random
Walk on a Heterogeneous Network with Multiple Data
Sources",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "905--915",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2550432",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Since the discovery of the regulatory function of
microRNA miRNA, increased attention has focused on
identifying the relationship between miRNA and disease.
It has been suggested that computational method is an
efficient way to identify potential disease-related
miRNAs for further confirmation using biological
experiments. In this paper, we first highlighted three
limitations commonly associated with previous
computational methods. To resolve these limitations, we
established disease similarity subnetwork and miRNA
similarity subnetwork by integrating multiple data
sources, where the disease similarity is composed of
disease semantic similarity and disease functional
similarity, and the miRNA similarity is calculated
using the miRNA-target gene and miRNA-lncRNA long
non-coding RNA associations. Then, a heterogeneous
network was constructed by connecting the disease
similarity subnetwork and the miRNA similarity
subnetwork using the known miRNA-disease associations.
We extended random walk with restart to predict
miRNA-disease associations in the heterogeneous
network. The leave-one-out cross-validation achieved an
average area under the curve AUC of $ 0.8049 $ across $
341 $ diseases and $ 476 $ miRNAs. For five-fold
cross-validation, our method achieved an AUC from $
0.7970 $ to $ 0.9249 $ for $ 15 $ human diseases. Case
studies further demonstrated the feasibility of our
method to discover potential miRNA-disease
associations. An online service for prediction is
freely available at http://ifmda.aliapp.com.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2017:IIS,
author = "Min Li and Zhongxiang Liao and Yiming He and Jianxin
Wang and Junwei Luo and Yi Pan",
title = "{ISEA}: Iterative Seed-Extension Algorithm for {De}
{Novo} Assembly Using Paired-End Information and Insert
Size Distribution",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "916--925",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2550433",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The purpose of de novo assembly is to report more
contiguous, complete, and less error prone contigs.
Thanks to the advent of the next generation sequencing
NGS technologies, the cost of producing high depth
reads is reduced greatly. However, due to the
disadvantages of NGS, de novo assembly has to face the
difficulties brought by repeat regions, error rate, and
low sequencing coverage in some regions. Although many
de novo algorithms have been proposed to solve these
problems, the de novo assembly still remains a
challenge. In this article, we developed an iterative
seed-extension algorithm for de novo assembly, called
ISEA. To avoid the negative impact induced by error
rate, ISEA utilizes reads overlap and paired-end
information to correct error reads before assemblying.
During extending seeds in a De Bruijn graph, ISEA uses
an elaborately designed score function based on
paired-end information and the distribution of insert
size to solve the repeat region problem. By employing
the distribution of insert size, the score function can
also reduce the influence of error reads. In
scaffolding, ISEA adopts a relaxed strategy to join
contigs that were terminated for low coverage during
the extension. The performance of ISEA was compared
with six previous popular assemblers on four real
datasets. The experimental results demonstrate that
ISEA can effectively obtain longer and more accurate
scaffolds.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ma:2017:OIS,
author = "Tianle Ma and Aidong Zhang",
title = "Omics Informatics: From Scattered Individual Software
Tools to Integrated Workflow Management Systems",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "926--946",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2535251",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Omic data analyses pose great informatics challenges.
As an emerging subfield of bioinformatics, omics
informatics focuses on analyzing multi-omic data
efficiently and effectively, and is gaining momentum.
There are two underlying trends in the expansion of
omics informatics landscape: the explosion of scattered
individual omics informatics tools with each of which
focuses on a specific task in both single- and multi-
omic settings, and the fast-evolving integrated
software platforms such as workflow management systems
that can assemble multiple tools into pipelines and
streamline integrative analysis for complicated tasks.
In this survey, we give a holistic view of omics
informatics, from scattered individual informatics
tools to integrated workflow management systems. We not
only outline the landscape and challenges of omics
informatics, but also sample a number of widely used
and cutting-edge algorithms in omics data analysis to
give readers a fine-grained view. We survey various
workflow management systems WMSs, classify them into
three levels of WMSs from simple software toolkits to
integrated multi-omic analytical platforms, and point
out the emerging needs for developing intelligent
workflow management systems. We also discuss the
challenges, strategies and some existing work in
systematic evaluation of omics informatics tools. We
conclude by providing future perspectives of emerging
fields and new frontiers in omics informatics.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2017:PCI,
author = "Jianhua Zhang and Zhong Yin and Rubin Wang",
title = "Pattern Classification of Instantaneous Cognitive
Task-load Through {GMM} Clustering, {Laplacian}
Eigenmap, and Ensemble {SVMs}",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "947--965",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2561927",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The identification of the temporal variations in human
operator cognitive task-load CTL is crucial for
preventing possible accidents in human-machine
collaborative systems. Recent literature has shown that
the change of discrete CTL level during human-machine
system operations can be objectively recognized using
neurophysiological data and supervised learning
technique. The objective of this work is to design
subject-specific multi-class CTL classifier to reveal
the complex unknown relationship between the operator's
task performance and neurophysiological features by
combining target class labeling, physiological feature
reduction and selection, and ensemble classification
techniques. The psychophysiological data acquisition
experiments were performed under multiple human-machine
process control tasks. Four or five target classes of
CTL were determined by using a Gaussian mixture model
and three human performance variables. By using
Laplacian eigenmap, a few salient EEG features were
extracted, and heart rates were used as the input
features of the CTL classifier. Then, multiple support
vector machines were aggregated via majority voting to
create an ensemble classifier for recognizing the CTL
classes. Finally, the obtained CTL classification
results were compared with those of several existing
methods. The results showed that the proposed methods
are capable of deriving a reasonable number of target
classes and low-dimensional optimal EEG features for
individual human operator subjects.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2017:PND,
author = "Liang Yu and Ruidan Su and Bingbo Wang and Long Zhang
and Yapeng Zou and Jing Zhang and Lin Gao",
title = "Prediction of Novel Drugs for Hepatocellular Carcinoma
Based on Multi-Source Random Walk",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "966--977",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2550453",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Computational approaches for predicting drug-disease
associations by integrating gene expression and
biological network provide great insights to the
complex relationships among drugs, targets, disease
genes, and diseases at a system level. Hepatocellular
carcinoma HCC is one of the most common malignant
tumors with a high rate of morbidity and mortality. We
provide an integrative framework to predict novel d
rugs for HCC based on multi-source random walk PD-MRW.
Firstly, based on gene expression and protein
interaction network, we construct a gene-gene weighted
interaction network GWIN. Then, based on multi-source
random walk in GWIN, we build a drug-drug similarity
network. Finally, based on the known drugs for HCC, we
score all drugs in the drug-drug similarity network.
The robustness of our predictions, their overlap with
those reported in Comparative Toxicogenomics Database
CTD and literatures, and their enriched KEGG pathway
demonstrate our approach can effectively identify new
drug indications. Specifically, regorafenib Rank = 9 in
top-20 list is proven to be effective in Phase I and II
clinical trials of HCC, and the Phase III trial is
ongoing. And, it has 11 overlapping pathways with HCC
with lower p-values. Focusing on a particular disease,
we believe our approach is more accurate and possesses
better scalability.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Stalidzans:2017:SMS,
author = "Egils Stalidzans and Ivars Mozga and Jurijs Sulins and
Peteris Zikmanis",
title = "Search for a Minimal Set of Parameters by Assessing
the Total Optimization Potential for a Dynamic Model of
a Biochemical Network",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "978--985",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2550451",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Selecting an efficient small set of adjustable
parameters to improve metabolic features of an organism
is important for a reduction of implementation costs
and risks of unpredicted side effects. In practice, to
avoid the analysis of a huge combinatorial space for
the possible sets of adjustable parameters,
experience-, and intuition-based subsets of parameters
are often chosen, possibly leaving some interesting
counter-intuitive combinations of parameters
unrevealed. The combinatorial scan of possible
adjustable parameter combinations at the model
optimization level is possible; however, the number of
analyzed combinations is still limited. The total
optimization potential TOP approach is proposed to
assess the full potential for increasing the value of
the objective function by optimizing all possible
adjustable parameters. This seemingly unpractical
combination of adjustable parameters allows assessing
the maximum attainable value of the objective function
and stopping the combinatorial space scanning when the
desired fraction of TOP is reached and any further
increase in the number of adjustable parameters cannot
bring any reasonable improvement. The relation between
the number of adjustable parameters and the reachable
fraction of TOP is a valuable guideline in choosing a
rational solution for industrial implementation. The
TOP approach is demonstrated on the basis of two case
studies.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dinc:2017:STS,
author = "Imren Dinc and Semih Dinc and Madhav Sigdel and Madhu
S. Sigdel and Marc L. Pusey and Ramazan S. Aygun",
title = "Super-Thresholding: Supervised Thresholding of Protein
Crystal Images",
journal = j-TCBB,
volume = "14",
number = "4",
pages = "986--998",
month = jul,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2542811",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Oct 3 16:58:45 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In general, a single thresholding technique is
developed or enhanced to separate foreground objects
from background for a domain of images. This idea may
not generate satisfactory results for all images in a
dataset, since different images may require different
types of thresholding methods for proper binarization
or segmentation. To overcome this limitation, in this
study, we propose a novel approach called
``super-thresholding'' that utilizes a supervised
classifier to decide an appropriate thresholding method
for a specific image. This method provides a generic
framework that allows selection of the best
thresholding method among different thresholding
techniques that are beneficial for the problem domain.
A classifier model is built using features extracted
priori from the original image only or posteriori by
analyzing the outputs of thresholding methods and the
original image. This model is applied to identify the
thresholding method for new images of the domain. We
performed our method on protein crystallization images,
and then we compared our results with six thresholding
techniques. Numerical results are provided using four
different correctness measurements. Super-thresholding
outperforms the best single thresholding method around
10 percent, and it gives the best performance for
protein crystallization dataset in our experiments.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Catalyurek:2017:GEI,
author = "Umit V. Catalyurek",
title = "{Guest Editor}'s Introduction: Selected Papers from
{ACM-BCB 2014}",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1000--1001",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2722158",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The papers in this special issue were presented at the
5th ACM Conference on Bioinformatics, Computational
Biology, and Health Informatics, held in Newport Beach,
CA in September 2014, The papers address the use of
computational modeling in the biological and health
research fields. With the new high throughput devices,
such as sequencers and imaging devices, and ubiquitous
sensor technologies, the landscape of how we do
biomedical research; how the knowledge is curated; and
results are delivered to stake holders are constantly
changing.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gorecki:2017:UDD,
author = "Pawel Pawel Gorecki and Jaroslaw Paszek and Oliver
Eulenstein",
title = "Unconstrained Diameters for Deep Coalescence",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1002--1012",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2520937",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The minimizing-deep-coalescence MDC approach infers a
median species tree for a given set of gene trees under
the deep coalescence cost. This cost accounts for the
minimum number of deep coalescences needed to reconcile
a gene tree with a species tree where the leaf-genes
are mapped to the leaf-species through a function
called leaf labeling. In order to better understand the
MDC approach we investigate here the diameter of a gene
tree, which is an important property of the deep
coalescence cost. This diameter is the maximal deep
coalescence costs for a given gene tree under all leaf
labelings for each possible species tree topology.
While we prove that this diameter is generally
infinite, this result relies on the diameter's
unrealistic assumption that species trees can be of
infinite size. Providing a more practical definition,
we introduce a natural extension of the gene tree
diameter that constrains the species tree size by a
given constant. For this new diameter, we describe an
exact formula, present a complete classification of the
trees yielding this diameter, derive formulas for its
mean and variance, and demonstrate its ability using
comparative studies.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2017:SLF,
author = "Zhiyong Wang and Benika Hall and Jinbo Xu and Xinghua
Shi",
title = "A Sparse Learning Framework for Joint Effect Analysis
of Copy Number Variants",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1013--1027",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2462332",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Copy number variants CNVs, including large deletions
and duplications, represent an unbalanced change of DNA
segments. Abundant in human genomes, CNVs contribute to
a large proportion of human genetic diversity, with
impact on many human phenotypes. Although recent
advances in genetic studies have shed light on the
impact of individual CNVs on different traits, the
analysis of joint effect of multiple interactive CNVs
lags behind from many perspectives. A primary reason is
that the large number of CNV combinations and
interactions in the human genome make it
computationally challenging to perform such joint
analysis. To address this challenge, we developed a
novel sparse learning framework that combines sparse
learning with biological networks to identify
interacting CNVs with joint effect on particular
traits. We showed that our approach performs well in
identifying CNVs with joint phenotypic effect using
simulated data. Applied to a real human genomic dataset
from the 1,000 Genomes Project, our approach identified
multiple CNVs that collectively contribute to
population differentiation. We found a set of multiple
CNVs that have joint effect in different populations,
and affect gene expression differently in distinct
populations. These results provided a collection of
CNVs that likely have downstream biomedical
implications in individuals from diverse population
backgrounds.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{DeBlasio:2017:LPA,
author = "Dan DeBlasio and John Kececioglu",
title = "Learning Parameter-Advising Sets for Multiple Sequence
Alignment",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1028--1041",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2430323",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "While the multiple sequence alignment output by an
aligner strongly depends on the parameter values used
for the alignment scoring function such as the choice
of gap penalties and substitution scores, most users
rely on the single default parameter setting provided
by the aligner. A different parameter setting, however,
might yield a much higher-quality alignment for the
specific set of input sequences. The problem of picking
a good choice of parameter values for specific input
sequences is called parameter advising. A parameter
advisor has two ingredients: i a set of parameter
choices to select from, and ii an estimator that
provides an estimate of the accuracy of the alignment
computed by the aligner using a parameter choice. The
parameter advisor picks the parameter choice from the
set whose resulting alignment has highest estimated
accuracy. In this paper, we consider for the first time
the problem of learning the optimal set of parameter
choices for a parameter advisor that uses a given
accuracy estimator. The optimal set is one that
maximizes the expected true accuracy of the resulting
parameter advisor, averaged over a collection of
training data. While we prove that learning an optimal
set for an advisor is NP-complete, we show there is a
natural approximation algorithm for this problem, and
prove a tight bound on its approximation ratio.
Experiments with an implementation of this
approximation algorithm on biological benchmarks, using
various accuracy estimators from the literature, show
it finds sets for advisors that are surprisingly close
to optimal. Furthermore, the resulting parameter
advisors are significantly more accurate in practice
than simply aligning with a single default parameter
choice.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ritz:2017:PAS,
author = "Anna Ritz and Brendan Avent and T. M. Murali",
title = "Pathway Analysis with Signaling Hypergraphs",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1042--1055",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2459681",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Signaling pathways play an important role in the
cell's response to its environment. Signaling pathways
are often represented as directed graphs, which are not
adequate for modeling reactions such as complex
assembly and dissociation, combinatorial regulation,
and protein activation/inactivation. More accurate
representations such as directed hypergraphs remain
underutilized. In this paper, we present an extension
of a directed hypergraph that we call a signaling
hypergraph. We formulate a problem that asks what
proteins and interactions must be involved in order to
stimulate a specific response downstream of a signaling
pathway. We relate this problem to computing the
shortest acyclic $B$-hyperpath in a signaling
hypergraph-an NP-hard problem-and present a mixed
integer linear program to solve it. We demonstrate that
the shortest hyperpaths computed in signaling
hypergraphs are far more informative than shortest
paths, Steiner trees, and subnetworks containing many
short paths found in corresponding graph
representations. Our results illustrate the potential
of signaling hypergraphs as an improved representation
of signaling pathways and motivate the development of
novel hypergraph algorithms.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yoo:2017:IIK,
author = "Boyoung Yoo and Fazle Elahi Faisal and Huili Chen and
Tijana Milenkovic",
title = "Improving Identification of Key Players in Aging via
Network De-Noising and Core Inference",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1056--1069",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2495170",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Current ``ground truth'' knowledge about human aging
has been obtained by transferring aging-related
knowledge from well-studied model species via sequence
homology or by studying human gene expression data.
Since proteins function by interacting with each other,
analyzing protein-protein interaction PPI networks in
the context of aging is promising. Unlike existing
static network research of aging, since cellular
functioning is dynamic, we recently integrated the
static human PPI network with aging-related gene
expression data to form dynamic, age-specific networks.
Then, we predicted as key players in aging those
proteins whose network topologies significantly changed
with age. Since current networks are noisy , here, we
use link prediction to de-noise the human network and
predict improved key players in aging from the
de-noised data. Indeed, de-noising gives more
significant overlap between the predicted data and the
``ground truth'' aging-related data. Yet, we obtain
novel predictions, which we validate in the literature.
Also, we improve the predictions by an alternative
strategy: removing ``redundant'' edges from the
age-specific networks and using the resulting
age-specific network ``cores'' to study aging. We
produce new knowledge from dynamic networks
encompassing multiple data types, via network
de-noising or core inference, complementing the
existing knowledge obtained from sequence or expression
data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Azofeifa:2017:AAA,
author = "Joseph G. Azofeifa and Mary A. Allen and Manuel E.
Lladser and Robin D. Dowell",
title = "An Annotation Agnostic Algorithm for Detecting Nascent
{RNA} Transcripts in {GRO-Seq}",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1070--1081",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2520919",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We present a fast and simple algorithm to detect
nascent RNA transcription in global nuclear run-on
sequencing GRO-seq. GRO-seq is a relatively new
protocol that captures nascent transcripts from
actively engaged polymerase, providing a direct
read-out on bona fide transcription. Most traditional
assays, such as RNA-seq, measure steady state RNA
levels which are affected by transcription,
post-transcriptional processing, and RNA stability.
GRO-seq data, however, presents unique analysis
challenges that are only beginning to be addressed.
Here, we describe a new algorithm, Fast Read Stitcher
FStitch, that takes advantage of two popular
machine-learning techniques, hidden Markov models and
logistic regression, to classify which regions of the
genome are transcribed. Given a small user-defined
training set, our algorithm is accurate, robust to
varying read depth, annotation agnostic, and fast.
Analysis of GRO-seq data without a priori need for
annotation uncovers surprising new insights into
several aspects of the transcription process.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Goodacre:2017:PND,
author = "Norman Goodacre and Nathan Edwards and Mark Danielsen
and Peter Uetz and Cathy Wu",
title = "Predicting {nsSNPs} that Disrupt Protein-Protein
Interactions Using Docking",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1082--1093",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2520931",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The human genome contains a large number of protein
polymorphisms due to individual genome variation. How
many of these polymorphisms lead to altered
protein-protein interaction is unknown. We have
developed a method to address this question. The
intersection of the SKEMPI database of affinity
constants among interacting proteins and CAPRI 4.0
docking benchmark was docked using HADDOCK, leading to
a training set of 166 mutant pairs. A random forest
classifier based on the differences in resulting
docking scores between the 166 mutant pairs and their
wild-types was used, to distinguish between variants
that have either completely or partially lost binding
ability. Fifty percent of non-binders were correctly
predicted with a false discovery rate of only 2
percent. The model was tested on a set of 15 HIV-1 ---
human, as well as seven human- human
glioblastoma-related, mutant protein pairs: 50 percent
of combined non-binders were correctly predicted with a
false discovery rate of 10 percent. The model was also
used to identify 10 protein-protein interactions
between human proteins and their HIV-1 partners that
are likely to be abolished by rare non-synonymous
single-nucleotide polymorphisms nsSNPs. These nsSNPs
may represent novel and potentially
therapeutically-valuable targets for anti-viral therapy
by disruption of viral binding.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{He:2017:IIP,
author = "Dan He and Zhanyong Wang and Laxmi Parida and Eleazar
Eskin",
title = "{IPED2}: Inheritance Path Based Pedigree
Reconstruction Algorithm for Complicated Pedigrees",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1094--1103",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2688439",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Reconstruction of family trees, or pedigree
reconstruction, for a group of individuals is a
fundamental problem in genetics. The problem is known
to be NP-hard even for datasets known to only contain
siblings. Some recent methods have been developed to
accurately and efficiently reconstruct pedigrees. These
methods, however, still consider relatively simple
pedigrees, for example, they are not able to handle
half-sibling situations where a pair of individuals
only share one parent. In this work, we propose an
efficient method, IPED2, based on our previous work,
which specifically targets reconstruction of
complicated pedigrees that include half-siblings. We
note that the presence of half-siblings makes the
reconstruction problem significantly more challenging
which is why previous methods exclude the possibility
of half-siblings. We proposed a novel model as well as
an efficient graph algorithm and experiments show that
our algorithm achieves relatively accurate
reconstruction. To our knowledge, this is the first
method that is able to handle pedigree reconstruction
from genotype data when half-sibling exists in any
generation of the pedigree.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huang:2017:GES,
author = "De-Shuang Huang and Vitoantonio Bevilacqua and M.
Michael Gromiha",
title = "Guest Editorial for Special Section on the {11th
International Conference on Intelligent Computing
ICIC}",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1104--1105",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2677098",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The papers in this special section were presented at
the 11th International Conference on Intelligent
Computing ICIC held in Fuzhou, China, on August 20-23,
2015. This conference was formed to provide an annual
forum dedicated to the emerging and challenging topics
in artificial intelligence, machine learning,
bioinformatics, etc. It aims to bring together
researchers and practitioners from both academia and
industry to share ideas, problems, and solutions
related to the multifaceted aspects of intelligent
computing.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wu:2017:DCR,
author = "Hongjie Wu and Kun Wang and Liyao Lu and Yu Xue and
Qiang Lyu and Min Jiang",
title = "Deep Conditional Random Field Approach to
Transmembrane Topology Prediction and Application to
{GPCR} Three-Dimensional Structure Modeling",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1106--1114",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2602872",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Transmembrane proteins play important roles in
cellular energy production, signal transmission, and
metabolism. Many shallow machine learning methods have
been applied to transmembrane topology prediction, but
the performance was limited by the large size of
membrane proteins and the complex biological evolution
information behind the sequence. In this paper, we
proposed a novel deep approach based on conditional
random fields named as dCRF-TM for predicting the
topology of transmembrane proteins. Conditional random
fields take into account more complicated interrelation
between residue labels in full-length sequence than HMM
and SVM-based methods. Three widely-used datasets were
employed in the benchmark. DCRF-TM had the accuracy 95
percent over helix location prediction and the accuracy
78 percent over helix number prediction. DCRF-TM
demonstrated a more robust performance on large size
proteins {$>$350} residues against 11 state-of-the-art
predictors. Further dCRF-TM was applied to ab initio
modeling three-dimensional structures of
seven-transmembrane receptors, also known as G
protein-coupled receptors. The predictions on 24 solved
G protein-coupled receptors and unsolved vasopressin V2
receptor illustrated that dCRF-TM helped
abGPCR-I-TASSER to improve TM-score 34.3 percent rather
than using the random transmembrane definition. Two out
of five predicted models caught the experimental
verified disulfide bonds in vasopressin V2 receptor.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ge:2017:CSD,
author = "Shu-Guang Ge and Junfeng Xia and Wen Sha and Chun-Hou
Zheng",
title = "Cancer Subtype Discovery Based on Integrative Model of
Multigenomic Data",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1115--1121",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2621769",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "One major goal of large-scale cancer omics study is to
understand molecular mechanisms of cancer and find new
biomedical targets. To deal with the high-dimensional
multidimensional cancer omics data DNA methylation,
mRNA expression, etc., which can be used to discover
new insight on identifying cancer subtypes, clustering
methods are usually used to find an effective
low-dimensional subspace of the original data and then
cluster cancer samples in the reduced subspace.
However, due to data-type diversity and big data
volume, few methods can integrate these data and map
them into an effective low-dimensional subspace. In
this paper, we develop a dimension-reduction and
data-integration method for indentifying cancer
subtypes, named Scluster. First, Scluster,
respectively, projects the different original data into
the principal subspaces by an adaptive sparse
reduced-rank regression method. Then, a fused
patient-by-patient network is obtained for these
subgroups through a scaled exponential similarity
kernel method. Finally, candidate cancer subtypes are
identified using spectral clustering method. We
demonstrate the efficiency of our Scluster method using
three cancers by jointly analyzing mRNA expression,
miRNA expression, and DNA methylation data. The
evaluation results and analyses show that Scluster is
effective for predicting survival and identifies novel
cancer subtypes of large-scale multi-omics data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bao:2017:CPS,
author = "Wenzheng Bao and Dong Wang and Yuehui Chen",
title = "Classification of Protein Structure Classes on
Flexible Neutral Tree",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1122--1133",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2610967",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Accurate classification on protein structural is
playing an important role in Bioinformatics. An
increase in evidence demonstrates that a variety of
classification methods have been employed in such a
field. In this research, the features of amino acids
composition, secondary structure's feature, and
correlation coefficient of amino acid dimers and amino
acid triplets have been used. Flexible neutral tree
FNT, a particular tree structure neutral network, has
been employed as the classification model in the
protein structures' classification framework.
Considering different feature groups owing diverse
roles in the model, impact factors of different groups
have been put forward in this research. In order to
evaluate different impact factors, Impact Factors
Scaling IFS algorithm, which aim at reducing redundant
information of the selected features in some degree,
have been put forward. To examine the performance of
such framework, the 640, 1189, and ASTRAL datasets are
employed as the low-homology protein structure
benchmark datasets. Experimental results demonstrate
that the performance of the proposed method is better
than the other methods in the low-homology protein
tertiary structures.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2017:ECR,
author = "Qingfeng Chen and Chaowang Lan and Baoshan Chen and
Lusheng Wang and Jinyan Li and Chengqi Zhang",
title = "Exploring Consensus {RNA} Substructural Patterns Using
Subgraph Mining",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1134--1146",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2645202",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Frequently recurring RNAid=``Q1''{$>$} structural
motifs play important roles in RNA folding process and
interaction with other molecules. Traditional
index-based and shape-based schemas are useful in
modeling RNA secondary structures but ignore the
structural discrepancy of individual RNA family member.
Further, the in-depth analysis of underlying
substructure pattern is insufficient due to varied and
unnormalized substructure data. This prevents us from
understanding RNAs functions and their inherent
synergistic regulation networks. This article thus
proposes a novel labeled graph-based algorithm RnaGraph
to uncover frequently RNA substructure patterns.
Attribute data and graph data are combined to
characterize diverse substructures and their
correlations, respectively. Further, a top-k graph
pattern mining algorithm is developed to extract
interesting substructure motifs by integrating
frequency and similarity. The experimental results show
that our methods assist in not only modelling complex
RNA secondary structures but also identifying hidden
but interesting RNA substructure patterns.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Deng:2017:ISK,
author = "Su-Ping Deng and Shaolong Cao and De-Shuang Huang and
Yu-Ping Wang",
title = "Identifying Stages of Kidney Renal Cell Carcinoma by
Combining Gene Expression and {DNA} Methylation Data",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1147--1153",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2607717",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this study, in order to take advantage of
complementary information from different types of data
for better disease status diagnosis, we combined gene
expression with DNA methylation data and generated a
fused network, based on which the stages of Kidney
Renal Cell Carcinoma KIRC can be better identified. It
is well recognized that a network is important for
investigating the connectivity of disease groups. We
exploited the potential of the network's features to
identify the KIRC stage. We first constructed a patient
network from each type of data. We then built a fused
network based on network fusion method. Based on the
link weights of patients, we used a generalized linear
model to predict the group of KIRC subjects. Finally,
the group prediction method was applied to test the
power of network-based features. The performance e.g.,
the accuracy of identifying cancer stages when using
the fused network from two types of data is shown to be
superior to that when using two patient networks from
only one data type. The work provides a good example
for using network based features from multiple data
types for a more comprehensive diagnosis.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yuan:2017:NPB,
author = "Lin Yuan and Lin Zhu and Wei-Li Guo and Xiaobo Zhou
and Youhua Zhang and Zhenhua Huang and De-Shuang
Huang",
title = "Nonconvex Penalty Based Low-Rank Representation and
Sparse Regression for {eQTL} Mapping",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1154--1164",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2609420",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper addresses the problem of accounting for
confounding factors and expression quantitative trait
loci eQTL mapping in the study of SNP-gene
associations. The existing convex penalty based
algorithm has limited capacity to keep main information
of matrix in the process of reducing matrix rank. We
present an algorithm, which use nonconvex penalty based
low-rank representation to account for confounding
factors and make use of sparse regression for eQTL
mapping NCLRS. The efficiency of the presented
algorithm is evaluated by comparing the results of 18
synthetic datasets given by NCLRS and presented
algorithm, respectively. The experimental results or
biological dataset show that our approach is an
effective tool to account for non-genetic effects than
currently existing methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2017:PSP,
author = "Jian-Qiang Li and Zhu-Hong You and Xiao Li and Zhong
Ming and Xing Chen",
title = "{PSPEL}: In Silico Prediction of Self-Interacting
Proteins from Amino Acids Sequences Using Ensemble
Learning",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1165--1172",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2649529",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Self interacting proteins SIPs play an important role
in various aspects of the structural and functional
organization of the cell. Detecting SIPs is one of the
most important issues in current molecular biology.
Although a large number of SIPs data has been generated
by experimental methods, wet laboratory approaches are
both time-consuming and costly. In addition, they yield
high false negative and positive rates. Thus, there is
a great need for in silico methods to predict SIPs
accurately and efficiently. In this study, a new
sequence-based method is proposed to predict SIPs. The
evolutionary information contained in Position-Specific
Scoring Matrix PSSM is extracted from of protein with
known sequence. Then, features are fed to an ensemble
classifier to distinguish the self-interacting and
non-self-interacting proteins. When performed on
Saccharomyces cerevisiae and Human SIPs data sets, the
proposed method can achieve high accuracies of 86.86
and 91.30 percent, respectively. Our method also shows
a good performance when compared with the SVM
classifier and previous methods. Consequently, the
proposed method can be considered to be a novel
promising tool to predict SIPs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2017:HBH,
author = "Pu Wang and Ruiquan Ge and Xuan Xiao and Manli Zhou
and Fengfeng Zhou",
title = "{hMuLab}: a Biomedical Hybrid {MUlti-LABel} Classifier
Based on Multiple Linear Regression",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1173--1180",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2603507",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Many biomedical classification problems are
multi-label by nature, e.g., a gene involved in a
variety of functions and a patient with multiple
diseases. The majority of existing classification
algorithms assumes each sample with only one class
label, and the multi-label classification problem
remains to be a challenge for biomedical researchers.
This study proposes a novel multi-label learning
algorithm, hMuLab, by integrating both feature-based
and neighbor-based similarity scores. The multiple
linear regression modeling techniques make hMuLab
capable of producing multiple label assignments for a
query sample. The comparison results over six
commonly-used multi-label performance measurements
suggest that hMuLab performs accurately and stably for
the biomedical datasets, and may serve as a complement
to the existing literature.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Muki-Marttunen:2017:AMB,
author = "Tuomo Muki-Marttunen",
title = "An Algorithm for Motif-Based Network Design",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1181--1186",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2576442",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A determinant property of the structure of a
biological network is the distribution of local
connectivity patterns, i.e., network motifs. In this
work, a method for creating directed, unweighted
networks while promoting a certain combination of
motifs is presented. This motif-based network algorithm
starts with an empty graph and randomly connects the
nodes by advancing or discouraging the formation of
chosen motifs. The in- or out-degree distribution of
the generated networks can be explicitly chosen. The
algorithm is shown to perform well in producing
networks with high occurrences of the targeted motifs,
both ones consisting of three nodes as well as ones
consisting of four nodes. Moreover, the algorithm can
also be tuned to bring about global network
characteristics found in many natural networks, such as
small-worldness and modularity.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ma:2017:EBS,
author = "Lichun Ma and Debby D. Wang and Bin Zou and Hong Yan",
title = "An Eigen-Binding Site Based Method for the Analysis of
Anti-{EGFR} Drug Resistance in Lung Cancer Treatment",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1187--1194",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2568184",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We explore the drug resistance mechanism in non-small
cell lung cancer treatment by characterizing the
drug-binding site of a protein mutant based on local
surface and energy features. These features are
transformed to an eigen-binding site space and used for
drug resistance level prediction and analysis.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{dAcierno:2017:IID,
author = "Antonio dAcierno",
title = "{IsAProteinDB}: an Indexed Database of Trypsinized
Proteins for Fast Peptide Mass Fingerprinting",
journal = j-TCBB,
volume = "14",
number = "5",
pages = "1195--1201",
month = sep,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2564964",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In peptite mass fingerprinting, an unknown protein is
fragmented into smaller peptides whose masses are
accurately measured; the obtained list of weights is
then compared with a reference database to obtain a set
of matching proteins. The exponential growth of known
proteins discourage the use of brute force methods,
where the weights' list is compared with each protein
in the reference collection; luckily, the scientific
literature in the database field highlights that well
designed searching algorithms, coupled with a proper
data organization, allow to quickly solve the
identification problem even on standard desktop
computers. In this paper, IsAProteinsDB, an indexed
database of trypsinized proteins, is presented. The
corresponding search algorithm shows a time complexity
that does not significantly depends on the size of the
reference protein database.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kawam:2017:SSH,
author = "Ahmad {Al Kawam} and Sunil Khatri and Aniruddha
Datta",
title = "A Survey of Software and Hardware Approaches to
Performing Read Alignment in Next Generation
Sequencing",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1202--1213",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2586070",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Computational genomics is an emerging field that is
enabling us to reveal the origins of life and the
genetic basis of diseases such as cancer. Next
Generation Sequencing NGS technologies have unleashed a
wealth of genomic information by producing immense
amounts of raw data. Before any functional analysis can
be applied to this data, read alignment is applied to
find the genomic coordinates of the produced sequences.
Alignment algorithms have evolved rapidly with the
advancement in sequencing technology, striving to
achieve biological accuracy at the expense of
increasing space and time complexities. Hardware
approaches have been proposed to accelerate the
computational bottlenecks created by the alignment
process. Although several hardware approaches have
achieved remarkable speedups, most have overlooked
important biological features, which have hampered
their widespread adoption by the genomics community. In
this paper, we provide a brief biological introduction
to genomics and NGS. We discuss the most popular next
generation read alignment tools and algorithms.
Furthermore, we provide a comprehensive survey of the
hardware implementations used to accelerate these
algorithms.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sirin:2017:BMT,
author = "Utku Sirin and Faruk Polat and Reda Alhajj",
title = "Batch Mode {TD$ \lambda $} for Controlling Partially
Observable Gene Regulatory Networks",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1214--1227",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2595577",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "External control of gene regulatory networks GRNs has
received much attention in recent years. The aim is to
find a series of actions to apply to a gene regulation
system making it avoid its diseased states. In this
work, we propose a novel method for controlling
partially observable GRNs combining batch mode
reinforcement learning Batch RL and TD$ \lambda $
algorithms. Unlike the existing studies inferring a
computational model from gene expression data, and
obtaining a control policy over the constructed model,
our idea is to interpret the time series gene
expression data as a sequence of observations that the
system produced, and obtain an approximate stochastic
policy directly from the gene expression data without
estimation of the internal states of the partially
observable environment. Thereby, we get rid of the most
time consuming phases of the existing studies,
inferring a model and running the model for the
control. Results show that our method is able to
provide control solutions for regulation systems of
several thousands of genes only in seconds, whereas
existing studies cannot solve control problems of even
a few dozens of genes. Results also show that our
approximate stochastic policies are almost as good as
the policies generated by the existing studies.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{L:2017:BDW,
author = "Biji C. L. and Achuthsankar S. Nair",
title = "Benchmark Dataset for Whole Genome Sequence
Compression",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1228--1236",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2568186",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The research in DNA data compression lacks a standard
dataset to test out compression tools specific to DNA.
This paper argues that the current state of achievement
in DNA compression is unable to be benchmarked in the
absence of such scientifically compiled whole genome
sequence dataset and proposes a benchmark dataset using
multistage sampling procedure. Considering the genome
sequence of organisms available in the National Centre
for Biotechnology and Information NCBI as the universe,
the proposed dataset selects 1,105 prokaryotes, 200
plasmids, 164 viruses, and 65 eukaryotes. This paper
reports the results of using three established tools on
the newly compiled dataset and show that their strength
and weakness are evident only with a comparison based
on the scientifically compiled benchmark dataset.
Availability: The sample dataset and the respective
links are available at
\url{https://sourceforge.net/projects/benchmarkdnacompressiondataset/}.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{doNascimento:2017:CNV,
author = "Francisco do Nascimento and Katia S. Guimaraes",
title = "Copy Number Variations Detection: Unravelling the
Problem in Tangible Aspects",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1237--1250",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2576441",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In the midst of the important genomic variants
associated to the susceptibility and resistance to
complex diseases, Copy Number Variations CNV has
emerged as a prevalent class of structural variation.
Following the flood of next-generation sequencing data,
numerous tools publicly available have been developed
to provide computational strategies to identify CNV at
improved accuracy. This review goes beyond scrutinizing
the main approaches widely used for structural variants
detection in general, including Split-Read, Paired-End
Mapping, Read-Depth, and Assembly-based. In this paper,
1 we characterize the relevant technical details around
the detection of CNV, which can affect the estimation
of breakpoints and number of copies, 2 we pinpoint the
most important insights related to GC-content and
mappability biases, and 3 we discuss the paramount
caveats in the tools evaluation process. The points
brought out in this study emphasize common assumptions,
a variety of possible limitations, valuable insights,
and directions for desirable contributions to the
state-of-the-art in CNV detection tools.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ceri:2017:DMH,
author = "Stefano Ceri and Abdulrahman Kaitoua and Marco
Masseroli and Pietro Pinoli and Francesco Venco",
title = "Data Management for Heterogeneous Genomic Datasets",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1251--1264",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2576447",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Next Generation Sequencing NGS, a family of
technologies for reading DNA and RNA, is changing
biological research, and will soon change medical
practice, by quickly providing sequencing data and
high-level features of numerous individual genomes in
different biological and clinical conditions. The
availability of millions of whole genome sequences may
soon become the biggest and most important ``big data''
problem of mankind. In this exciting framework, we
recently proposed a new paradigm to raise the level of
abstraction in NGS data management, by introducing a
GenoMetric Query Language GMQL and demonstrating its
usefulness through several biological query examples.
Leveraging on that effort, here we motivate and
formalize GMQL operations, especially focusing on the
most characteristic and domain-specific ones.
Furthermore, we address their efficient implementation
and illustrate the architecture of the new software
system that we have developed for their execution on
big genomic data in a cloud computing environment,
providing the evaluation of its performance. The new
system implementation is available for download at the
GMQL website
http://www.bioinformatics.deib.polimi.it/GMQL/; GMQL
can also be tested through a set of predefined queries
on ENCODE and Roadmap Epigenomics data at
http://www.bioinformatics.deib.polimi.it/GMQL/queries/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Adl:2017:DPI,
author = "Amin Ahmadi Adl and Hye-Seung Lee and Xiaoning Qian",
title = "Detecting Pairwise Interactive Effects of Continuous
Random Variables for Biomarker Identification with
Small Sample Size",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1265--1275",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2586042",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Aberrant changes to interactions among cellular
components have been conjectured to be potential causes
of abnormalities in cellular functions. By systematic
analysis of high-throughput-omics data, researchers
hope to detect potential associations among measured
variables for better biomarker identification and
phenotype prediction. In this paper, we focus on the
methods to measure pairwise interactive effects among
continuous random variables, representing molecular
expressions, with respect to a given categorical
outcome. Together with a comprehensive review on the
existing measures, we further propose new measures that
better estimate interactive effects, especially in
small sample size scenarios. We first evaluate the
performance of the existing and new methods for both
small and large sample sizes based on simulated
datasets that shows our proposed methods outperform
previous methods in general. The best performing method
for small sample size scenarios suggested by simulation
experiments is then implemented to estimate interactive
effects among genes with respect to the metastasis
outcome in two breast cancer studies based on
micro-array gene expression datasets. Our results
further demonstrate that integrating detected
interactive effects together with individual effects
can help in finding more accurate biomarkers for breast
cancer metastasis, which are indeed involved in
important pathways related to cancer metastasis based
on gene set enrichment analysis.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Grewal:2017:EAO,
author = "Nivit Grewal and Shailendra Singh and Trilok Chand",
title = "Effect of Aggregation Operators on Network-Based
Disease Gene Prioritization: a Case Study on Blood
Disorders",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1276--1287",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2599155",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Owing to the innate noise in the biological data
sources, a single source or a single measure do not
suffice for an effective disease gene prioritization.
So, the integration of multiple data sources or
aggregation of multiple measures is the need of the
hour. The aggregation operators combine multiple
related data values to a single value such that the
combined value has the effect of all the individual
values. In this paper, an attempt has been made for
applying the fuzzy aggregation on the network-based
disease gene prioritization and investigate its effect
under noise conditions. This study has been conducted
for a set of 15 blood disorders by fusing four
different network measures, computed from the protein
interaction network, using a selected set of
aggregation operators and ranking the genes on the
basis of the aggregated value. The aggregation
operator-based rankings have been compared with the
``Random walk with restart'' gene prioritization
method. The impact of noise has also been investigated
by adding varying proportions of noise to the seed set.
The results reveal that for all the selected blood
disorders, the Mean of Maximal operator has relatively
outperformed the other aggregation operators for noisy
as well as non-noisy data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2017:EPC,
author = "Gui-Jun Zhang and Xiao-Gen Zhou and Xu-Feng Yu and
Xiao-Hu Hao and Li Yu",
title = "Enhancing Protein Conformational Space Sampling Using
Distance Profile-Guided Differential Evolution",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1288--1301",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2566617",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "De novo protein structure prediction aims to search
for low-energy conformations as it follows the
thermodynamics hypothesis that places native
conformations at the global minimum of the protein
energy surface. However, the native conformation is not
necessarily located in the lowest-energy regions owing
to the inaccuracies of the energy model. This study
presents a differential evolution algorithm using
distance profile-based selection strategy to sample
conformations with reasonable structure effectively. In
the proposed algorithm, besides energy, the
residue-residue distance is considered another measure
of the conformation. The average distance errors of
decoys between the distance of each residue pair and
the corresponding distance in the distance profiles are
first calculated when the trial conformation yields a
larger energy value than that of the target. Then, the
distance acceptance probability of the trial
conformation is designed based on distance profiles if
the trial conformation obtains a lower average distance
error compared with that of the target conformation.
The trial conformation is accepted to the next
generation in accordance with its distance acceptance
probability. By using the dual constraints of energy
and distance in guiding sampling, the algorithm can
sample conformations with lower energies and more
reasonable structures. Experimental results of 28
benchmark proteins show that the proposed algorithm can
effectively predict near-native protein structures.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Aparicio:2017:EAG,
author = "David Aparicio and Pedro Ribeiro and Fernando Silva",
title = "Extending the Applicability of Graphlets to Directed
Networks",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1302--1315",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2586046",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "With recent advances in high-throughput cell biology,
the amount of cellular biological data has grown
drastically. Such data is often modeled as graphs also
called networks and studying them can lead to new
insights into molecule-level organization. A possible
way to understand their structure is by analyzing the
smaller components that constitute them, namely network
motifs and graphlets. Graphlets are particularly well
suited to compare networks and to assess their level of
similarity due to the rich topological information that
they offer but are almost always used as small
undirected graphs of up to five nodes, thus limiting
their applicability in directed networks. However, a
large set of interesting biological networks such as
metabolic, cell signaling, or transcriptional
regulatory networks are intrinsically directional, and
using metrics that ignore edge direction may gravely
hinder information extraction. Our main purpose in this
work is to extend the applicability of graphlets to
directed networks by considering their edge direction,
thus providing a powerful basis for the analysis of
directed biological networks. We tested our approach on
two network sets, one composed of synthetic graphs and
another of real directed biological networks, and
verified that they were more accurately grouped using
directed graphlets than undirected graphlets. It is
also evident that directed graphlets offer
substantially more topological information than simple
graph metrics such as degree distribution or
reciprocity. However, enumerating graphlets in large
networks is a computationally demanding task. Our
implementation addresses this concern by using a
state-of-the-art data structure, the g-trie, which is
able to greatly reduce the necessary computation. We
compared our tool to other state-of-the art methods and
verified that it is the fastest general tool for
graphlet counting.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Stegmayer:2017:HCI,
author = "Georgina Stegmayer and Cristian Yones and Laura
Kamenetzky and Diego H. Milone",
title = "High Class-Imbalance in pre-{miRNA} Prediction: a
Novel Approach Based on {deepSOM}",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1316--1326",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2576459",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The computational prediction of novel microRNA within
a full genome involves identifying sequences having the
highest chance of being a miRNA precursor pre-miRNA.
These sequences are usually named candidates to miRNA.
The well-known pre-miRNAs are usually only a few in
comparison to the hundreds of thousands of potential
candidates to miRNA that have to be analyzed, which
makes this task a high class-imbalance classification
problem. The classical way of approaching it has been
training a binary classifier in a supervised manner,
using well-known pre-miRNAs as positive class and
artificially defining the negative class. However,
although the selection of positive labeled examples is
straightforward, it is very difficult to build a set of
negative examples in order to obtain a good set of
training samples for a supervised method. In this work,
we propose a novel and effective way of approaching
this problem using machine learning, without the
definition of negative examples. The proposal is based
on clustering unlabeled sequences of a genome together
with well-known miRNA precursors for the organism under
study, which allows for the quick identification of the
best candidates to miRNA as those sequences clustered
with known precursors. Furthermore, we propose a deep
model to overcome the problem of having very few
positive class labels. They are always maintained in
the deep levels as positive class while less likely
pre-miRNA sequences are filtered level after level. Our
approach has been compared with other methods for
pre-miRNAs prediction in several species, showing
effective predictivity of novel miRNAs. Additionally,
we will show that our approach has a lower training
time and allows for a better graphical navegability and
interpretation of the results. A web-demo interface to
try deepSOM is available at
http://fich.unl.edu.ar/sinc/web-demo/deepsom/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Akkasi:2017:IBN,
author = "Abbas Akkasi and Ekrem Varoglu",
title = "Improving Biochemical Named Entity Recognition Using
{PSO} Classifier Selection and {Bayesian} Combination
Methods",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1327--1338",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2570216",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Named Entity Recognition NER is a basic step for large
number of consequent text mining tasks in the
biochemical domain. Increasing the performance of such
recognition systems is of high importance and always
poses a challenge. In this study, a new community based
decision making system is proposed which aims at
increasing the efficiency of NER systems in the
chemical/drug name context. Particle Swarm Optimization
PSO algorithm is chosen as the expert selection
strategy along with the Bayesian combination method to
merge the outputs of the selected classifiers as well
as evaluate the fitness of the selected candidates. The
proposed system performs in two steps. The first step
focuses on creating various numbers of baseline
classifiers for NER with different features sets using
the Conditional Random Fields CRFs. The second step
involves the selection and efficient combination of the
classifiers using PSO and Bayesisan combination. Two
comprehensive corpora from BioCreative events, namely
ChemDNER and CEMP, are used for the experiments
conducted. Results show that the ensemble of
classifiers selected by means of the proposed approach
perform better than the single best classifier as well
as ensembles formed using other popular
selection/combination strategies for both corpora.
Furthermore, the proposed method outperforms the best
performing system at the Biocreative IV ChemDNER track
by achieving an F-score of 87.95 percent.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bittig:2017:MSH,
author = "Arne T. Bittig and Adelinde M. Uhrmacher",
title = "{ML-Space}: Hybrid Spatial {Gillespie} and Particle
Simulation of Multi-Level Rule-Based Models in Cell
Biology",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1339--1349",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2598162",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Spatio-temporal dynamics of cellular processes can be
simulated at different levels of detail, from
deterministic partial differential equations via the
spatial Stochastic Simulation algorithm to tracking
Brownian trajectories of individual particles. We
present a spatial simulation approach for multi-level
rule-based models, which includes dynamically
hierarchically nested cellular compartments and
entities. Our approach ML-Space combines discrete
compartmental dynamics, stochastic spatial approaches
in discrete space, and particles moving in continuous
space. The rule-based specification language of
ML-Space supports concise and compact descriptions of
models and to adapt the spatial resolution of models
easily.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kang:2017:MBB,
author = "Mingon Kang and Juyoung Park and Dong-Chul Kim and
Ashis K. Biswas and Chunyu Liu and Jean Gao",
title = "Multi-Block Bipartite Graph for Integrative Genomic
Analysis",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1350--1358",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2591521",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Human diseases involve a sequence of complex
interactions between multiple biological processes. In
particular, multiple genomic data such as Single
Nucleotide Polymorphism SNP, Copy Number Variation CNV,
DNA Methylation DM, and their interactions
simultaneously play an important role in human
diseases. However, despite the widely known complex
multi-layer biological processes and increased
availability of the heterogeneous genomic data, most
research has considered only a single type of genomic
data. Furthermore, recent integrative genomic studies
for the multiple genomic data have also been facing
difficulties due to the high-dimensionality and
complexity, especially when considering their intra-
and inter-block interactions. In this paper, we
introduce a novel multi-block bipartite graph and its
inference methods, MB2I and sMB2I, for the integrative
genomic study. The proposed methods not only integrate
multiple genomic data but also incorporate
intra/inter-block interactions by using a multi-block
bipartite graph. In addition, the methods can be used
to predict quantitative traits e.g., gene expression,
survival time from the multi-block genomic data. The
performance was assessed by simulation experiments that
implement practical situations. We also applied the
method to the human brain data of psychiatric
disorders. The experimental results were analyzed by
maximum edge biclique and biclustering, and biological
findings were discussed.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Weyenberg:2017:NKB,
author = "Grady Weyenberg and Ruriko Yoshida and Daniel Howe",
title = "Normalizing Kernels in the {Billera--Holmes--Vogtmann}
Treespace",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1359--1365",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2565475",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "As costs of genome sequencing have dropped
precipitously, development of efficient bioinformatic
methods to analyze genome structure and evolution have
become ever more urgent. For example, most published
phylogenomic studies involve either massive
concatenation of sequences, or informal comparisons of
phylogenies inferred on a small subset of orthologous
genes, neither of which provides a comprehensive
overview of evolution or systematic identification of
genes with unusual and interesting evolution e.g.,
horizontal gene transfers, gene duplication, and
subsequent neofunctionalization. We are interested in
identifying such ``outlying'' gene trees from the set
of gene trees and estimating the distribution of trees
over the ``tree space''. This paper describes an
improvement to the kdetrees algorithm, an adaptation of
classical kernel density estimation to the metric space
of phylogenetic trees Billera-Holmes-Vogtman treespace,
whereby the kernel normalizing constants, are estimated
through the use of the novel holonomic gradient
methods. As in the original kdetrees paper, we have
applied kdetrees to a set of Apicomplexa genes. The
analysis identified several unreliable sequence
alignments that had escaped previous detection, as well
as a gene independently reported as a possible case of
horizontal gene transfer. The updated version of the
kdetrees software package is available both from CRAN
the official R package system, as well as from the
official development repository on Github.
github.com/grady/kdetrees.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ding:2017:NMM,
author = "Yuchun Ding and Marie Christine Pardon and Alessandra
Agostini and Henryk Faas and Jinming Duan and Wil O. C.
Ward and Felicity Easton and Dorothee Auer and Li Bai",
title = "Novel Methods for Microglia Segmentation, Feature
Extraction, and Classification",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1366--1377",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2591520",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Segmentation and analysis of histological images
provides a valuable tool to gain insight into the
biology and function of microglial cells in health and
disease. Common image segmentation methods are not
suitable for inhomogeneous histology image analysis and
accurate classification of microglial activation states
has remained a challenge. In this paper, we introduce
an automated image analysis framework capable of
efficiently segmenting microglial cells from histology
images and analyzing their morphology. The framework
makes use of variational methods and the fast-split
Bregman algorithm for image denoising and segmentation,
and of multifractal analysis for feature extraction to
classify microglia by their activation states.
Experiments show that the proposed framework is
accurate and scalable to large datasets and provides a
useful tool for the study of microglial biology.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Savel:2017:PEL,
author = "Daniel Savel and Thomas LaFramboise and Ananth Grama
and Mehmet Koyuturk",
title = "{Pluribus} --- Exploring the Limits of Error
Correction Using a Suffix Tree",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1378--1388",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2586060",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Next generation sequencing technologies enable
efficient and cost-effective genome sequencing.
However, sequencing errors increase the complexity of
the de novo assembly process, and reduce the quality of
the assembled sequences. Many error correction
techniques utilizing substring frequencies have been
developed to mitigate this effect. In this paper, we
present a novel and effective method called Pluribus,
for correcting sequencing errors using a generalized
suffix trie. Pluribus utilizes multiple manifestations
of an error in the trie to accurately identify errors
and suggest corrections. We show that Pluribus produces
the least number of false positives across a diverse
set of real sequencing datasets when compared to other
methods. Furthermore, Pluribus can be used in
conjunction with other contemporary error correction
methods to achieve higher levels of accuracy than
either tool alone. These increases in error correction
accuracy are also realized in the quality of the
contigs that are generated during assembly. We explore,
in-depth, the behavior of Pluribus , to explain the
observed improvement in accuracy and assembly
performance. Pluribus is freely available at
http://compbio.case.edu/pluribus/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hu:2017:PPD,
author = "Jun Hu and Yang Li and Ming Zhang and Xibei Yang and
Hong-Bin Shen and Dong-Jun Yu",
title = "Predicting Protein-{DNA} Binding Residues by
Weightedly Combining Sequence-Based Features and
Boosting Multiple {SVMs}",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1389--1398",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2616469",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein-DNA interactions are ubiquitous in a wide
variety of biological processes. Correctly locating
DNA-binding residues solely from protein sequences is
an important but challenging task for protein function
annotations and drug discovery, especially in the
post-genomic era where large volumes of protein
sequences have quickly accumulated. In this study, we
report a new predictor, named TargetDNA, for targeting
protein-DNA binding residues from primary sequences.
TargetDNA uses a protein's evolutionary information and
its predicted solvent accessibility as two base
features and employs a centered linear kernel alignment
algorithm to learn the weights for weightedly combining
the two features. Based on the weightedly combined
feature, multiple initial predictors with SVM as
classifiers are trained by applying a random
under-sampling technique to the original dataset, the
purpose of which is to cope with the severe imbalance
phenomenon that exists between the number of
DNA-binding and non-binding residues. The final
ensembled predictor is obtained by boosting the
multiple initially trained predictors. Experimental
simulation results demonstrate that the proposed
TargetDNA achieves a high prediction performance and
outperforms many existing sequence-based protein-DNA
binding residue predictors. The TargetDNA web server
and datasets are freely available at
http://csbio.njust.edu.cn/bioinf/TargetDNA/ for
academic use.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhong:2017:PII,
author = "Jiancheng Zhong and Jianxing Wang and Xiaojun Ding and
Zhen Zhang and Min Li and Fang-Xiang Wu and Yi Pan",
title = "Protein Inference from the Integration of Tandem {MS}
Data and Interactome Networks",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1399--1409",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2601618",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Since proteins are digested into a mixture of peptides
in the preprocessing step of tandem mass spectrometry
MS, it is difficult to determine which specific protein
a shared peptide belongs to. In recent studies, besides
tandem MS data and peptide identification information,
some other information is exploited to infer proteins.
Different from the methods which first use only tandem
MS data to infer proteins and then use network
information to refine them, this study proposes a
protein inference method named TMSIN, which uses
interactome networks directly. As two interacting
proteins should co-exist, it is reasonable to assume
that if one of the interacting proteins is confidently
inferred in a sample, its interacting partners should
have a high probability in the same sample, too.
Therefore, we can use the neighborhood information of a
protein in an interactome network to adjust the
probability that the shared peptide belongs to the
protein. In TMSIN, a multi-weighted graph is
constructed by incorporating the bipartite graph with
interactome network information, where the bipartite
graph is built with the peptide identification
information. Based on multi-weighted graphs, TMSIN
adopts an iterative workflow to infer proteins. At each
iterative step, the probability that a shared peptide
belongs to a specific protein is calculated by using
the Bayes' law based on the neighbor protein support
scores of each protein which are mapped by the shared
peptides. We carried out experiments on yeast data and
human data to evaluate the performance of TMSIN in
terms of ROC, q-value, and accuracy. The experimental
results show that AUC scores yielded by TMSIN are 0.742
and 0.874 in yeast dataset and human dataset,
respectively, and TMSIN yields the maximum number of
true positives when q-value less than or equal to 0.05.
The overlap analysis shows that TMSIN is an effective
complementary approach for protein inference.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gu:2017:RGS,
author = "Deqing Gu and Xingxing Jian and Cheng Zhang and Qiang
Hua",
title = "Reframed Genome-Scale Metabolic Model to Facilitate
Genetic Design and Integration with Expression Data",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1410--1418",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2576456",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genome-scale metabolic network models GEMs have played
important roles in the design of genetically engineered
strains and helped biologists to decipher metabolism.
However, due to the complex gene-reaction relationships
that exist in model systems, most algorithms have
limited capabilities with respect to directly
predicting accurate genetic design for metabolic
engineering. In particular, methods that predict
reaction knockout strategies leading to overproduction
are often impractical in terms of gene manipulations.
Recently, we proposed a method named logical
transformation of model LTM to simplify the
gene-reaction associations by introducing intermediate
pseudo reactions, which makes it possible to generate
genetic design. Here, we propose an alternative method
to relieve researchers from deciphering complex
gene-reactions by adding pseudo gene controlling
reactions. In comparison to LTM, this new method
introduces fewer pseudo reactions and generates a much
smaller model system named as gModel. We showed that
gModel allows two seldom reported applications:
identification of minimal genomes and design of minimal
cell factories within a modified OptKnock framework. In
addition, gModel could be used to integrate expression
data directly and improve the performance of the E-Fmin
method for predicting fluxes. In conclusion, the model
transformation procedure will facilitate genetic
research based on GEMs, extending their applications.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Maji:2017:SFS,
author = "Pradipta Maji and Ekta Shah",
title = "Significance and Functional Similarity for
Identification of Disease Genes",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1419--1433",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2598163",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "One of the most significant research issues in
functional genomics is insilico identification of
disease related genes. In this regard, the paper
presents a new gene selection algorithm, termed as
SiFS, for identification of disease genes. It
integrates the information obtained from interaction
network of proteins and gene expression profiles. The
proposed SiFS algorithm culls out a subset of genes
from microarray data as disease genes by maximizing
both significance and functional similarity of the
selected gene subset. Based on the gene expression
profiles, the significance of a gene with respect to
another gene is computed using mutual information. On
the other hand, a new measure of similarity is
introduced to compute the functional similarity between
two genes. Information derived from the protein-protein
interaction network forms the basis of the proposed
SiFS algorithm. The performance of the proposed gene
selection algorithm and new similarity measure, is
compared with that of other related methods and
similarity measures, using several cancer microarray
data sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Qi:2017:SCM,
author = "Zhen Qi and Eberhard O. Voit",
title = "Strategies for Comparing Metabolic Profiles:
Implications for the Inference of Biochemical
Mechanisms from Metabolomics Data",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1434--1445",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2586065",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Background: Large amounts of metabolomics data have
been accumulated in recent years and await analysis.
Previously, we had developed a systems biology approach
to infer biochemical mechanisms underlying metabolic
alterations observed in cancers and other diseases. The
method utilized the typical Euclidean distance for
comparing metabolic profiles. Here, we ask whether any
of the numerous alternative metrics might serve this
purpose better. Methods and Findings: We used enzymatic
alterations in purine metabolism that were measured in
human renal cell carcinoma to test various metrics with
the goal of identifying the best metrics for discerning
metabolic profiles of healthy and diseased individuals.
The results showed that several metrics have similarly
good performance, but that some are unsuited for
comparisons of metabolic profiles. Furthermore, the
results suggest that relative changes in metabolite
levels, which reduce bias toward large metabolite
concentrations, are better suited for comparisons of
metabolic profiles than absolute changes. Finally, we
demonstrate that a sequential search for enzymatic
alterations, ranked by importance, is not always valid.
Conclusions: We identified metrics that are appropriate
for comparisons of metabolic profiles. In addition, we
constructed strategic guidelines for the algorithmic
identification of biochemical mechanisms from
metabolomics data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mohammadi:2017:TAT,
author = "Shahin Mohammadi and David F. Gleich and Tamara G.
Kolda and Ananth Grama",
title = "Triangular Alignment {TAME}: a Tensor-Based Approach
for Higher-Order Network Alignment",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1446--1458",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2595583",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Network alignment has extensive applications in
comparative interactomics. Traditional approaches aim
to simultaneously maximize the number of conserved
edges and the underlying similarity of aligned
entities. We propose a novel formulation of the network
alignment problem that extends topological similarity
to higher-order structures and provides a new objective
function that maximizes the number of aligned
substructures. This objective function corresponds to
an integer programming problem, which is NP-hard.
Consequently, we identify a closely related surrogate
function whose maximization results in a tensor
eigenvector problem. Based on this formulation, we
present an algorithm called Triangular AlignMEnt TAME,
which attempts to maximize the number of aligned
triangles across networks. Using a case study on the
NAPAbench dataset, we show that triangular alignment is
capable of producing mappings with high node
correctness. We further evaluate our method by aligning
yeast and human interactomes. Our results indicate that
TAME outperforms the state-of-art alignment methods in
terms of conserved triangles. In addition, we show that
the number of conserved triangles is more significantly
correlated, compared to the conserved edge, with node
correctness and co-expression of edges. Our formulation
and resulting algorithms can be easily extended to
arbitrary motifs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2017:UBM,
author = "Yun Liu and Tao Hou and Bing Kang and Fu Liu",
title = "Unsupervised Binning of Metagenomic Assembled Contigs
Using Improved Fuzzy {C}-Means Method",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1459--1467",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2576452",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Metagenomic contigs binning is a necessary step of
metagenome analysis. After assembly, the number of
contigs belonging to different genomes is usually
unequal. So a metagenomic contigs dataset is a kind of
imbalanced dataset and traditional fuzzy c-means method
FCM fails to handle it very well. In this paper, we
will introduce an improved version of fuzzy c-means
method IFCM into metagenomic contigs binning. First,
tetranucleotide frequencies are calculated for every
contig. Second, the number of bins is roughly estimated
by the distribution of genome lengths of a complete set
of non-draft sequenced microbial genomes from NCBI.
Then, IFCM is used to cluster DNA contigs with the
estimated result. Finally, a clustering validity
function is utilized to determine the binning result.
We tested this method on a synthetic and two real
datasets and experimental results have showed the
effectiveness of this method compared with other
tools.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Luo:2017:CPD,
author = "Jiawei Luo and Pingjian Ding and Cheng Liang and Buwen
Cao and Xiangtao Chen",
title = "Collective Prediction of Disease-Associated {miRNAs}
Based on Transduction Learning",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1468--1475",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2599866",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The discovery of human disease-related miRNA is a
challenging problem for complex disease biology
research. For existing computational methods, it is
difficult to achieve excellent performance with sparse
known miRNA-disease association verified by biological
experiment. Here, we develop CPTL, a Collective
Prediction based on Transduction Learning, to
systematically prioritize miRNAs related to disease. By
combining disease similarity, miRNA similarity with
known miRNA-disease association, we construct a
miRNA-disease network for predicting miRNA-disease
association. Then, CPTL calculates relevance score and
updates the network structure iteratively, until a
convergence criterion is reached. The relevance score
of node including miRNA and disease is calculated by
the use of transduction learning based on its
neighbors. The network structure is updated using
relevance score, which increases the weight of
important links. To show the effectiveness of our
method, we compared CPTL with existing methods based on
HMDD datasets. Experimental results indicate that CPTL
outperforms existing approaches in terms of AUC,
precision, recall, and F1-score. Moreover, experiments
performed with different number of iterations verify
that CPTL has good convergence. Besides, it is analyzed
that the varying of weighted parameters affect
predicted results. Case study on breast cancer has
further confirmed the identification ability of CPTL.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dureau:2017:MIA,
author = "Maxime Dureau and Angelo Alessandri and Patrizia
Bagnerini and Stephane Vincent",
title = "Modeling and Identification of Amnioserosa Cell
Mechanical Behavior by Using Mass-Spring Lattices",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1476--1481",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2586063",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Various mechanical models of live amnioserosa cells
during Drosophila melanogaster's dorsal closure are
proposed. Such models account for specific
biomechanical oscillating behaviors and depend on a
different set of parameters. The identification of the
parameters for each of the proposed models is
accomplished according to a least-squares approach in
such a way to best fit the cellular dynamics extracted
from live images. For the purpose of comparison, the
resulting models after identification are validated to
allow for the selection of the most appropriate
description of such a cell dynamics. The proposed
methodology is general and it may be applied to other
planar biological processes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lovato:2017:SNR,
author = "Pietro Lovato and Marco Cristani and Manuele Bicego",
title = "Soft {Ngram} Representation and Modeling for Protein
Remote Homology Detection",
journal = j-TCBB,
volume = "14",
number = "6",
pages = "1482--1488",
month = nov,
year = "2017",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2595575",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Jan 12 18:05:03 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Remote homology detection represents a central problem
in bioinformatics, where the challenge is to detect
functionally related proteins when their sequence
similarity is low. Recent solutions employ
representations derived from the sequence profile,
obtained by replacing each amino acid of the sequence
by the corresponding most probable amino acid in the
profile. However, the information contained in the
profile could be exploited more deeply, provided that
there is a representation able to capture and properly
model such crucial evolutionary information. In this
paper, we propose a novel profile-based representation
for sequences, called soft Ngram. This representation,
which extends the traditional Ngram scheme obtained by
grouping N consecutive amino acids, permits considering
all of the evolutionary information in the profile:
this is achieved by extracting Ngrams from the whole
profile, equipping them with a weight directly computed
from the corresponding evolutionary frequencies. We
illustrate two different approaches to model the
proposed representation and to derive a feature vector,
which can be effectively used for classification using
a support vector machine SVM. A thorough evaluation on
three benchmarks demonstrates that the new approach
outperforms other Ngram-based methods, and shows very
promising results also in comparison with a broader
spectrum of techniques.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2018:GMS,
author = "Yanbo Wang and Weikang Qian and Bo Yuan",
title = "A Graphical Model of Smoking-Induced Global
Instability in Lung Cancer",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "1--14",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2599867",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Smoking is the major cause of lung cancer and the
leading cause of cancer-related death in the world. The
most current view about lung cancer is no longer
limited to individual genes being mutated by any
carcinogenic insults from smoking. Instead,
tumorigenesis is a phenotype conferred by many
systematic and global alterations, leading to extensive
heterogeneity and variation for both the genotypes and
phenotypes of individual cancer cells. Thus,
strategically it is foremost important to develop a
methodology to capture any consistent and global
alterations presumably shared by most of the cancerous
cells for a given population. This is particularly true
that almost all of the data collected from solid
cancers including lung cancers are usually distant
apart over a large span of temporal or even spatial
contexts. Here, we report a multiple non-Gaussian
graphical model to reconstruct the gene interaction
network using two previously published gene expression
datasets. Our graphical model aims to selectively
detect gross structural changes at the level of gene
interaction networks. Our methodology is extensively
validated, demonstrating good robustness, as well as
the selectivity and specificity expected based on our
biological insights. In summary, gene regulatory
networks are still relatively stable during presumably
the early stage of neoplastic transformation. But
drastic structural differences can be found between
lung cancer and its normal control, including the gain
of functional modules for cellular proliferations such
as EGFR and PDGFRA, as well as the lost of the
important IL6 module, supporting their roles as
potential drug targets. Interestingly, our method can
also detect early modular changes, with the ALDH3A1 and
its associated interactions being strongly implicated
as a potential early marker, whose activations appear
to alter LCN2 module as well as its interactions with
the important TP53-MDM2 circuitry. Our strategy using
the graphical model to reconstruct gene interaction
work with biologically-inspired constraints exemplifies
the importance and beauty of biology in developing any
bio-computational approach.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jansson:2018:AMR,
author = "Jesper Jansson and Ramesh Rajaby and Chuanqi Shen and
Wing-Kin Sung",
title = "Algorithms for the Majority Rule + Consensus Tree and
the Frequency Difference Consensus Tree",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "15--26",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2609923",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This article presents two new deterministic algorithms
for constructing consensus trees. Given an input of $k$
phylogenetic trees with identical leaf label sets and
$n$ leaves each, the first algorithm constructs the
majority rule + consensus tree in $ O k n$ time, which
is optimal since the input size is $ \Omega k n$, and
the second one constructs the frequency difference
consensus tree in $ \min \lbrace O k n^2, O k n k +
\log^2 n \rbrace $ time.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Vyas:2018:AGP,
author = "Renu Vyas and Sanket Bapat and Purva Goel and
Muthukumarasamy Karthikeyan and Sanjeev S. Tambe and
Bhaskar D. Kulkarni",
title = "Application of Genetic Programming {GP} Formalism for
Building Disease Predictive Models from
Protein--Protein Interactions {PPI} Data",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "27--37",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2621042",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein-protein interactions PPIs play a vital role in
the biological processes involved in the cell functions
and disease pathways. The experimental methods known to
predict PPIs require tremendous efforts and the results
are often hindered by the presence of a large number of
false positives. Herein, we demonstrate the use of a
new Genetic Programming GP based Symbolic Regression SR
approach for predicting PPIs related to a disease. In
this case study, a dataset consisting of 135 PPI
complexes related to cancer was used to construct a
generic PPI predicting model with good PPI prediction
accuracy and generalization ability. A high correlation
coefficient CC magnitude of 0.893, and low root mean
square error RMSE, and mean absolute percentage error
MAPE values of 478.221 and 0.239, respectively, were
achieved for both the training and test set outputs. To
validate the discriminatory nature of the model, it was
applied on a dataset of diabetes complexes where it
yielded significantly low CC values. Thus, the GP model
developed here serves a dual purpose: a a predictor of
the binding energy of cancer related PPI complexes, and
b a classifier for discriminating PPI complexes related
to cancer from those of other diseases.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hu:2018:ARE,
author = "Bin Hu and Xiaowei Li and Shuting Sun and Martyn
Ratcliffe",
title = "Attention Recognition in {EEG}-Based Affective
Learning Research Using {CFS + KNN} Algorithm",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "38--45",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2616395",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The research detailed in this paper focuses on the
processing of Electroencephalography EEG data to
identify attention during the learning process. The
identification of affect using our procedures is
integrated into a simulated distance learning system
that provides feedback to the user with respect to
attention and concentration. The authors propose a
classification procedure that combines
correlation-based feature selection CFS and a
k-nearest-neighbor KNN data mining algorithm. To
evaluate the CFS+KNN algorithm, it was test against
CFS+C4.5 algorithm and other classification algorithms.
The classification performance was measured 10 times
with different 3-fold cross validation data. The data
was derived from 10 subjects while they were attempting
to learn material in a simulated distance learning
environment. A self-assessment model of self-report was
used with a single valence to evaluate attention on 3
levels high, neutral, low. It was found that CFS+KNN
had a much better performance, giving the highest
correct classification rate CCR of $ 80.84 \pm 3.0 $ \%
for the valence dimension divided into three classes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ushakov:2018:BLB,
author = "Anton V. Ushakov and Xenia Klimentova and Igor
Vasilyev",
title = "Bi-level and Bi-objective $p$-Median Type Problems for
Integrative Clustering: Application to Analysis of
Cancer Gene-Expression and Drug-Response Data",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "46--59",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2622692",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recent advances in high-throughput technologies have
given rise to collecting large amounts of
multidimensional heterogeneous data that provide
diverse information on the same biological samples.
Integrative analysis of such multisource datasets may
reveal new biological insights into complex biological
mechanisms and therefore remains an important research
field in systems biology. Most of the modern
integrative clustering approaches rely on independent
analysis of each dataset and consensus clustering,
probabilistic or statistical modeling, while flexible
distance-based integrative clustering techniques are
sparsely covered. We propose two distance-based
integrative clustering frameworks based on bi-level and
bi-objective extensions of the p-median problem. A
hybrid branch-and-cut method is developed to find
global optimal solutions to the bi-level p-median
model. As to the bi-objective problem, an $ \varepsilon
$-constraint algorithm is proposed to generate an
approximation to the Pareto optimal set. Every solution
found by any of the frameworks corresponds to an
integrative clustering. We present an application of
our approaches to integrative analysis of NCI-60 human
tumor cell lines characterized by gene expression and
drug activity profiles. We demonstrate that the
proposed mathematical optimization-based approaches
outperform some state-of-the-art and traditional
distance-based integrative and non-integrative
clustering techniques.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Viswanath:2018:CET,
author = "Narayanan C. Viswanath",
title = "Calculating the Expected Time to Eradicate {HIV-1}
Using a {Markov} Chain",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "60--67",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2619342",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this study, the expected time required to eradicate
HIV-1 completely was found as the conditional absorbing
time in a finite state space continuous-time Markov
chain model. The Markov chain has two absorbing states:
one corresponds to HIV eradication and another
representing the possible disaster. This method allowed
us to calculate the expected eradication time by
solving systems of linear equations. To overcome the
challenge of huge dimension of the problem, we applied
a novel stop and resume technique. This technique also
helped to stop the numerical computation whenever we
wanted and continue later from that point until the
final result was obtained. Our numerical study showed
the dependence of the expected eradication time of HIV
on the half-life of the latently infected cells and
there agreed with the previous studies. The study
predicted that when the half-life of the latent cells
varied from 4.6 to 60 months, it took a mean 4.97 to
31.04 years with a corresponding standard deviation of
0.64 to 3.99 years to eradicate the latent cell
reservoir. It also revealed the crucial dependence of
eradication time on the initial number of latently
infected cells.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Karbalayghareh:2018:CST,
author = "Alireza Karbalayghareh and Ulisses Braga-Neto and
Jianping Hua and Edward Russell Dougherty",
title = "Classification of State Trajectories in Gene
Regulatory Networks",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "68--82",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2616470",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gene-expression-based phenotype classification is used
for disease diagnosis and prognosis relating to
treatment strategies. The present paper considers
classification based on sequential measurements of
multiple genes using gene regulatory network GRN
modeling. There are two networks, original and mutated,
and observations consist of trajectories of network
states. The problem is to classify an observation
trajectory as coming from either the original or
mutated network. GRNs are modeled via probabilistic
Boolean networks, which incorporate stochasticity at
both the gene and network levels. Mutation affects the
regulatory logic. Classification is based upon
observing a trajectory of states of some given length.
We characterize the Bayes classifier and find the Bayes
error for a general PBN and the special case of a
single Boolean network affected by random perturbations
BNp. The Bayes error is related to network sensitivity,
meaning the extent of alteration in the steady-state
distribution of the original network owing to mutation.
Using standard methods to calculate steady-state
distributions is cumbersome and sometimes impossible,
so we provide an efficient algorithm and
approximations. Extensive simulations are performed to
study the effects of various factors, including
approximation accuracy. We apply the classification
procedure to a p53 BNp and a mammalian cell cycle
PBN.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hartmann:2018:CTD,
author = "Tom Hartmann and An-Chiang Chu and Martin Middendorf
and Matthias Bernt",
title = "Combinatorics of Tandem Duplication Random Loss
Mutations on Circular Genomes",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "83--95",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2613522",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The tandem duplication random loss operation TDRL is
an important genome rearrangement operation in metazoan
mitochondrial genomes. A TDRL consists of a duplication
of a contiguous set of genes in tandem followed by a
random loss of one copy of each duplicated gene. This
paper presents an analysis of the combinatorics of
TDRLs on circular genomes, e.g., the mitochondrial
genome. In particular, results on TDRLs for circular
genomes and their linear representatives are
established. Moreover, the distance between gene orders
with respect to linear TDRLs and circular TDRLs is
studied. An analysis of the available animal
mitochondrial gene orders shows the practical relevance
of the theoretical results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hujdurovic:2018:CAF,
author = "Ademir Hujdurovic and Ursa Kacar and Martin Milanic
and Bernard Ries and Alexandru I. Tomescu",
title = "Complexity and Algorithms for Finding a Perfect
Phylogeny from Mixed Tumor Samples",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "96--108",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2606620",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Hajirasouliha and Raphael WABI 2014 proposed a model
for deconvoluting mixed tumor samples measured from a
collection of high-throughput sequencing reads. This is
related to understanding tumor evolution and critical
cancer mutations. In short, their formulation asks to
split each row of a binary matrix so that the resulting
matrix corresponds to a perfect phylogeny and has the
minimum number of rows among all matrices with this
property. In this paper, we disprove several claims
about this problem, including an NP-hardness proof of
it. However, we show that the problem is indeed
NP-hard, by providing a different proof. We also prove
NP-completeness of a variant of this problem proposed
in the same paper. On the positive side, we propose an
efficient though not necessarily optimal heuristic
algorithm based on coloring co-comparability graphs,
and a polynomial time algorithm for solving the problem
optimally on matrix instances in which no column is
contained in both columns of a pair of conflicting
columns. Implementations of these algorithms are freely
available at
https://github.com/alexandrutomescu/MixedPerfectPhylogeny.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2018:DEP,
author = "Wei Zhang and Jia Xu and Yuanyuan Li and Xiufen Zou",
title = "Detecting Essential Proteins Based on Network
Topology, Gene Expression Data, and Gene Ontology
Information",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "109--116",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2615931",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
note = "See correction \cite{Zhang:2018:CDE}.",
abstract = "The identification of essential proteins in
protein-protein interaction PPI networks is of great
significance for understanding cellular processes. With
the increasing availability of large-scale PPI data,
numerous centrality measures based on network topology
have been proposed to detect essential proteins from
PPI networks. However, most of the current approaches
focus mainly on the topological structure of PPI
networks, and largely ignore the gene ontology
annotation information. In this paper, we propose a
novel centrality measure, called TEO, for identifying
essential proteins by combining network topology, gene
expression profiles, and GO information. To evaluate
the performance of the TEO method, we compare it with
five other methods degree, betweenness, NC, Pec, and
CowEWC in detecting essential proteins from two
different yeast PPI datasets. The simulation results
show that adding GO information can effectively improve
the predicted precision and that our method outperforms
the others in predicting essential proteins.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pizzi:2018:EAS,
author = "Cinzia Pizzi and Mattia Ornamenti and Simone Spangaro
and Simona E. Rombo and Laxmi Parida",
title = "Efficient Algorithms for Sequence Analysis with
Entropic Profiles",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "117--128",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2620143",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Entropy, being closely related to repetitiveness and
compressibility, is a widely used information-related
measure to assess the degree of predictability of a
sequence. Entropic profiles are based on information
theory principles, and can be used to study the
under-/over-representation of subwords, by also
providing information about the scale of conserved DNA
regions. Here, we focus on the algorithmic aspects
related to entropic profiles. In particular, we propose
linear time algorithms for their computation that rely
on suffix-based data structures, more specifically on
the truncated suffix tree TST and on the enhanced
suffix array ESA. We performed an extensive
experimental campaign showing that our algorithms,
beside being faster, make it possible the analysis of
longer sequences, even for high degrees of resolution,
than state of the art algorithms.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kim:2018:GNA,
author = "Hyunjin Kim and Sang-Min Choi and Sanghyun Park",
title = "{GSEH}: a Novel Approach to Select Prostate
Cancer-Associated Genes Using Gene Expression
Heterogeneity",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "129--146",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2618927",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "When a gene shows varying levels of expression among
normal people but similar levels in disease patients or
shows similar levels of expression among normal people
but different levels in disease patients, we can assume
that the gene is associated with the disease. By
utilizing this gene expression heterogeneity, we can
obtain additional information that abets discovery of
disease-associated genes. In this study, we used
collaborative filtering to calculate the degree of gene
expression heterogeneity between classes and then
scored the genes on the basis of the degree of gene
expression heterogeneity to find ``differentially
predicted'' genes. Through the proposed method, we
discovered more prostate cancer-associated genes than
10 comparable methods. The genes prioritized by the
proposed method are potentially significant to
biological processes of a disease and can provide
insight into them.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2018:HSB,
author = "Jian Zhang and Haiting Chai and Bo Gao and Guifu Yang
and Zhiqiang Ma",
title = "{HEMEsPred}: Structure-Based Ligand-Specific Heme
Binding Residues Prediction by Using Fast-Adaptive
Ensemble Learning Scheme",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "147--156",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2615010",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Heme is an essential biomolecule that widely exists in
numerous extant organisms. Accurately identifying heme
binding residues HEMEs is of great importance in
disease progression and drug development. In this
study, a novel predictor named HEMEsPred was proposed
for predicting HEMEs. First, several sequence- and
structure-based features, including amino acid
composition, motifs, surface preferences, and secondary
structure, were collected to construct feature
matrices. Second, a novel fast-adaptive ensemble
learning scheme was designed to overcome the serious
class-imbalance problem as well as to enhance the
prediction performance. Third, we further developed
ligand-specific models considering that different heme
ligands varied significantly in their roles, sizes, and
distributions. Statistical test proved the
effectiveness of ligand-specific models. Experimental
results on benchmark datasets demonstrated good
robustness of our proposed method. Furthermore, our
method also showed good generalization capability and
outperformed many state-of-art predictors on two
independent testing datasets. HEMEsPred web server was
available at http://www.inforstation.com/HEMEsPred/ for
free academic use.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Taha:2018:IFP,
author = "Kamal Taha",
title = "Inferring the Functions of Proteins from the
Interrelationships between Functional Categories",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "157--167",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2615608",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This study proposes a new method to determine the
functions of an unannotated protein. The proteins and
amino acid residues mentioned in biomedical texts
associated with an unannotated protein $p$ can be
considered as characteristics terms for $p$, which are
highly predictive of the potential functions of $p$.
Similarly, proteins and amino acid residues mentioned
in biomedical texts associated with proteins annotated
with a functional category $f$ can be considered as
characteristics terms of $f$. We introduce in this
paper an information extraction system called IFP_IFC
that predicts the functions of an unannotated protein
$p$ by representing $p$ and each functional category
$f$ by a vector of weights. Each weight reflects the
degree of association between a characteristic term and
$p$ or a characteristic term and $f$. First, IFP_IFC
constructs a network, whose nodes represent the
different functional categories, and its edges the
interrelationships between the nodes. Then, it
determines the functions of $p$ by employing random
walks with restarts on the mentioned network. The
walker is the vector of $p$ . Finally, $p$ is assigned
to the functional categories of the nodes in the
network that are visited most by the walker. We
evaluated the quality of IFP_IFC by comparing it
experimentally with two other systems. Results showed
marked improvement.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Leale:2018:IUB,
author = "Guillermo Leale and Ariel Emilio Baya and Diego H.
Milone and Pablo M. Granitto and Georgina Stegmayer",
title = "Inferring Unknown Biological Function by Integration
of {GO} Annotations and Gene Expression Data",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "168--180",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2615960",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Characterizing genes with semantic information is an
important process regarding the description of gene
products. In spite that complete genomes of many
organisms have been already sequenced, the biological
functions of all of their genes are still unknown.
Since experimentally studying the functions of those
genes, one by one, would be unfeasible, new
computational methods for gene functions inference are
needed. We present here a novel computational approach
for inferring biological function for a set of genes
with previously unknown function, given a set of genes
with well-known information. This approach is based on
the premise that genes with similar behaviour should be
grouped together. This is known as the
guilt-by-association principle. Thus, it is possible to
take advantage of clustering techniques to obtain
groups of unknown genes that are co-clustered with
genes that have well-known semantic information GO
annotations. Meaningful knowledge to infer unknown
semantic information can therefore be provided by these
well-known genes. We provide a method to explore the
potential function of new genes according to those
currently annotated. The results obtained indicate that
the proposed approach could be a useful and effective
tool when used by biologists to guide the inference of
biological functions for recently discovered genes. Our
work sets an important landmark in the field of
identifying unknown gene functions through clustering,
using an external source of biological input. A simple
web interface to this proposal can be found at
http://fich.unl.edu.ar/sinc/webdemo/gamma-am/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chlis:2018:ISB,
author = "Nikolaos-Kosmas Chlis and Ekaterini S. Bei and
Michalis Zervakis",
title = "Introducing a Stable Bootstrap Validation Framework
for Reliable Genomic Signature Extraction",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "181--190",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2633267",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The application of machine learning methods for the
identification of candidate genes responsible for
phenotypes of interest, such as cancer, is a major
challenge in the field of bioinformatics. These lists
of genes are often called genomic signatures and their
linkage to phenotype associations may form a
significant step in discovering the causation between
genotypes and phenotypes. Traditional methods that
produce genomic signatures from DNA Microarray data
tend to extract significantly different lists under
relatively small variations of the training data. That
instability hinders the validity of research findings
and raises skepticism about the reliability of such
methods. In this study, a complete framework for the
extraction of stable and reliable lists of candidate
genes is presented. The proposed methodology enforces
stability of results at the validation step and as a
result, it is independent of the feature selection and
classification methods used. Furthermore, two different
statistical tests are performed in order to assess the
statistical significance of the observed results.
Moreover, the consistency of the signatures extracted
by independent executions of the proposed method is
also evaluated. The results of this study highlight the
importance of stability issues in genomic signatures,
beyond their prediction capabilities.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ma:2018:MLR,
author = "Eddie Y. T. Ma and Sujeevan Ratnasingham and Stefan C.
Kremer",
title = "Machine Learned Replacement of {$N$}-Labels for
Basecalled Sequences in {DNA} Barcoding",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "191--204",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2598752",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This study presents a machine learning method that
increases the number of identified bases in Sanger
Sequencing. The system post-processes a KB basecalled
chromatogram. It selects a recoverable subset of
N-labels in the KB-called chromatogram to replace with
basecalls A,C,G,T. An N-label correction is defined
given an additional read of the same sequence, and a
human finished sequence. Corrections are added to the
dataset when an alignment determines the additional
read and human agree on the identity of the N-label. KB
must also rate the replacement with quality value of $
> 60 $ in the additional read. Corrections are only
available during system training. Developing the
system, nearly 850,000 N-labels are obtained from
Barcode of Life Datasystems, the premier database of
genetic markers called DNA Barcodes. Increasing the
number of correct bases improves reference sequence
reliability, increases sequence identification
accuracy, and assures analysis correctness. Keeping
with barcoding standards, our system maintains an error
rate of $ < 1 $ percent. Our system only applies
corrections when it estimates low rate of error. Tested
on this data, our automation selects and recovers: 79
percent of N-labels from COI animal barcode; 80 percent
from matK and rbcL plant barcodes; and 58 percent from
non-protein-coding sequences across eukaryotes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jetten:2018:NTB,
author = "Laura Jetten and Leo van Iersel",
title = "Nonbinary Tree-Based Phylogenetic Networks",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "205--217",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2615918",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Rooted phylogenetic networks are used to describe
evolutionary histories that contain non-treelike
evolutionary events such as hybridization and
horizontal gene transfer. In some cases, such histories
can be described by a phylogenetic base-tree with
additional linking arcs, which can, for example,
represent gene transfer events. Such phylogenetic
networks are called tree-based. Here, we consider two
possible generalizations of this concept to nonbinary
networks, which we call tree-based and
strictly-tree-based nonbinary phylogenetic networks. We
give simple graph-theoretic characterizations of
tree-based and strictly-tree-based nonbinary
phylogenetic networks. Moreover, we show for each of
these two classes that it can be decided in polynomial
time whether a given network is contained in the class.
Our approach also provides a new view on tree-based
binary phylogenetic networks. Finally, we discuss two
examples of nonbinary phylogenetic networks in biology
and show how our results can be applied to them.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mohsenizadeh:2018:OOB,
author = "Daniel N. Mohsenizadeh and Roozbeh Dehghannasiri and
Edward R. Dougherty",
title = "Optimal Objective-Based Experimental Design for
Uncertain Dynamical Gene Networks with Experimental
Error",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "218--230",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2602873",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In systems biology, network models are often used to
study interactions among cellular components, a salient
aim being to develop drugs and therapeutic mechanisms
to change the dynamical behavior of the network to
avoid undesirable phenotypes. Owing to limited
knowledge, model uncertainty is commonplace and network
dynamics can be updated in different ways, thereby
giving multiple dynamic trajectories, that is, dynamics
uncertainty. In this manuscript, we propose an
experimental design method that can effectively reduce
the dynamics uncertainty and improve performance in an
interaction-based network. Both dynamics uncertainty
and experimental error are quantified with respect to
the modeling objective, herein, therapeutic
intervention. The aim of experimental design is to
select among a set of candidate experiments the
experiment whose outcome, when applied to the network
model, maximally reduces the dynamics uncertainty
pertinent to the intervention objective.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Behinaein:2018:PNS,
author = "Behnam Behinaein and Karen Rudie and Waheed Sangrar",
title = "{Petri} Net Siphon Analysis and Graph Theoretic
Measures for Identifying Combination Therapies in
Cancer",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "231--243",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2614301",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Epidermal Growth Factor Receptor EGFR signaling to the
Ras-MAPK pathway is implicated in the development and
progression of cancer and is a major focus of targeted
combination therapies. Physiochemical models have been
used for identifying and testing the signal-inhibiting
potential of targeted therapies; however, their
application to larger multi-pathway networks is limited
by the availability of experimentally-determined rate
and concentration parameters. An alternate strategy for
identifying and evaluating drug-targetable nodes is
proposed. A physiochemical model of EGFR-Ras-MAPK
signaling is implemented and calibrated to experimental
data. Essential topological features of the model are
converted into a Petri net and nodes that behave as
siphons-a structural property of Petri nets-are
identified. Siphons represent potential drug-targets
since they are unrecoverable if their values fall below
a threshold. Centrality measures are then used to
prioritize siphons identified as candidate
drug-targets. Single and multiple drug-target
combinations are identified which correspond to
clinically relevant drug targets and exhibit inhibition
synergy in physiochemical simulations of EGF-induced
EGFR-Ras-MAPK signaling. Taken together, these studies
suggest that siphons and centrality analyses are a
promising computational strategy to identify and rank
drug-targetable nodes in larger networks as they do not
require knowledge of the dynamics of the system, but
rely solely on topology.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Khan:2018:RPR,
author = "Shujaat Khan and Imran Naseem and Roberto Togneri and
Mohammed Bennamoun",
title = "{RAFP-Pred}: Robust Prediction of Antifreeze Proteins
Using Localized Analysis of $n$-Peptide Compositions",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "244--250",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2617337",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In extreme cold weather, living organisms produce
Antifreeze Proteins AFPs to counter the otherwise
lethal intracellular formation of ice. Structures and
sequences of various AFPs exhibit a high degree of
heterogeneity, consequently the prediction of the AFPs
is considered to be a challenging task. In this
research, we propose to handle this arduous manifold
learning task using the notion of localized processing.
In particular, an AFP sequence is segmented into two
sub-segments each of which is analyzed for amino acid
and di-peptide compositions. We propose to use only the
most significant features using the concept of
information gain IG followed by a random forest
classification approach. The proposed RAFP-Pred
achieved an excellent performance on a number of
standard datasets. We report a high Youden's index
sensitivity+specificity-1 value of 0.75 on the standard
independent test data set outperforming the AFP-PseAAC,
AFP_PSSM, AFP-Pred, and iAFP by a margin of 0.05, 0.06,
0.14, and 0.68, respectively. The verification rate on
the UniProKB dataset is found to be 83.19 percent which
is substantially superior to the 57.18 percent reported
for the iAFP method.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Czapla:2018:RSS,
author = "Roman Czapla",
title = "Random Sets of Stadiums in Square and Collective
Behavior of Bacteria",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "251--256",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2611676",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Collective motion of swimmers can be detected by
hydrodynamic interactions through the effective
macroscopic viscosity. It follows from the general
hydrodynamics that the effective viscosity of
non-dilute random suspensions depends on the shape of
particles and of their spacial probabilistic
distribution. Therefore, a comparative analysis of
disordered and collectively interacting particles of
the bacteria shape can be done in terms of the
probabilistic geometric parameters which determine the
effective viscosity. In this paper, we develop a
quantitative criterion to detect the collective
behavior of bacteria. This criterion is based on the
basic statistic moments $e$-sums or generalized
Eisenstein-Rayleigh sums which characterize the
high-order correlation functions. The locations and the
shape of bacteria are modeled by stadiums randomly
embedded in medium without overlapping. These shape
models can be considered as improvement of the previous
segment model. We calculate the $e$-sums of the
simulated disordered sets and of the observed
experimental locations of bacteria subtilis. The
obtained results show a difference between these two
sets that demonstrates the collective motion of
bacteria.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Borges:2018:RGS,
author = "Vinicius R. P. Borges and Maria Cristina F. de
Oliveira and Thais Garcia Silva and Armando Augusto
Henriques Vieira and Bernd Hamann",
title = "Region Growing for Segmenting Green Microalgae
Images",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "257--270",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2615606",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We describe a specialized methodology for segmenting
2D microscopy digital images of freshwater green
microalgae. The goal is to obtain representative algae
shapes to extract morphological features to be employed
in a posterior step of taxonomical classification of
the species. The proposed methodology relies on the
seeded region growing principle and on a fine-tuned
filtering preprocessing stage to smooth the input
image. A contrast enhancement process then takes place
to highlight algae regions on a binary pre-segmentation
image. This binary image is also employed to determine
where to place the seed points and to estimate the
statistical probability distributions that characterize
the target regions, i.e., the algae areas and the
background, respectively. These preliminary stages
produce the required information to set the homogeneity
criterion for region growing. We evaluate the proposed
methodology by comparing its resulting segmentations
with a set of corresponding ground-truth segmentations
provided by an expert biologist and also with
segmentations obtained with existing strategies. The
experimental results show that our solution achieves
highly accurate segmentation rates with greater
efficiency, as compared with the performance of
standard segmentation approaches and with an
alternative previous solution, based on level-sets,
also specialized to handle this particular problem.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ali:2018:SDS,
author = "M. Syed Ali and N. Gunasekaran and Choon Ki Ahn and
Peng Shi",
title = "Sampled-Data Stabilization for Fuzzy Genetic
Regulatory Networks with Leakage Delays",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "271--285",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2606477",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper deals with the sampled-data stabilization
problem for Takagi-Sugeno T-S fuzzy genetic regulatory
networks with leakage delays. A novel
Lyapunov-Krasovskii functional LKF is established by
the non-uniform division of the delay intervals with
triplex and quadruplex integral terms. Using such LKFs
for constant and time-varying delay cases, new
stability conditions are obtained in the T-S fuzzy
framework. Based on this, a new condition for the
sampled-data controller design is proposed using a
linear matrix inequality representation. A numerical
result is provided to show the effectiveness and
potential of the developed design method.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Suryanto:2018:SCC,
author = "Chendra Hadi Suryanto and Hiroto Saigo and Kazuhiro
Fukui",
title = "Structural Class Classification of {$3$D} Protein
Structure Based on Multi-View {$2$D} Images",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "286--299",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2603987",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Computing similarity or dissimilarity between protein
structures is an important task in structural biology.
A conventional method to compute protein structure
dissimilarity requires structural alignment of the
proteins. However, defining one best alignment is
difficult, especially when the structures are very
different. In this paper, we propose a new similarity
measure for protein structure comparisons using a set
of multi-view 2D images of 3D protein structures. In
this approach, each protein structure is represented by
a subspace from the image set. The similarity between
two protein structures is then characterized by the
canonical angles between the two subspaces. The primary
advantage of our method is that precise alignment is
not needed. We employed Grassmann Discriminant Analysis
GDA as the subspace-based learning in the
classification framework. We applied our method for the
classification problem of seven SCOP structural classes
of protein 3D structures. The proposed method
outperformed the k-nearest neighbor method k-NN based
on conventional alignment-based methods CE, FATCAT, and
TM-align. Our method was also applied to the
classification of SCOP folds of membrane proteins,
where the proposed method could recognize the fold
HEM-binding four-helical bundle f.21 much better than
TM-Align.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Anders:2018:IPR,
author = "Gerd Anders and Ulrich Hassiepen and Stephan Theisgen
and Stephan Heymann and Lionel Muller and Tania
Panigada and Daniel Huster and Sergey A. Samsonov",
title = "The Intrinsic Pepsin Resistance of Interleukin-8 Can
Be Explained from a Combined Bioinformatical and
Experimental Approach",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "300--308",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2614821",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Interleukin-8 IL-8, CXCL8 is a neutrophil chemotactic
factor belonging to the family of chemokines. IL-8 was
shown to resist pepsin cleavage displaying its high
resistance to this protease. However, the molecular
mechanisms underlying this resistance are not fully
understood. Using our in-house database containing the
data on three-dimensional arrangements of secondary
structure elements from the whole Protein Data Bank, we
found a striking structural similarity between IL-8 and
pepsin inhibitor-3. Such similarity could play a key
role in understanding IL-8 resistance to the protease
pepsin. To support this hypothesis, we applied pepsin
assays confirming that intact IL-8 is not degraded by
pepsin in comparison to IL-8 in a denaturated state.
Applying 1H-15N Heteronuclear Single Quantum Coherence
NMR measurements, we determined the putative regions at
IL-8 that are potentially responsible for interactions
with the pepsin. The results obtained in this work
contribute to the understanding of the resistance of
IL-8 to pepsin proteolysis in terms of its structural
properties.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Luo:2018:NAI,
author = "Jiawei Luo and Wei Huang and Buwen Cao",
title = "A Novel Approach to Identify the {miRNA--mRNA} Causal
Regulatory Modules in Cancer",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "309--315",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2612199",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "MicroRNAs miRNAs play an essential role in many
biological processes by regulating the target genes,
especially in the initiation and development of
cancers. Therefore, the identification of the
miRNA-mRNA regulatory modules is important for
understanding the regulatory mechanisms. Most
computational methods only used statistical
correlations in predicting miRNA-mRNA modules, and
neglected the fact there are causal relationships
between miRNAs and their target genes. In this paper,
we propose a novel approach called CALM the causal
regulatory modules to identify the miRNA-mRNA
regulatory modules through integrating the causal
interactions and statistical correlations between the
miRNAs and their target genes. Our algorithm largely
consists of three steps: it first forms the causal
regulatory relationships of miRNAs and genes from gene
expression profiles and detects the miRNA clusters
according to the GO function information of their
target genes, then expands each miRNA cluster by greedy
adding discarding the target genes to maximize the
modularity score. To show the performance of our
method, we apply CALM on four datasets including EMT,
breast, ovarian, and thyroid cancer and validate our
results. The experiment results show that our method
can not only outperform the compared method, but also
achieve ideal overall performance in terms of the
functional enrichment.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bao:2018:AAC,
author = "Feng Bao and Yue Deng and Qionghai Dai",
title = "{ACID}: Association Correction for Imbalanced Data in
{GWAS}",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "316--322",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2608819",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genome-wide association study GWAS has been widely
witnessed as a powerful tool for revealing suspicious
loci from various diseases. However, real world GWAS
tasks always suffer from the data imbalance problem of
sufficient control samples and limited case samples.
This imbalance issue can cause serious biases to the
result and thus leads to losses of significance for
true causal markers. To tackle this problem, we
proposed a computational framework to perform
association correction for imbalanced data ACID that
could potentially improve the performance of GWAS under
the imbalance condition. ACID is inspired by the
imbalance learning theory but is particularly modified
to address the task of association discovery from
sequential genomic data. Simulation studies demonstrate
ACID can dramatically improve the power of traditional
GWAS method on the dataset with severe imbalances. We
further applied ACID to two imbalanced datasets gastric
cancer and bladder cancer to conduct genome wide
association analysis. Experimental results indicate
that our method has better abilities in identifying
suspicious loci than the regression approach and shows
consistencies with existing discoveries.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jiao:2018:FDA,
author = "Hongmei Jiao and Michael Shi and Qikun Shen and Junwu
Zhu and Peng Shi",
title = "Filter Design with Adaptation to Time-Delay Parameters
for Genetic Regulatory Networks",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "323--329",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2606430",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In existing works, the filters designed for delayed
genetic regulatory networks GRNs contain time delay. If
the time delay is unknown, the filters do not work in
practical applications. In order to overcome the
shortcoming in such existing works, this paper studies
the filter design problem of GRNs with unknown constant
time delay, and a novel adaptive filter is introduced,
in which all unknown network parameters and the unknown
time delay can be estimated online. By Lyapunove
approach, it is shown that the estimating errors
asymptotically converge to the origin. Finally,
simulation results are presented to illustrate the
effectiveness of the new method proposed in this
paper.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Elmsallati:2018:IBN,
author = "Ahed Elmsallati and Abdulghani Msalati and Jugal
Kalita",
title = "Index-Based Network Aligner of Protein--Protein
Interaction Networks",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "330--336",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2613098",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Network Alignment over graph-structured data has
received considerable attention in many recent
applications. Global network alignment tries to
uniquely find the best mapping for a node in one
network to only one node in another network. The
mapping is performed according to some matching
criteria that depend on the nature of data. In
molecular biology, functional orthologs, protein
complexes, and evolutionary conserved pathways are some
examples of information uncovered by global network
alignment. Current techniques for global network
alignment suffer from several drawbacks, e.g., poor
performance and high memory requirements. We address
these problems by proposing IBNAL, Indexes-Based
Network ALigner, for better alignment quality and
faster results. To accelerate the alignment step, IBNAL
makes use of a novel clique-based index and is able to
align large networks in seconds. IBNAL produces a
higher topological quality alignment and comparable
biological match in alignment relative to other
state-of-the-art aligners even though topological fit
is primarily used to match nodes. IBNAL's results
confirm and give another evidence that homology
information is more likely to be encoded in network
topology than sequence information.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Allman:2018:STI,
author = "Elizabeth S. Allman and James H. Degnan and John A.
Rhodes",
title = "Species Tree Inference from Gene Splits by Unrooted
{STAR} Methods",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "337--342",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2604812",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The $ \text {NJ}_{st} $ method was proposed by Liu and
Yu to infer a species tree topology from unrooted
topological gene trees. While its statistical
consistency under the multispecies coalescent model was
established only for a four-taxon tree, simulations
demonstrated its good performance on gene trees
inferred from sequences for many taxa. Here, we prove
the statistical consistency of the method for an
arbitrarily large species tree. Our approach connects $
\text {NJ}_{st} $ to a generalization of the STAR
method of Liu, Pearl, and Edwards, and a previous
theoretical analysis of it. We further show $ \text
{NJ}_{st} $ utilizes only the distribution of splits in
the gene trees, and not their individual topologies.
Finally, we discuss how multiple samples per taxon per
gene should be handled for statistical consistency.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Matsen:2018:TRT,
author = "Frederick A. Matsen and Sara C. Billey and Arnold Kas
and Matjaz Konvalinka",
title = "Tanglegrams: a Reduction Tool for Mathematical
Phylogenetics",
journal = j-TCBB,
volume = "15",
number = "1",
pages = "343--349",
month = jan,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2613040",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 13 17:18:15 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Many discrete mathematics problems in phylogenetics
are defined in terms of the relative labeling of pairs
of leaf-labeled trees. These relative labelings are
naturally formalized as tanglegrams, which have
previously been an object of study in coevolutionary
analysis. Although there has been considerable work on
planar drawings of tanglegrams, they have not been
fully explored as combinatorial objects until recently.
In this paper, we describe how many discrete
mathematical questions on trees ``factor'' through a
problem on tanglegrams, and how understanding that
factoring can simplify analysis. Depending on the
problem, it may be useful to consider a unordered
version of tanglegrams, and/or their unrooted
counterparts. For all of these definitions, we show how
the isomorphism types of tanglegrams can be understood
in terms of double cosets of the symmetric group, and
we investigate their automorphisms. Understanding
tanglegrams better will isolate the distinct problems
on leaf-labeled pairs of trees and reveal natural
symmetries of spaces associated with such problems.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{daSilvaArruda:2018:GBH,
author = "Thiago {da Silva Arruda} and Ulisses Dias and Zanoni
Dias",
title = "A {GRASP}-Based Heuristic for the Sorting by
Length-Weighted Inversions Problem",
journal = j-TCBB,
volume = "15",
number = "2",
pages = "352--363",
month = mar,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2474400",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Apr 7 18:55:55 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genome Rearrangements are large-scale mutational
events that affect genomes during the evolutionary
process. Therefore, these mutations differ from
punctual mutations. They can move genes from one place
to the other, change the orientation of some genes, or
even change the number of chromosomes. In this work, we
deal with inversion events which occur when a segment
of DNA sequence in the genome is reversed. In our
model, each inversion costs the number of elements in
the reversed segment. We present a new algorithm for
this problem based on the metaheuristic called Greedy
Randomized Adaptive Search Procedure GRASP that has
been routinely used to find solutions for combinatorial
optimization problems. In essence, we implemented an
iterative process in which each iteration receives a
feasible solution whose neighborhood is investigated.
Our analysis shows that we outperform any other
approach by significant margin. We also use our
algorithm to build phylogenetic trees for a subset of
species in the Yersinia genus and we compared our trees
to other results in the literature.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lin:2018:MGD,
author = "Congping Lin and Laurent Lemarchand and Reinhardt
Euler and Imogen Sparkes",
title = "Modeling the Geometry and Dynamics of the Endoplasmic
Reticulum Network",
journal = j-TCBB,
volume = "15",
number = "2",
pages = "377--386",
month = mar,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2389226",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Apr 7 18:55:55 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The endoplasmic reticulum ER is an intricate network
that pervades the entire cortex of plant cells and its
geometric shape undergoes drastic changes. This paper
proposes a mathematical model to reconstruct geometric
network dynamics by combining the node movements within
the network and topological changes engendered by these
nodes. The network topology in the model is determined
by a modified optimization procedure from the work
Lemarchand, et al. 2014 which minimizes the total
length taking into account both degree and angle
constraints, beyond the conditions of connectedness and
planarity. A novel feature for solving our optimization
problem is the use of ``lifted'' angle constraints,
which allows one to considerably reduce the solution
runtimes. Using this optimization technique and a
Langevin approach for the branching node movement, the
simulated network dynamics represent the ER network
dynamics observed under latrunculin B treated condition
and recaptures features such as the
appearance/disappearance of loops within the ER under
the native condition. The proposed modeling approach
allows quantitative comparison of networks between the
model and experimental data based on topological
changes induced by node dynamics. An increased temporal
resolution of experimental data will allow a more
detailed comparison of network dynamics using this
modeling approach.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ghaffari:2018:ENT,
author = "Noushin Ghaffari and Osama A. Arshad and Hyundoo Jeong
and John Thiltges and Michael F. Criscitiello and
Byung-Jun Yoon and Aniruddha Datta and Charles D.
Johnson",
title = "Examining {De Novo} Transcriptome Assemblies via a
Quality Assessment Pipeline",
journal = j-TCBB,
volume = "15",
number = "2",
pages = "494--505",
month = mar,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2446478",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Apr 7 18:55:55 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "New de novo transcriptome assembly and annotation
methods provide an incredible opportunity to study the
transcriptome of organisms that lack an assembled and
annotated genome. There are currently a number of de
novo transcriptome assembly methods, but it has been
difficult to evaluate the quality of these assemblies.
In order to assess the quality of the transcriptome
assemblies, we composed a workflow of multiple quality
check measurements that in combination provide a clear
evaluation of the assembly performance. We presented
novel transcriptome assemblies and functional
annotations for Pacific Whiteleg Shrimp Litopenaeus
vannamei , a mariculture species with great national
and international interest, and no solid
transcriptome/genome reference. We examined Pacific
Whiteleg transcriptome assemblies via multiple metrics,
and provide an improved gene annotation. Our
investigations show that assessing the quality of an
assembly purely based on the assembler's statistical
measurements can be misleading; we propose a hybrid
approach that consists of statistical quality checks
and further biological-based evaluations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kim:2018:CPP,
author = "Mansuck Kim and Huan Zhang and Charles Woloshuk and
Won-Bo Shim and Byung-Jun Yoon",
title = "Computational Prediction of Pathogenic Network Modules
in Fusarium verticillioides",
journal = j-TCBB,
volume = "15",
number = "2",
pages = "506--515",
month = mar,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2440232",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Apr 7 18:55:55 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Fusarium verticillioides is a fungal pathogen that
triggers stalk rots and ear rots in maize. In this
study, we performed a comparative analysis of wild type
and loss-of-virulence mutant F. verticillioides
co-expression networks to identify subnetwork modules
that are associated with its pathogenicity. We
constructed the F. verticillioides co-expression
networks from RNA-Seq data and searched through these
networks to identify subnetwork modules that are
differentially activated between the wild type and
mutant F. verticillioides, which considerably differ in
terms of pathogenic potentials. A greedy
seed-and-extend approach was utilized in our search,
where we also used an efficient branch-out technique
for reliable prediction of functional subnetwork
modules in the fungus. Through our analysis, we
identified four potential pathogenicity-associated
subnetwork modules, each of which consists of
interacting genes with coordinated expression patterns,
but whose activation level is significantly different
in the wild type and the mutant. The predicted modules
were comprised of functionally coherent genes and
topologically cohesive. Furthermore, they contained
several orthologs of known pathogenic genes in other
fungi, which may play important roles in the fungal
pathogenesis.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bahadorinejad:2018:OFD,
author = "Arghavan Bahadorinejad and Ulisses M. Braga-Neto",
title = "Optimal Fault Detection and Diagnosis in
Transcriptional Circuits Using Next-Generation
Sequencing",
journal = j-TCBB,
volume = "15",
number = "2",
pages = "516--525",
month = mar,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2404819",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Apr 7 18:55:55 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We propose a methodology for model-based fault
detection and diagnosis for stochastic Boolean
dynamical systems indirectly observed through a single
time series of transcriptomic measurements using Next
Generation Sequencing NGS data. The fault detection
consists of an innovations filter followed by a fault
certification step, and requires no knowledge about the
possible system faults. The innovations filter uses the
optimal Boolean state estimator, called the Boolean
Kalman Filter BKF. In the presence of knowledge about
the possible system faults, we propose an additional
step of fault diagnosis based on a multiple model
adaptive estimation MMAE method consisting of a bank of
BKFs running in parallel. Performance is assessed by
means of false detection and misdiagnosis rates, as
well as average times until correct detection and
diagnosis. The efficacy of the proposed methodology is
demonstrated via numerical experiments using a p53-MDM2
negative feedback loop Boolean network with stuck-at
faults that model molecular events commonly found in
cancer.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cui:2018:MND,
author = "Xiaodong Cui and Lin Zhang and Jia Meng and Manjeet K.
Rao and Yidong Chen and Yufei Huang",
title = "{MeTDiff}: a Novel Differential {RNA} Methylation
Analysis for {MeRIP-Seq} Data",
journal = j-TCBB,
volume = "15",
number = "2",
pages = "526--534",
month = mar,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2403355",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Apr 7 18:55:55 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "N6-Methyladenosine m6A transcriptome methylation is an
exciting new research area that just captures the
attention of research community. We present in this
paper, MeTDiff, a novel computational tool for
predicting differential m6A methylation sites from
Methylated RNA immunoprecipitation sequencing MeRIP-Seq
data. Compared with the existing algorithm exomePeak,
the advantages of MeTDiff are that it explicitly models
the reads variation in data and also devices a more
power likelihood ratio test for differential
methylation site prediction. Comprehensive evaluation
of MeTDiff's performance using both simulated and real
datasets showed that MeTDiff is much more robust and
achieved much higher sensitivity and specificity over
exomePeak. The R package ``MeTDiff'' and additional
details are available at:
https://github.com/compgenomics/MeTDiff.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhao:2018:BMV,
author = "Yize Zhao and Jian Kang and Qi Long",
title = "{Bayesian} Multiresolution Variable Selection for
Ultra-High Dimensional Neuroimaging Data",
journal = j-TCBB,
volume = "15",
number = "2",
pages = "537--550",
month = mar,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2440244",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Apr 7 18:55:55 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Ultra-high dimensional variable selection has become
increasingly important in analysis of neuroimaging
data. For example, in the Autism Brain Imaging Data
Exchange ABIDE study, neuroscientists are interested in
identifying important biomarkers for early detection of
the autism spectrum disorder ASD using high resolution
brain images that include hundreds of thousands voxels.
However, most existing methods are not feasible for
solving this problem due to their extensive
computational costs. In this work, we propose a novel
multiresolution variable selection procedure under a
Bayesian probit regression framework. It recursively
uses posterior samples for coarser-scale variable
selection to guide the posterior inference on
finer-scale variable selection, leading to very
efficient Markov chain Monte Carlo MCMC algorithms. The
proposed algorithms are computationally feasible for
ultra-high dimensional data. Also, our model
incorporates two levels of structural information into
variable selection using Ising priors: the spatial
dependence between voxels and the functional
connectivity between anatomical brain regions. Applied
to the resting state functional magnetic resonance
imaging R-fMRI data in the ABIDE study, our methods
identify voxel-level imaging biomarkers highly
predictive of the ASD, which are biologically
meaningful and interpretable. Extensive simulations
also show that our methods achieve better performance
in variable selection compared to existing methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yuan:2018:GIH,
author = "Lin Yuan and Fanglin Chen and Ling-Li Zeng and Lubin
Wang and Dewen Hu",
title = "Gender Identification of Human Brain Image with a
Novel {$3$D} Descriptor",
journal = j-TCBB,
volume = "15",
number = "2",
pages = "551--561",
month = mar,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2448081",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Apr 7 18:55:55 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Determining gender by examining the human brain is not
a simple task because the spatial structure of the
human brain is complex, and no obvious differences can
be seen by the naked eyes. In this paper, we propose a
novel three-dimensional feature descriptor, the
three-dimensional weighted histogram of gradient
orientation 3D WHGO to describe this complex spatial
structure. The descriptor combines local information
for signal intensity and global three-dimensional
spatial information for the whole brain. We also
improve a framework to address the classification of
three-dimensional images based on MRI. This framework,
three-dimensional spatial pyramid, uses additional
information regarding the spatial relationship between
features. The proposed method can be used to
distinguish gender at the individual level. We examine
our method by using the gender identification of
individual magnetic resonance imaging MRI scans of a
large sample of healthy adults across four research
sites, resulting in up to individual-level accuracies
under the optimized parameters for distinguishing
between females and males. Compared with previous
methods, the proposed method obtains higher accuracy,
which suggests that this technology has higher
discriminative power. With its improved performance in
gender identification, the proposed method may have the
potential to inform clinical practice and aid in
research on neurological and psychiatric disorders.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hu:2018:CBG,
author = "Meng Hu and Wu Li and Hualou Liang",
title = "A Copula-Based {Granger} Causality Measure for the
Analysis of Neural Spike Train Data",
journal = j-TCBB,
volume = "15",
number = "2",
pages = "562--569",
month = mar,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2014.2388311",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Apr 7 18:55:55 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In systems neuroscience, it is becoming increasingly
common to record the activity of hundreds of neurons
simultaneously via electrode arrays. The ability to
accurately measure the causal interactions among
multiple neurons in the brain is crucial to
understanding how neurons work in concert to generate
specific brain functions. The development of new
statistical methods for assessing causal influence
between spike trains is still an active field of
neuroscience research. Here, we suggest a copula-based
Granger causality measure for the analysis of neural
spike train data. This method is built upon our recent
work on copula Granger causality for the analysis of
continuous-valued time series by extending it to
point-process neural spike train data. The proposed
method is therefore able to reveal nonlinear and
high-order causality in the spike trains while
retaining all the computational advantages such as
model-free, efficient estimation, and variability
assessment of Granger causality. The performance of our
algorithm can be further boosted with time-reversed
data. Our method performed well on extensive
simulations, and was then demonstrated on neural
activity simultaneously recorded from primary visual
cortex of a monkey performing a contour detection
task.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2018:CTC,
author = "Lijun Zhang and Ming Wang and Nicholas W. Sterling and
Eun-Young Lee and Paul J. Eslinger and Daymond Wagner
and Guangwei Du and Mechelle M. Lewis and Young Truong
and F. DuBois Bowman and Xuemei Huang",
title = "Cortical Thinning and Cognitive Impairment in
{Parkinson}'s Disease without Dementia",
journal = j-TCBB,
volume = "15",
number = "2",
pages = "570--580",
month = mar,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2465951",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Apr 7 18:55:55 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Parkinson's disease PD is a progressive
neurodegenerative disorder characterized clinically by
motor dysfunction bradykinesia, rigidity, tremor, and
postural instability, and pathologically by the loss of
dopaminergic neurons in the substantia nigra of the
basal ganglia. Growing literature supports that
cognitive deficits may also be present in PD, even in
non-demented patients. Gray matter GM atrophy has been
reported in PD and may be related to cognitive decline.
This study investigated cortical thickness in
non-demented PD subjects and elucidated its
relationship to cognitive impairment using
high-resolution T1-weighted brain MRI and comprehensive
cognitive function scores from 71 non-demented PD and
48 control subjects matched for age, gender, and
education. Cortical thickness was compared between
groups using a flexible hierarchical multivariate
Bayesian model, which accounts for correlations between
brain regions. Correlation analyses were performed
among brain areas and cognitive domains as well, which
showed significant group differences in the PD
population. Compared to Controls, PD subjects
demonstrated significant age-adjusted cortical thinning
predominantly in inferior and superior parietal areas
and extended to superior frontal, superior temporal,
and precuneus areas posterior probability $ > 0.9 $.
Cortical thinning was also found in the left precentral
and lateral occipital, and right postcentral, middle
frontal, and fusiform regions posterior probability $ >
0.9 $. PD patients showed significantly reduced
cognitive performance in executive function, including
set shifting $ p = 0.005 $ and spontaneous flexibility
$ p = 0.02 $, which were associated with the above
cortical thinning regions $ p < 0.05 $.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sato:2018:CNM,
author = "Joao Ricardo Sato and Maciel Calebe Vidal and Suzana
de Siqueira Santos and Katlin Brauer Massirer and Andre
Fujita",
title = "Complex Network Measures in Autism Spectrum
Disorders",
journal = j-TCBB,
volume = "15",
number = "2",
pages = "581--587",
month = mar,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2476787",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Apr 7 18:55:55 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recent studies have suggested abnormal brain network
organization in subjects with Autism Spectrum Disorders
ASD. Here we applied spectral clustering algorithm,
diverse centrality measures betweenness BC, clustering
CC, eigenvector EC, and degree DC, and also the network
entropy NE to identify brain sub-systems associated
with ASD. We have found that BC increases in the
following ASD clusters: in the somatomotor,
default-mode, cerebellar, and fronto-parietal. On the
other hand, CC, EC, and DC decrease in the somatomotor,
default-mode, and cerebellar clusters. Additionally, NE
decreases in ASD in the cerebellar cluster. These
findings reinforce the hypothesis of under-connectivity
in ASD and suggest that the difference in the network
organization is more prominent in the cerebellar
system. The cerebellar cluster presents reduced NE in
ASD, which relates to a more regular organization of
the networks. These results might be important to
improve current understanding about the etiological
processes and the development of potential tools
supporting diagnosis and therapeutic interventions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Handl:2018:AAD,
author = "J. Handl and A. Shehu and Jose {Santos Reyes}",
title = "Advances in the Application and Development of
Non-Linear Global Optimization Techniques in
Computational Structural Biology",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "688--689",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2817267",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Correa:2018:MAP,
author = "Leonardo Correa and Bruno Borguesan and Camilo Farfan
and Mario Inostroza-Ponta and Marcio Dorn",
title = "A Memetic Algorithm for {$3$D} Protein Structure
Prediction Problem",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "690--704",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2635143",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Memetic Algorithms are population-based metaheuristics
intrinsically concerned with exploiting all available
knowledge about the problem under study. The
incorporation of problem domain knowledge is not an
optional mechanism, but a fundamental feature of the
Memetic Algorithms. In this paper, we present a Memetic
Algorithm to tackle the three-dimensional protein
structure prediction problem. The method uses a
structured population and incorporates a Simulated
Annealing algorithm as a local search strategy, as well
as ad-hoc crossover and mutation operators to deal with
the problem. It takes advantage of structural knowledge
stored in the Protein Data Bank, by using an Angle
Probability List that helps to reduce the search space
and to guide the search strategy. The proposed
algorithm was tested on 19 protein sequences of amino
acid residues, and the results show the ability of the
algorithm to find native-like protein structures.
Experimental results have revealed that the proposed
algorithm can find good solutions regarding
root-mean-square deviation and global distance total
score test in comparison with the experimental protein
structures. We also show that our results are
comparable in terms of folding organization with
state-of-the-art prediction methods, corroborating the
effectiveness of our proposal.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huang:2018:GNA,
author = "Jiaxiang Huang and Maoguo Gong and Lijia Ma",
title = "A Global Network Alignment Method Using Discrete
Particle Swarm Optimization",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "705--718",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2618380",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Molecular interactions data increase exponentially
with the advance of biotechnology. This makes it
possible and necessary to comparatively analyze the
different data at a network level. Global network
alignment is an important network comparison approach
to identify conserved subnetworks and get insight into
evolutionary relationship across species. Network
alignment which is analogous to subgraph isomorphism is
known to be an NP-hard problem. In this paper, we
introduce a novel heuristic Particle-Swarm-Optimization
based Network Aligner PSONA, which optimizes a weighted
global alignment model considering both protein
sequence similarity and interaction conservations. The
particle statuses and status updating rules are
redefined in a discrete form by using permutation. A
seed-and-extend strategy is employed to guide the
searching for the superior alignment. The proposed
initialization method ``seeds'' matches with high
sequence similarity into the alignment, which
guarantees the functional coherence of the mapping
nodes. A greedy local search method is designed as the
``extension'' procedure to iteratively optimize the
edge conservations. PSONA is compared with several
state-of-art methods on ten network pairs combined by
five species. The experimental results demonstrate that
the proposed aligner can map the proteins with high
functional coherence and can be used as a booster to
effectively refine the well-studied aligners.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sapin:2018:OME,
author = "Emmanuel Sapin and Kenneth A. {De Jong} and Amarda
Shehu",
title = "From Optimization to Mapping: an Evolutionary
Algorithm for Protein Energy Landscapes",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "719--731",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2628745",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Stochastic search is often the only viable option to
address complex optimization problems. Recently,
evolutionary algorithms have been shown to handle
challenging continuous optimization problems related to
protein structure modeling. Building on recent work in
our laboratories, we propose an evolutionary algorithm
for efficiently mapping the multi-basin energy
landscapes of dynamic proteins that switch between
thermodynamically stable or semi-stable structural
states to regulate their biological activity in the
cell. The proposed algorithm balances computational
resources between exploration and exploitation of the
nonlinear, multimodal landscapes that characterize
multi-state proteins via a novel combination of global
and local search to generate a dynamically-updated,
information-rich map of a protein's energy landscape.
This new mapping-oriented EA is applied to several
dynamic proteins and their disease-implicated variants
to illustrate its ability to map complex energy
landscapes in a computationally feasible manner. We
further show that, given the availability of such maps,
comparison between the maps of wildtype and variants of
a protein allows for the formulation of a structural
and thermodynamic basis for the impact of sequence
mutations on dysfunction that may prove useful in
guiding further wet-laboratory investigations of
dysfunction and molecular interventions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rydzewski:2018:CSB,
author = "Jakub Rydzewski and Rafal Jakubowski and Giuseppe
Nicosia and Wieslaw Nowak",
title = "Conformational Sampling of a Biomolecular Rugged
Energy Landscape",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "732--739",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2634008",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The protein structure refinement using conformational
sampling is important in hitherto protein studies. In
this paper, we examined the protein structure
refinement by means of potential energy minimization
using immune computing as a method of sampling
conformations. The method was tested on the x-ray
structure and 30 decoys of the mutant of
[Leu]Enkephalin, a paradigmatic example of the
biomolecular multiple-minima problem. In order to score
the refined conformations, we used a standard potential
energy function with the OPLSAA force field. The
effectiveness of the search was assessed using a
variety of methods. The robustness of sampling was
checked by the energy yield function which measures
quantitatively the number of the peptide decoys
residing in an energetic funnel. Furthermore, the
potential energy-dependent Pareto fronts were
calculated to elucidate dissimilarities between peptide
conformations and the native state as observed by x-ray
crystallography. Our results showed that the probed
potential energy landscape of [Leu]Enkephalin is
self-similar on different metric scales and that the
local potential energy minima of the peptide decoys are
metastable, thus they can be refined to conformations
whose potential energy is decreased by approximately
$-$250 kJ/mol.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Leinweber:2018:GBP,
author = "Matthias Leinweber and Thomas Fober and Bernd
Freisleben",
title = "{GPU-Based} Point Cloud Superpositioning for
Structural Comparisons of Protein Binding Sites",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "740--752",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2625793",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, we present a novel approach to solve
the labeled point cloud superpositioning problem for
performing structural comparisons of protein binding
sites. The solution is based on a parallel evolution
strategy that operates on large populations and runs on
GPU hardware. The proposed evolution strategy reduces
the likelihood of getting stuck in a local optimum of
the multimodal real-valued optimization problem
represented by labeled point cloud superpositioning.
The performance of the GPU-based parallel evolution
strategy is compared to a previously proposed CPU-based
sequential approach for labeled point cloud
superpositioning, indicating that the GPU-based
parallel evolution strategy leads to qualitatively
better results and significantly shorter runtimes, with
speed improvements of up to a factor of 1,500 for large
populations. Binary classification tests based on the
ATP, NADH, and FAD protein subsets of CavBase, a
database containing putative binding sites, show
average classification rate improvements from about 92
percent CPU to 96 percent GPU. Further experiments
indicate that the proposed GPU-based labeled point
cloud superpositioning approach can be superior to
traditional protein comparison approaches based on
sequence alignments.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhou:2018:BNR,
author = "Jie Zhou and Yuan-Yuan Shi",
title = "A Bipartite Network and Resource Transfer-Based
Approach to Infer {lncRNA}-Environmental Factor
Associations",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "753--759",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2695187",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Phenotypes and diseases are often determined by the
complex interactions between genetic factors and
environmental factors EFs. However, compared with
protein-coding genes and microRNAs, there is a paucity
of computational methods for understanding the
associations between long non-coding RNAs lncRNAs and
EFs. In this study, we focused on the associations
between lncRNA and EFs. By using the common miRNA
partners of any pair of lncRNA and EF, based on the
competing endogenous RNA ceRNA hypothesis and the
technique of resources transfer within the
experimentally-supported lncRNA-miRNA and miRNA-EF
association bipartite networks, we propose an algorithm
for predicting new lncRNA-EF associations. Results show
that, compared with another recently-proposed method,
our approach is capable of predicting more credible
lncRNA-EF associations. These results support the
validity of our approach to predict biologically
significant associations, which could lead to a better
understanding of the molecular processes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pesonen:2018:CPN,
author = "Maiju Pesonen and Jaakko Nevalainen and Steven Potter
and Somnath Datta and Susmita Datta",
title = "A Combined {PLS} and Negative Binomial Regression
Model for Inferring Association Networks from
Next-Generation Sequencing Count Data",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "760--773",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2665495",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A major challenge of genomics data is to detect
interactions displaying functional associations from
large-scale observations. In this study, a new
cPLS-algorithm combining partial least squares approach
with negative binomial regression is suggested to
reconstruct a genomic association network for
high-dimensional next-generation sequencing count data.
The suggested approach is applicable to the raw counts
data, without requiring any further pre-processing
steps. In the settings investigated, the cPLS-algorithm
outperformed the two widely used comparative methods,
graphical lasso, and weighted correlation network
analysis. In addition, cPLS is able to estimate the
full network for thousands of genes without major
computational load. Finally, we demonstrate that cPLS
is capable of finding biologically meaningful
associations by analyzing an example data set from a
previously published study to examine the molecular
anatomy of the craniofacial development.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2018:CSI,
author = "Xiangtao Li and Ka-Chun Wong",
title = "A Comparative Study for Identifying the
Chromosome-Wide Spatial Clusters from High-Throughput
Chromatin Conformation Capture Data",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "774--787",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2684800",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In the past years, the high-throughput sequencing
technologies have enabled massive insights into genomic
annotations. In contrast, the full-scale
three-dimensional arrangements of genomic regions are
relatively unknown. Thanks to the recent breakthroughs
in High-throughput Chromosome Conformation Capture Hi-C
techniques, non-negative matrix factorization NMF has
been adopted to identify local spatial clusters of
genomic regions from Hi-C data. However, such
non-negative matrix factorization entails a
high-dimensional non-convex objective function to be
optimized with non-negative constraints. We propose and
compare more than ten optimization algorithms to
improve the identification of local spatial clusters
via NMF. To circumvent and optimize the
high-dimensional, non-convex, and constrained objective
function, we draw inspiration from the nature to
perform in silico evolution. The proposed algorithms
consist of a population of candidates to be evolved
while the NMF acts as local search during the
evolutions. The population based optimization algorithm
coordinates and guides the non-negative matrix
factorization toward global optima. Experimental
results show that the proposed algorithms can improve
the quality of non-negative matrix factorization over
the recent state-of-the-arts. The effectiveness and
robustness of the proposed algorithms are supported by
comprehensive performance benchmarking on
chromosome-wide Hi-C contact maps of yeast and human.
In addition, time complexity analysis, convergence
analysis, parameter analysis, biological case studies,
and gene ontology similarity analysis are conducted to
demonstrate the robustness of the proposed methods from
different perspectives.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Peng:2018:JTL,
author = "Xin Peng and Yang Tang and Wangli He and Wenli Du and
Feng Qian",
title = "A Just-in-Time Learning Based Monitoring and
Classification Method for Hyper\slash Hypocalcemia
Diagnosis",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "788--801",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2655522",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This study focuses on the classification and
pathological status monitoring of hyper/hypo-calcemia
in the calcium regulatory system. By utilizing the
Independent Component Analysis ICA mixture model,
samples from healthy patients are collected, diagnosed,
and subsequently classified according to their
underlying behaviors, characteristics, and mechanisms.
Then, a Just-in-Time Learning JITL has been employed in
order to estimate the diseased status dynamically. In
terms of JITL, for the purpose of the construction of
an appropriate similarity index to identify relevant
datasets, a novel similarity index based on the ICA
mixture model is proposed in this paper to improve
online model quality. The validity and effectiveness of
the proposed approach have been demonstrated by
applying it to the calcium regulatory system under
various hypocalcemic and hypercalcemic diseased
conditions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhou:2018:STM,
author = "Xichuan Zhou and Fan Yang and Yujie Feng and Qin Li
and Fang Tang and Shengdong Hu and Zhi Lin and Lei
Zhang",
title = "A Spatial-Temporal Method to Detect Global Influenza
Epidemics Using Heterogeneous Data Collected from the
{Internet}",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "802--812",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2690631",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The 2009 influenza pandemic teaches us how fast the
influenza virus could spread globally within a short
period of time. To address the challenge of timely
global influenza surveillance, this paper presents a
spatial-temporal method that incorporates heterogeneous
data collected from the Internet to detect influenza
epidemics in real time. Specifically, the influenza
morbidity data, the influenza-related Google query data
and news data, and the international air transportation
data are integrated in a multivariate hidden Markov
model, which is designed to describe the intrinsic
temporal-geographical correlation of influenza
transmission for surveillance purpose. Respective
models are built for 106 countries and regions in the
world. Despite that the WHO morbidity data are not
always available for most countries, the proposed
method achieves 90.26 to 97.10 percent accuracy on
average for real-time detection of global influenza
epidemics during the period from January 2005 to
December 2015. Moreover, experiment shows that, the
proposed method could even predict an influenza
epidemic before it occurs with 89.20 percent accuracy
on average. Timely international surveillance results
may help the authorities to prevent and control the
influenza disease at the early stage of a global
influenza pandemic.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Avcu:2018:ACM,
author = "Neslihan Avcu and Nihal Pekergin and Ferhan Pekergin
and Cuneyt Guzelis",
title = "Aggregation for Computing Multi-Modal Stationary
Distributions in {$1$-D} Gene Regulatory Networks",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "813--827",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2699177",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper proposes aggregation-based, three-stage
algorithms to overcome the numerical problems
encountered in computing stationary distributions and
mean first passage times for multi-modal birth-death
processes of large state space sizes. The considered
birth-death processes which are defined by Chemical
Master Equations are used in modeling stochastic
behavior of gene regulatory networks. Computing
stationary probabilities for a multi-modal distribution
from Chemical Master Equations is subject to have
numerical problems due to the probability values
running out of the representation range of the standard
programming languages with the increasing size of the
state space. The aggregation is shown to provide a
solution to this problem by analyzing first reduced
size subsystems in isolation and then considering the
transitions between these subsystems. The proposed
algorithms are applied to study the bimodal behavior of
the lac operon of E. coli described with a
one-dimensional birth-death model. Thus, the
determination of the entire parameter range of
bimodality for the stochastic model of lac operon is
achieved.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Shao:2018:OCG,
author = "Wei Shao and Mingxia Liu and Ying-Ying Xu and Hong-Bin
Shen and Daoqiang Zhang",
title = "An Organelle Correlation-Guided Feature Selection
Approach for Classifying Multi-Label Subcellular
Bio-Images",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "828--838",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2677907",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Nowadays, with the advances in microscopic imaging,
accurate classification of bioimage-based protein
subcellular location pattern has attracted as much
attention as ever. One of the basic challenging
problems is how to select the useful feature components
among thousands of potential features to describe the
images. This is not an easy task especially considering
there is a high ratio of multi-location proteins.
Existing feature selection methods seldom take the
correlation among different cellular compartments into
consideration, and thus may miss some features that
will be co-important for several subcellular locations.
To deal with this problem, we make use of the important
structural correlation among different cellular
compartments and propose an organelle structural
correlation regularized feature selection method CSF
Common-Sets of Features in this paper. We formulate the
multi-label classification problem by adopting a
group-sparsity regularizer to select common subsets of
relevant features from different cellular compartments.
In addition, we also add a cell structural correlation
regularized Laplacian term, which utilizes the prior
biological structural information to capture the
intrinsic dependency among different cellular
compartments. The CSF provides a new feature selection
strategy for multi-label bio-image subcellular pattern
classifications, and the experimental results also show
its superiority when comparing with several existing
algorithms.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Dutta:2018:ASS,
author = "Pritha Dutta and Subhadip Basu and Mahantapas Kundu",
title = "Assessment of Semantic Similarity between Proteins
Using Information Content and Topological Properties of
the Gene Ontology Graph",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "839--849",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2689762",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The semantic similarity between two interacting
proteins can be estimated by combining the similarity
scores of the GO terms associated with the proteins.
Greater number of similar GO annotations between two
proteins indicates greater interaction affinity.
Existing semantic similarity measures make use of the
GO graph structure, the information content of GO
terms, or a combination of both. In this paper, we
present a hybrid approach which utilizes both the
topological features of the GO graph and information
contents of the GO terms. More specifically, we 1
consider a fuzzy clustering of the GO graph based on
the level of association of the GO terms, 2 estimate
the GO term memberships to each cluster center based on
the respective shortest path lengths, and 3 assign
weightage to GO term pairs on the basis of their
dissimilarity with respect to the cluster centers. We
test the performance of our semantic similarity measure
against seven other previously published similarity
measures using benchmark protein-protein interaction
datasets of Homo sapiens and Saccharomyces cerevisiae
based on sequence similarity, Pfam similarity, area
under ROC curve, and $ F_1 $ measure.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pirayre:2018:BCC,
author = "Aurelie Pirayre and Camille Couprie and Laurent Duval
and Jean-Christophe Pesquet",
title = "{BRANE Clust}: Cluster-Assisted Gene Regulatory
Network Inference Refinement",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "850--860",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2688355",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Discovering meaningful gene interactions is crucial
for the identification of novel regulatory processes in
cells. Building accurately the related graphs remains
challenging due to the large number of possible
solutions from available data. Nonetheless, enforcing a
priori on the graph structure, such as modularity, may
reduce network indeterminacy issues. BRANE Clust
Biologically-Related A priori Network Enhancement with
Clustering refines gene regulatory network GRN
inference thanks to cluster information. It works as a
post-processing tool for inference methods i.e., CLR,
GENIE3. In BRANE Clust, the clustering is based on the
inversion of a system of linear equations involving a
graph-Laplacian matrix promoting a modular structure.
Our approach is validated on DREAM4 and DREAM5 datasets
with objective measures, showing significant
comparative improvements. We provide additional
insights on the discovery of novel regulatory or
co-expressed links in the inferred Escherichia coli
network evaluated using the STRING database. The
comparative pertinence of clustering is discussed
computationally SIMoNe, WGCNA, X-means and biologically
RegulonDB. BRANE Clust software is available at:
http://www-syscom.univ-mlv.fr/~pirayre/Codes-GRN-BRANE-clust.html.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hashem:2018:CML,
author = "Somaya Hashem and Gamal Esmat and Wafaa Elakel and
Shahira Habashy and Safaa Abdel Raouf and Mohamed
Elhefnawi and Mohamed Eladawy and Mahmoud ElHefnawi",
title = "Comparison of Machine Learning Approaches for
Prediction of Advanced Liver Fibrosis in Chronic
Hepatitis {C} Patients",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "861--868",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2690848",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Background/Aim: Using machine learning approaches as
non-invasive methods have been used recently as an
alternative method in staging chronic liver diseases
for avoiding the drawbacks of biopsy. This study aims
to evaluate different machine learning techniques in
prediction of advanced fibrosis by combining the serum
bio-markers and clinical information to develop the
classification models. Methods: A prospective cohort of
39,567 patients with chronic hepatitis C was divided
into two sets-one categorized as mild to moderate
fibrosis F0-F2, and the other categorized as advanced
fibrosis F3-F4 according to METAVIR score. Decision
tree, genetic algorithm, particle swarm optimization,
and multi-linear regression models for advanced
fibrosis risk prediction were developed. Receiver
operating characteristic curve analysis was performed
to evaluate the performance of the proposed models.
Results: Age, platelet count, AST, and albumin were
found to be statistically significant to advanced
fibrosis. The machine learning algorithms under study
were able to predict advanced fibrosis in patients with
HCC with AUROC ranging between 0.73 and 0.76 and
accuracy between 66.3 and 84.4 percent. Conclusions:
Machine-learning approaches could be used as
alternative methods in prediction of the risk of
advanced liver fibrosis due to chronic hepatitis C.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ray:2018:DPM,
author = "Sumanta Ray and Ujjwal Maulik",
title = "Discovering Perturbation of Modular Structure in {HIV}
Progression by Integrating Multiple Data Sources
Through Non-Negative Matrix Factorization",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "869--877",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2642184",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Detecting perturbation in modular structure during
HIV-1 disease progression is an important step to
understand stage specific infection pattern of HIV-1
virus in human cell. In this article, we proposed a
novel methodology on integration of multiple biological
information to identify such disruption in human gene
module during different stages of HIV-1 infection. We
integrate three different biological information: gene
expression information, protein-protein interaction
information, and gene ontology information in single
gene meta-module, through non negative matrix
factorization NMF. As the identified meta-modules
inherit those information so, detecting perturbation of
these, reflects the changes in expression pattern, in
PPI structure and in functional similarity of genes
during the infection progression. To integrate modules
of different data sources into strong meta-modules, NMF
based clustering is utilized here. Perturbation in
meta-modular structure is identified by investigating
the topological and intramodular properties and putting
rank to those meta-modules using a rank aggregation
algorithm. We have also analyzed the preservation
structure of significant GO terms in which the human
proteins of the meta-modules participate. Moreover, we
have performed an analysis to show the change of
coregulation pattern of identified transcription
factors TFs over the HIV progression stages.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Awdeh:2018:DEA,
author = "Aseel Awdeh and Hilary Phenix and Mads Karn and
Theodore J. Perkins",
title = "Dynamics in Epistasis Analysis",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "878--891",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2653110",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Finding regulatory relationships between genes,
including the direction and nature of influence between
them, is a fundamental challenge in the field of
molecular genetics. One classical approach to this
problem is epistasis analysis. Broadly speaking,
epistasis analysis infers the regulatory relationships
between a pair of genes in a genetic pathway by
considering the patterns of change in an observable
trait resulting from single and double deletion of
genes. While classical epistasis analysis has yielded
deep insights on numerous genetic pathways, it is not
without limitations. Here, we explore the possibility
of dynamic epistasis analysis, in which, in addition to
performing genetic perturbations of a pathway, we drive
the pathway by a time-varying upstream signal. We
explore the theoretical power of dynamical epistasis
analysis by conducting an identifiability analysis of
Boolean models of genetic pathways, comparing static
and dynamic approaches. We find that even relatively
simple input dynamics greatly increases the power of
epistasis analysis to discriminate alternative network
structures. Further, we explore the question of
experiment design, and show that a subset of short
time-varying signals, which we call dynamic primitives,
allow maximum discriminative power with a reduced
number of experiments.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{He:2018:EGC,
author = "Tiantian He and Keith C. C. Chan",
title = "Evolutionary Graph Clustering for Protein Complex
Identification",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "892--904",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2642107",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper presents a graph clustering algorithm,
called EGCPI, to discover protein complexes in
protein-protein interaction PPI networks. In performing
its task, EGCPI takes into consideration both network
topologies and attributes of interacting proteins, both
of which have been shown to be important for protein
complex discovery. EGCPI formulates the problem as an
optimization problem and tackles it with evolutionary
clustering. Given a PPI network, EGCPI first annotates
each protein with corresponding attributes that are
provided in Gene Ontology database. It then adopts a
similarity measure to evaluate how similar the
connected proteins are taking into consideration the
network topology. Given this measure, EGCPI then
discovers a number of graph clusters within which
proteins are densely connected, based on an
evolutionary strategy. At last, EGCPI identifies
protein complexes in each discovered cluster based on
the homogeneity of attributes performed by pairwise
proteins. EGCPI has been tested with several real data
sets and the experimental results show EGCPI is very
effective on protein complex discovery, and the
evolutionary clustering is helpful to identify protein
complexes in PPI networks. The software of EGCPI can be
downloaded via:
https://github.com/hetiantian1985/EGCPI.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ikram:2018:ICB,
author = "Najmul Ikram and Muhammad Abdul Qadir and Muhammad
Tanvir Afzal",
title = "Investigating Correlation between Protein Sequence
Similarity and Semantic Similarity Using Gene Ontology
Annotations",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "905--912",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2695542",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Sequence similarity is a commonly used measure to
compare proteins. With the increasing use of
ontologies, semantic function similarity is getting
importance. The correlation between these measures has
been applied in the evaluation of new semantic
similarity methods, and in protein function prediction.
In this research, we investigate the relationship
between the two similarity methods. The results suggest
absence of a strong correlation between sequence and
semantic similarities. There is a large number of
proteins with low sequence similarity and high semantic
similarity. We observe that Pearson's correlation
coefficient is not sufficient to explain the nature of
this relationship. Interestingly, the term semantic
similarity values above 0 and below 1 do not seem to
play a role in improving the correlation. That is, the
correlation coefficient depends only on the number of
common GO terms in proteins under comparison, and the
semantic similarity measurement method does not
influence it. Semantic similarity and sequence
similarity have a distinct behavior. These findings are
of significant effect for future works on protein
comparison, and will help understand the semantic
similarity between proteins in a better way.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhu:2018:LLM,
author = "Lin Zhu and Hong-Bo Zhang and De-Shuang Huang",
title = "{LMMO}: a Large Margin Approach for Refining
Regulatory Motifs",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "913--925",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2691325",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Although discriminative motif discovery DMD methods
are promising for eliciting motifs from high-throughput
experimental data, they usually have to sacrifice
accuracy and may fail to fully leverage the potential
of large datasets. Recently, it has been demonstrated
that the motifs identified by DMDs can be significantly
improved by maximizing the receiver-operating
characteristic curve AUC metric, which has been widely
used in the literature to rank the performance of
elicited motifs. However, existing approaches for motif
refinement choose to directly maximize the non-convex
and discontinuous AUC itself, which is known to be
difficult and may lead to suboptimal solutions. In this
paper, we propose Large Margin Motif Optimizer LMMO, a
large-margin-type algorithm for refining regulatory
motifs. By relaxing the AUC cost function with the
surrogate convex hinge loss, we show that the resultant
learning problem can be cast as an instance of
difference-of-convex DC programs, and solve it
iteratively using constrained concave-convex procedure
CCCP. To further save computational time, we combine
LMMO with existing techniques for improving the
scalability of large-margin-type algorithms, such as
cutting plane method. Experimental evaluations on
synthetic and real data illustrate the performance of
the proposed approach. The code of LMMO is freely
available at: https://github.com/ekffar/LMMO.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Alves:2018:MSE,
author = "Pedro Alves and Shuang Liu and Daifeng Wang and Mark
Gerstein",
title = "Multiple-Swarm Ensembles: Improving the Predictive
Power and Robustness of Predictive Models and Its Use
in Computational Biology",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "926--933",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2691329",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Machine learning is an integral part of computational
biology, and has already shown its use in various
applications, such as prognostic tests. In the last few
years in the non-biological machine learning community,
ensembling techniques have shown their power in data
mining competitions such as the Netflix challenge;
however, such methods have not found wide use in
computational biology. In this work, we endeavor to
show how ensembling techniques can be applied to
practical problems, including problems in the field of
bioinformatics, and how they often outperform other
machine learning techniques in both predictive power
and robustness. Furthermore, we develop a methodology
of ensembling, Multi-Swarm Ensemble MSWE by using
multiple particle swarm optimizations and demonstrate
its ability to further enhance the performance of
ensembles.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fotoohifiroozabadi:2018:NFN,
author = "Samira Fotoohifiroozabadi and Mohd Saberi Mohamad and
Safaai Deris",
title = "{NAHAL-Flex}: a Numerical and Alphabetical Hinge
Detection Algorithm for Flexible Protein Structure
Alignment",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "934--943",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2705080",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Flexible proteins are proteins that have
conformational changes in their structures. Protein
flexibility analysis is critical for classifying and
understanding protein functionality. For that analysis,
the hinge areas where proteins show flexibility must be
detected. To detect the location of the hinges,
previous methods have utilized the three-dimensional 3D
structure of proteins, which is highly computational.
To reduce the computational complexity, this study
proposes a novel text-based method using structural
alphabets SAs for detecting the hinge position, called
NAHAL-Flex. Protein structures were encoded to a
particular type of SA called the protein folding shape
code PFSC, which remains unaffected by location, scale,
and rotation. The flexible regions of the proteins are
the only places in which letter sequences can be
distorted. With this knowledge, it is possible to find
the longest alignment path of two letter sequences
using a dynamic programming DP algorithm. Then, the
proposed method looks for regions where the alphabet
sequence is distorted to find the most probable hinge
positions. In order to reduce the number of hinge
positions, a genetic algorithm GA was utilized to find
the best candidate hinge points. To evaluate the
method's effectiveness, four different flexible and
rigid protein databases, including two small datasets
and two large datasets, were utilized. For the small
dataset, the NAHAL-Flex method was comparable to
state-of-the-art structural flexible alignment methods.
The result for the large datasets show that NAHAL-Flex
outperforms some well-known alignment methods, e.g.,
DaliLite, Matt, DeepAlign, and TM-align; the speed of
NAHAL-Flex was faster and its result was more accurate
than the other methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Min:2018:NRS,
author = "Wenwen Min and Juan Liu and Shihua Zhang",
title = "Network-Regularized Sparse Logistic Regression Models
for Clinical Risk Prediction and Biomarker Discovery",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "944--953",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2640303",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Molecular profiling data e.g., gene expression has
been used for clinical risk prediction and biomarker
discovery. However, it is necessary to integrate other
prior knowledge like biological pathways or gene
interaction networks to improve the predictive ability
and biological interpretability of biomarkers. Here, we
first introduce a general regularized Logistic
Regression LR framework with regularized term $ \lambda
\Vert \boldsymbol {w} \Vert_1 + \eta \boldsymbol {w}^T
\boldsymbol {M} \boldsymbol {w} $, which can reduce to
different penalties, including Lasso, elastic net, and
network-regularized terms with different $ \boldsymbol
{M} $. This framework can be easily solved in a unified
manner by a cyclic coordinate descent algorithm which
can avoid inverse matrix operation and accelerate the
computing speed. However, if those estimated $
\boldsymbol {w}_i $ and $ \boldsymbol {w}_j $ have
opposite signs, then the traditional
network-regularized penalty may not perform well. To
address it, we introduce a novel network-regularized
sparse LR model with a new penalty $ \lambda \Vert
\boldsymbol {w} \Vert_1 + \eta | \boldsymbol {w}|^T
\boldsymbol {M}| \boldsymbol {w}| $ to consider the
difference between the absolute values of the
coefficients. We develop two efficient algorithms to
solve it. Finally, we test our methods and compare them
with the related ones using simulated and real data to
show their efficiency.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chicco:2018:NIE,
author = "Davide Chicco and Fernando Palluzzi and Marco
Masseroli",
title = "Novelty Indicator for Enhanced Prioritization of
Predicted Gene Ontology Annotations",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "954--965",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2695459",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Biomolecular controlled annotations have become
pivotal in computational biology, because they allow
scientists to analyze large amounts of biological data
to better understand test results, and to infer new
knowledge. Yet, biomolecular annotation databases are
incomplete by definition, like our knowledge of
biology, and might contain errors and inconsistent
information. In this context, machine-learning
algorithms able to predict and prioritize new
annotations are both effective and efficient,
especially if compared with time-consuming trials of
biological validation. To limit the possibility that
these techniques predict obvious and trivial high-level
features, and to help prioritize their results, we
introduce a new element that can improve accuracy and
relevance of the results of an annotation prediction
and prioritization pipeline. We propose a novelty
indicator able to state the level of ``originality'' of
the annotations predicted for a specific gene to Gene
Ontology GO terms. This indicator, joint with our
previously introduced prediction steps, helps by
prioritizing the most novel interesting annotations
predicted. We performed an accurate biological
functional analysis of the prioritized annotations
predicted with high accuracy by our indicator and
previously proposed methods. The relevance of our
biological findings proves effectiveness and
trustworthiness of our indicator and of its
prioritization of predicted annotations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Khalid:2018:PHD,
author = "Zoya Khalid and Osman Ugur Sezerman",
title = "Prediction of {HIV} Drug Resistance by Combining
Sequence and Structural Properties",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "966--973",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2638821",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Drug resistance is a major obstacle faced by therapist
in treating HIV infected patients. The reason behind
these phenomena is either protein mutation or the
changes in gene expression level that induces
resistance to drug treatments. These mutations affect
the drug binding activity, hence resulting in failure
of treatment. Therefore, it is necessary to conduct
resistance testing in order to carry out HIV effective
therapy. This study combines both sequence and
structural features for predicting HIV resistance by
applying SVM and Random Forests classifiers. The model
was tested on the mutants of HIV-1 protease and reverse
transcriptase. Taken together the features we have used
in our method, total contact energies among multiple
mutations have a strong impact in predicting resistance
as they are crucial in understanding the interactions
of HIV mutants. The combination of sequence-structure
features offers high accuracy with support vector
machines as compared to Random Forests classifier. Both
single and acquisition of multiple mutations are
important in predicting HIV resistance to certain drug
treatments. We have discovered the practicality of
these features; hence, these can be used in the future
to predict resistance for other complex diseases.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2018:RNN,
author = "Jin-Xing Liu and Dong Wang and Ying-Lian Gao and
Chun-Hou Zheng and Yong Xu and Jiguo Yu",
title = "Regularized Non-Negative Matrix Factorization for
Identifying Differentially Expressed Genes and
Clustering Samples: a Survey",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "974--987",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2665557",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Non-negative Matrix Factorization NMF, a classical
method for dimensionality reduction, has been applied
in many fields. It is based on the idea that negative
numbers are physically meaningless in various
data-processing tasks. Apart from its contribution to
conventional data analysis, the recent overwhelming
interest in NMF is due to its newly discovered ability
to solve challenging data mining and machine learning
problems, especially in relation to gene expression
data. This survey paper mainly focuses on research
examining the application of NMF to identify
differentially expressed genes and to cluster samples,
and the main NMF models, properties, principles, and
algorithms with its various generalizations,
extensions, and modifications are summarized. The
experimental results demonstrate the performance of the
various NMF algorithms in identifying differentially
expressed genes and clustering samples.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2018:DMD,
author = "Junhua Zhang and Shihua Zhang",
title = "The Discovery of Mutated Driver Pathways in Cancer:
Models and Algorithms",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "988--998",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2640963",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The pathogenesis of cancer in human is still poorly
understood. With the rapid development of
high-throughput sequencing technologies, huge volumes
of cancer genomics data have been generated.
Deciphering that data poses great opportunities and
challenges to computational biologists. One of such key
challenges is to distinguish driver mutations, genes as
well as pathways from passenger ones. Mutual
exclusivity of gene mutations each patient has no more
than one mutation in the gene set has been observed in
various cancer types and thus has been used as an
important property of a driver gene set or pathway. In
this article, we aim to review the recent development
of computational models and algorithms for discovering
driver pathways or modules in cancer with the focus on
mutual exclusivity-based ones.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2018:CDB,
author = "Bin Wang and Xuedong Zheng and Shihua Zhou and
Changjun Zhou and Xiaopeng Wei and Qiang Zhang and Ziqi
Wei",
title = "Constructing {DNA} Barcode Sets Based on Particle
Swarm Optimization",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "999--1002",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2679004",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Following the completion of the human genome project,
a large amount of high-throughput bio-data was
generated. To analyze these data, massively parallel
sequencing, namely next-generation sequencing, was
rapidly developed. DNA barcodes are used to identify
the ownership between sequences and samples when they
are attached at the beginning or end of sequencing
reads. Constructing DNA barcode sets provides the
candidate DNA barcodes for this application. To
increase the accuracy of DNA barcode sets, a particle
swarm optimization PSO algorithm has been modified and
used to construct the DNA barcode sets in this paper.
Compared with the extant results, some lower bounds of
DNA barcode sets are improved. The results show that
the proposed algorithm is effective in constructing DNA
barcode sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Poirier:2018:DAB,
author = "Carl Poirier and Benoit Gosselin and Paul Fortier",
title = "{DNA} Assembly with {de Bruijn} Graphs Using an {FPGA}
Platform",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "1003--1009",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2696522",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pvm.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper presents an FPGA implementation of a DNA
assembly algorithm, called Ray, initially developed to
run on parallel CPUs. The OpenCL language is used and
the focus is placed on modifying and optimizing the
original algorithm to better suit the new
parallelization tool and the radically different
hardware architecture. The results show that the
execution time is roughly one fourth that of the CPU
and factoring energy consumption yields a tenfold
savings.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Davidson:2018:EQR,
author = "Ruth Davidson and MaLyn Lawhorn and Joseph Rusinko and
Noah Weber",
title = "Efficient Quartet Representations of Trees and
Applications to Supertree and Summary Methods",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "1010--1015",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2638911",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Quartet trees displayed by larger phylogenetic trees
have long been used as inputs for species tree and
supertree reconstruction. Computational constraints
prevent the use of all displayed quartets in many
practical problems with large numbers of taxa. We
introduce the notion of an Efficient Quartet System EQS
to represent a phylogenetic tree with a subset of the
quartets displayed by the tree. We show mathematically
that the set of quartets obtained from a tree via an
EQS contains all of the combinatorial information of
the tree itself. Using performance tests on simulated
datasets, we also demonstrate that using an EQS to
reduce the number of quartets in both summary method
pipelines for species tree inference as well as methods
for supertree inference results in only small
reductions in accuracy.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liang:2018:OGS,
author = "Xianpeng Liang and Lin Zhu and De-Shuang Huang",
title = "Optimization of Gene Set Annotations Using Robust
Trace-Norm Multitask Learning",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "1016--1021",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2690427",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gene set enrichment GSE is a useful tool for analyzing
and interpreting large molecular datasets generated by
modern biomedical science. The accuracy and
reproducibility of GSE analysis are heavily affected by
the quality and integrity of gene sets annotations. In
this paper, we propose a novel method, robust
trace-norm multitask learning, to solve the
optimization problem of gene set annotations. Inspired
by the binary nature of annotations, we convert the
optimization of gene set annotations into a weakly
supervised classification problem and use
discriminative logistic regression to fit these
datasets. Then, the output of logistic regression can
be used to measure the probability of the existence of
annotations. In addition, the optimization of each row
of the annotation matrix can be treated as an
independent weakly classification task, and we use the
multitask learning approach with trace-norm
regularization to optimize all rows of annotation
matrix simultaneously. Finally, the experiments on
simulated and real data demonstrate the effectiveness
and good performance of the proposed method.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2018:PTB,
author = "Chengyu Liu and Rainer Lehtonen and Sampsa
Hautaniemi",
title = "{PerPAS}: Topology-Based Single Sample Pathway
Analysis Method",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "1022--1027",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2679745",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Identification of intracellular pathways that play key
roles in cancer progression and drug resistance is a
prerequisite for developing targeted cancer treatments.
The era of personalized medicine calls for
computational methods that can function with one sample
or a very small set of samples. Developing such methods
is challenging because standard statistical approaches
pose several limiting assumptions, such as number of
samples, that prevent their application when $n$
approaches to one. We have developed a novel pathway
analysis method called PerPAS to estimate pathway
activity at a single sample level by integrating
pathway topology and transcriptomics data. In addition,
PerPAS is able to identify altered pathways between
cancer and control samples as well as to identify key
nodes that contribute to the pathway activity. In our
case study using breast cancer data, we show that
PerPAS can identify highly altered pathways that are
associated with patient survival. PerPAS identified
four pathways that were associated with patient
survival and were successfully validated in three
independent breast cancer cohorts. In comparison to two
other pathway analysis methods that function at a
single sample level, PerPAS had superior performance in
both synthetic and breast cancer expression datasets.
PerPAS is a free R package
http://csbi.ltdk.helsinki.fi/pub/czliu/perpas/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Torshizi:2018:SPI,
author = "Abolfazl Doostparast Torshizi and Linda Petzold",
title = "Sparse Pathway-Induced Dynamic Network Biomarker
Discovery for Early Warning Signal Detection in Complex
Diseases",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "1028--1034",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2687925",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In many complex diseases, the transition process from
the healthy stage to the catastrophic stage does not
occur gradually. Recent studies indicate that the
initiation and progression of such diseases are
comprised of three steps including healthy stage,
pre-disease stage, and disease stage. It has been
demonstrated that a certain set of trajectories can be
observed in the genetic signatures at the molecular
level, which might be used to detect the pre-disease
stage and to take necessary medical interventions. In
this paper, we propose two optimization-based
algorithms for extracting the dynamic network
biomarkers responsible for catastrophic transition into
the disease stage, and to open new horizons to reverse
the disease progression at an early stage through
pinpointing molecular signatures provided by
high-throughput microarray data. The first algorithm
relies on meta-heuristic intelligent search to
characterize dynamic network biomarkers represented as
a complete graph. The second algorithm induces sparsity
on the adjacency matrix of the genes by taking into
account the biological signaling and metabolic
pathways, since not all the genes in the ineractome are
biologically linked. Comprehensive numerical and
meta-analytical experiments verify the effectiveness of
the results of the proposed approaches in terms of
network size, biological meaningfulness, and
verifiability.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2018:CDE,
author = "Wei Zhang and Jia Xu and Yuanyuan Li and Xiufen Zou",
title = "Correction to {``Detecting Essential Proteins Based on
Network Topology, Gene Expression Data, and Gene
Ontology Information''}",
journal = j-TCBB,
volume = "15",
number = "3",
pages = "1035--1035",
month = may,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2813918",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Sat Jun 30 09:34:37 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
note = "See \cite{Zhang:2018:DEP}.",
abstract = "Presents corrections to author information for the
paper, W. Zhang, J. Xu, Y. Li, and X. Zou, ``Detecting
essential proteins based on network topology, gene
expression data, and gene ontology information,'',
IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 15, no. 1,
pp. 109--116, Jan./Feb. 2018.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Murali:2018:GE,
author = "T. M. Murali",
title = "Guest Editorial",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1036--1036",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2856658",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ghoshal:2018:DCM,
author = "Asish Ghoshal and Jinyi Zhang and Michael A. Roth and
Kevin Muyuan Xia and Ananth Y. Grama and Somali
Chaterji",
title = "A Distributed Classifier for {MicroRNA} Target
Prediction with Validation Through {TCGA} Expression
Data",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1037--1051",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2828305",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Background: MicroRNAs miRNAs are approximately
22-nucleotide long regulatory RNA that mediate RNA
interference by binding to cognate mRNA target regions.
Here, we present a distributed kernel SVM-based binary
classification scheme to predict miRNA targets. It
captures the spatial profile of miRNA-mRNA interactions
via smooth B-spline curves. This is accomplished
separately for various input features, such as
thermodynamic and sequence-based features. Further, we
use a principled approach to uniformly model both
canonical and non-canonical seed matches, using a novel
seed enrichment metric. Finally, we verify our
miRNA-mRNA pairings using an Elastic Net-based
regression model on TCGA expression data for four
cancer types to estimate the miRNAs that together
regulate any given mRNA. Results: We present a suite of
algorithms for miRNA target prediction, under the
banner Avishkar, with superior prediction performance
over the competition. Specifically, our final kernel
SVM model, with an Apache Spark backend, achieves an
average true positive rate TPR of more than 75 percent,
when keeping the false positive rate of 20 percent, for
non-canonical human miRNA target sites. This is an
improvement of over 150 percent in the TPR for
non-canonical sites, over the best-in-class algorithm.
We are able to achieve such superior performance by
representing the thermodynamic and sequence profiles of
miRNA-mRNA interaction as curves, devising a novel seed
enrichment metric, and learning an ensemble of miRNA
family-specific kernel SVM classifiers. We provide an
easy-to-use system for large-scale interactive analysis
and prediction of miRNA targets. All operations in our
system, namely candidate set generation, feature
generation and transformation, training, prediction,
and computing performance metrics are fully distributed
and are scalable. Conclusions: We have developed an
efficient SVM-based model for miRNA target prediction
using recent CLIP-seq data, demonstrating superior
performance, evaluated using ROC curves for different
species human or mouse, or different target types
canonical or non-canonical. We analyzed the agreement
between the target pairings using CLIP-seq data and
using expression data from four cancer types. To the
best of our knowledge, we provide the first distributed
framework for miRNA target prediction based on Apache
Hadoop and Spark. Availability: All source code and
sample data are publicly available at
https://bitbucket.org/cellsandmachines/avishkar. Our
scalable implementation of kernel SVM using Apache
Spark, which can be used to solve large-scale
non-linear binary classification problems, is available
at
https://bitbucket.org/cellsandmachines/kernelsvmspark.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Qiao:2018:QDA,
author = "Shi Qiao and Mehmet Koyut{\"u}rk and Meral Z.
{\"O}zsoyo{\u{g}}lu",
title = "Querying of Disparate Association and Interaction Data
in Biomedical Applications",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1052--1065",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2637344",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In biomedical applications, network models are
commonly used to represent interactions and
higher-level associations among biological entities.
Integrated analyses of these interaction and
association data has proven useful in extracting
knowledge, and generating novel hypotheses for
biomedical research. However, since most datasets
provide their own schema and query interface,
opportunities for exploratory and integrative querying
of disparate data are currently limited. In this study,
we utilize RDF-based representations of biomedical
interaction and association data to develop a querying
framework that enables flexible specification and
efficient processing of graph template matching
queries. The proposed framework enables integrative
querying of biomedical databases to discover complex
patterns of associations among a diverse range of
biological entities, including biomolecules, biological
processes, organisms, and phenotypes. Our experimental
results on the UniProt dataset show that the proposed
framework can be used to efficiently process complex
queries, and identify biologically relevant patterns of
associations that cannot be readily obtained by
querying each dataset independently.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gossmann:2018:SRM,
author = "Alexej Gossmann and Shaolong Cao and Damian Brzyski
and Lan-Juan Zhao and Hong-Wen Deng and Yu-Ping Wang",
title = "A Sparse Regression Method for Group-Wise Feature
Selection with False Discovery Rate Control",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1066--1078",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2780106",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/s-plus.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The method of Sorted L-One Penalized Estimation, or
SLOPE, is a sparse regression method recently
introduced by Bogdan et. al. [1] . It can be used to
identify significant predictor variables in a linear
model that may have more unknown parameters than
observations. When the correlations between predictor
variables are small, the SLOPE method is shown to
successfully control the false discovery rate the
expected proportion of the irrelevant among all
selected predictors at a user specified level. However,
the requirement for nearly uncorrelated predictors is
too restrictive for genomic data, as demonstrated in
our recent study [2] by an application of SLOPE to
realistic simulated DNA sequence data. A possible
solution is to divide the predictor variables into
nearly uncorrelated groups, and to modify the procedure
to select entire groups with an overall significant
group effect, rather than individual predictors.
Following this motivation, we extend SLOPE in the
spirit of Group LASSO to Group SLOPE, a method that can
handle group structures between the predictor
variables, which are ubiquitous in real genomic data.
Our theoretical results show that Group SLOPE controls
the group-wise false discovery rate gFDR, when groups
are orthogonal to each other. For use in non-orthogonal
settings, we propose two types of Monte Carlo based
heuristics, which lead to gFDR control with Group SLOPE
in simulations based on real SNP data. As an
illustration of the merits of this method, an
application of Group SLOPE to a dataset from the
Framingham Heart Study results in the identification of
some known DNA sequence regions associated with bone
health, as well as some new candidate regions. The
novel methods are implemented in the R package
grpSLOPEMC, which is publicly available at
https://github.com/agisga/grpSLOPEMC.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Alim:2018:CSP,
author = "Md Abdul Alim and Ahmet Ay and Md Mahmudul Hasan and
My T. Thai and Tamer Kahveci",
title = "Construction of Signaling Pathways with {RNAi} Data
and Multiple Reference Networks",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1079--1091",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2710129",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Signaling networks are involved in almost all major
diseases such as cancer. As a result of this,
understanding how signaling networks function is vital
for finding new treatments for many diseases. Using
gene knockdown assays such as RNA interference RNAi
technology, many genes involved in these networks can
be identified. However, determining the interactions
between these genes in the signaling networks using
only experimental techniques is very challenging, as
performing extensive experiments is very expensive and
sometimes, even impractical. Construction of signaling
networks from RNAi data using computational techniques
have been proposed as an alternative way to solve this
challenging problem. However, the earlier approaches
are either not scalable to large scale networks, or
their accuracy levels are not satisfactory. In this
study, we integrate RNAi data given on a target network
with multiple reference signaling networks and
phylogenetic trees to construct the topology of the
target signaling network. In our work, the network
construction is considered as finding the minimum
number of edit operations on given multiple reference
networks, in which their contributions are weighted by
their phylogenetic distances to the target network. The
edit operations on the reference networks lead to a
target network that satisfies the RNAi knockdown
observations. Here, we propose two new reference-based
signaling network construction methods that provide
optimal results and scale well to large-scale signaling
networks of hundreds of components. We compare the
performance of these approaches to the state-of-the-art
reference-based network construction method SiNeC on
synthetic, semi-synthetic, and real datasets. Our
analyses show that the proposed methods outperform
SiNeC method in terms of accuracy. Furthermore, we show
that our methods function well even if evolutionarily
distant reference networks are used. Application of our
methods to the Apoptosis and Wnt signaling pathways
recovers the known protein-protein interactions and
suggests additional relevant interactions that can be
tested experimentally.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fan:2018:GES,
author = "Xiaodan Fan and Xinglai Ji and Rui Jiang",
title = "Guest Editorial for Special Section on the {Sixth
National Conference on Bioinformatics and System
Biology of China}",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1092--1092",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2838498",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zamanighomi:2018:GRN,
author = "Mahdi Zamanighomi and Mostafa Zamanian and Michael
Kimber and Zhengdao Wang",
title = "Gene Regulatory Network Inference from Perturbed
Time-Series Expression Data via Ordered Dynamical
Expansion of Non-Steady State Actors",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1093--1106",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2509992",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The reconstruction of gene regulatory networks from
gene expression data has been the subject of intense
research activity. A variety of models and methods have
been developed to address different aspects of this
important problem. However, these techniques are
narrowly focused on particular biological and
experimental platforms, and require experimental data
that are typically unavailable and difficult to
ascertain. The more recent availability of
higher-throughput sequencing platforms, combined with
more precise modes of genetic perturbation, presents an
opportunity to formulate more robust and comprehensive
approaches to gene network inference. Here, we propose
a step-wise framework for identifying gene-gene
regulatory interactions that expand from a known point
of genetic or chemical perturbation using time series
gene expression data. This novel approach sequentially
identifies non-steady state genes post-perturbation and
incorporates them into a growing series of
low-complexity optimization problems. The governing
ordinary differential equations of this model are
rooted in the biophysics of stochastic molecular events
that underlie gene regulation, delineating roles for
both protein and RNA-mediated gene regulation. We show
the successful application of our core algorithms for
network inference using simulated and real datasets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2018:PWT,
author = "Ke Liu and Sha Hou and Junbiao Dai and Zhirong Sun",
title = "{PyMut}: a {Web} Tool for Overlapping Gene
Loss-of-Function Mutation Design",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1107--1110",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2505290",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Loss-of-function study is an effective approach to
research gene functions. However, currently most of
such studies have ignored an important problem in this
paper, we call it ``off-target'' problem, that is, if
the target gene is an overlapping gene A gene whose
expressible nucleotides overlaps with that of another
one, loss-of-function mutation by deleting the complete
open reading frame ORF may also cause the gene it
overlaps lose function, resulting a phenotype which may
be rather different from that of single gene deletion.
Therefore, when doing such studies, the
loss-of-function mutations should be carefully designed
to guarantee only the function of the target gene will
be abolished. In this paper, we present PyMut, an
easy-to-use web tool for biologists to design such
mutations. To the best of our knowledge, PyMut is the
first tool that aims to solve the ``off-target''
problem regarding the overlapping genes. Our web server
is freely available at
http://www.bioinfo.tsinghua.edu.cn/~liuke/PyMut/index.html.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cheng:2018:NPI,
author = "Xiangfei Cheng and Yue Hou and Yumin Nie and Yiru
Zhang and Huan Huang and Hongde Liu and Xiao Sun",
title = "Nucleosome Positioning of Intronless Genes in the
Human Genome",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1111--1121",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2476811",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Nucleosomes, the basic units of chromatin, are
involved in transcription regulation and DNA
replication. Intronless genes, which constitute 3
percent of the human genome, differ from
intron-containing genes in evolution and function. Our
analysis reveals that nucleosome positioning shows a
distinct pattern in intronless and intron-containing
genes. The nucleosome occupancy upstream of
transcription start sites of intronless genes is lower
than that of intron-containing genes. In contrast, high
occupancy and well positioned nucleosomes are observed
along the gene body of intronless genes, which is
perfectly consistent with the barrier nucleosome model.
Intronless genes have a significantly lower expression
level than intron-containing genes and most of them are
not expressed in CD4+ T cell lines and GM12878 cell
lines, which results from their tissue specificity.
However, the highly expressed genes are at the same
expression level between the two types of genes. The
highly expressed intronless genes require a higher
density of RNA Pol II in an elongating state to
compensate for the lack of introns. Additionally, 5'
and 3' nucleosome depleted regions of highly expressed
intronless genes are deeper than those of highly
expressed intron-containing genes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bartocci:2018:GEI,
author = "Ezio Bartocci and Pietro Lio and Nicola Paoletti",
title = "Guest Editors' Introduction to the Special Section on
the {14th International Conference on Computational
Methods in Systems Biology CMSB 2016}",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1122--1123",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2816979",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Feret:2018:LTA,
author = "Jerome Feret and Kim Quyen Ly",
title = "Local Traces: an Over-Approximation of the Behavior of
the Proteins in Rule-Based Models",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1124--1137",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2812195",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Thanks to rule-based modelling languages, we can
assemble large sets of mechanistic protein-protein
interactions within integrated models. Our goal would
be to understand how the behavior of these systems
emerges from these low-level interactions. Yet, this is
a quite long term challenge and it is desirable to
offer intermediary levels of abstraction, so as to get
a better understanding of the models and to increase
our confidence within our mechanistic assumptions. To
this extend, static analysis can be used to derive
various abstractions of the semantics, each of them
offering new perspectives on the models. We propose an
abstract interpretation of the behavior of each
protein, in isolation. Given a model written in Kappa,
this abstraction computes for each kind of proteins a
transition system that describes which conformations
this protein may take and how a protein may pass from
one conformation to another one. Then, we use
simplicial complexes to abstract away the interleaving
order of the transformations between conformations that
commute. As a result, we get a compact summary of the
potential behavior of each protein of the model.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fages:2018:INC,
author = "Fran{\c{c}}ois Fages and Thierry Martinez and David A.
Rosenblueth and Sylvain Soliman",
title = "Influence Networks Compared with Reaction Networks:
Semantics, Expressivity and Attractors",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1138--1151",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2805686",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Biochemical reaction networks are one of the most
widely used formalisms in systems biology to describe
the molecular mechanisms of high-level cell processes.
However, modellers also reason with influence diagrams
to represent the positive and negative influences
between molecular species and may find an influence
network useful in the process of building a reaction
network. In this paper, we introduce a formalism of
influence networks with forces, and equip it with a
hierarchy of Boolean, Petri net, stochastic and
differential semantics, similarly to reaction networks
with rates. We show that the expressive power of
influence networks is the same as that of reaction
networks under the differential semantics, but weaker
under the discrete semantics. Furthermore, the
hierarchy of semantics leads us to consider a positive
Boolean semantics that cannot test the absence of a
species, that we compare with the negative Boolean
semantics with test for absence of a species in gene
regulatory networks {\`a} la Thomas. We study the
monotonicity properties of the positive semantics and
derive from them an algorithm to compute attractors in
both the positive and negative Boolean semantics. We
illustrate our results on models of the literature
about the p53/Mdm2 DNA damage repair system, the
circadian clock, and the influence of MAPK signaling on
cell-fate decision in urinary bladder cancer.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mu:2018:OOC,
author = "Chunyan Mu and Peter Dittrich and David Parker and
Jonathan E. Rowe",
title = "Organisation-Oriented Coarse Graining and Refinement
of Stochastic Reaction Networks",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1152--1166",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2804395",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Chemical organisation theory is a framework developed
to simplify the analysis of long-term behaviour of
chemical systems. In this work, we build on these ideas
to develop novel techniques for formal quantitative
analysis of chemical reaction networks, using discrete
stochastic models represented as continuous-time Markov
chains. We propose methods to identify organisations,
and to study quantitative properties regarding
movements between these organisations. We then
construct and formalise a coarse-grained Markov chain
model of hierarchic organisations for a given reaction
network, which can be used to approximate the behaviour
of the original reaction network. As an application of
the coarse-grained model, we predict the behaviour of
the reaction network systems over time via the master
equation. Experiments show that our predictions can
mimic the main pattern of the concrete behaviour in the
long run, but the precision varies for different models
and reaction rule rates. Finally, we propose an
algorithm to selectively refine the coarse-grained
models and show experiments demonstrating that the
precision of the prediction has been improved.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pauleve:2018:RQM,
author = "Loic Pauleve",
title = "Reduction of Qualitative Models of Biological Networks
for Transient Dynamics Analysis",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1167--1179",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2749225",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Qualitative models of dynamics of signalling pathways
and gene regulatory networks allow for the capturing of
temporal properties of biological networks while
requiring few parameters. However, these discrete
models typically suffer from the so-called state space
explosion problem which makes the formal assessment of
their potential behaviors very challenging. In this
paper, we describe a method to reduce a qualitative
model for enhancing the tractability of analysis of
transient reachability properties. The reduction does
not change the dimension of the model, but instead
limits its degree of freedom, therefore reducing the
set of states and transitions to consider. We rely on a
transition-centered specification of qualitative models
by the mean of automata networks. Our framework
encompasses the usual asynchronous Boolean and
multi-valued network, as well as 1-bounded Petri nets.
Applied to different large-scale biological networks
from the literature, we show that the reduction can
lead to a drastic improvement for the scalability of
verification methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Backenkohler:2018:MBP,
author = "Michael Backenkohler and Luca Bortolussi and Verena
Wolf",
title = "Moment-Based Parameter Estimation for Stochastic
Reaction Networks in Equilibrium",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1180--1192",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2775219",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Calibrating parameters is a crucial problem within
quantitative modeling approaches to reaction networks.
Existing methods for stochastic models rely either on
statistical sampling or can only be applied to small
systems. Here, we present an inference procedure for
stochastic models in equilibrium that is based on a
moment matching scheme with optimal weighting and that
can be used with high-throughput data like the one
collected by flow cytometry. Our method does not
require an approximation of the underlying equilibrium
probability distribution and, if reaction rate
constants have to be learned, the optimal values can be
computed by solving a linear system of equations. We
discuss important practical issues such as the
selection of the moments and evaluate the effectiveness
of the proposed approach on three case studies.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Villaverde:2018:PTI,
author = "Alejandro F. Villaverde and Kolja Becker and Julio R.
Banga",
title = "{PREMER}: a Tool to Infer Biological Networks",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1193--1202",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2758786",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/fortran3.bib;
https://www.math.utah.edu/pub/tex/bib/matlab.bib;
https://www.math.utah.edu/pub/tex/bib/pvm.bib;
https://www.math.utah.edu/pub/tex/bib/python.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Inferring the structure of unknown cellular networks
is a main challenge in computational biology.
Data-driven approaches based on information theory can
determine the existence of interactions among network
nodes automatically. However, the elucidation of
certain features-such as distinguishing between direct
and indirect interactions or determining the direction
of a causal link-requires estimating
information-theoretic quantities in a multidimensional
space. This can be a computationally demanding task,
which acts as a bottleneck for the application of
elaborate algorithms to large-scale network inference
problems. The computational cost of such calculations
can be alleviated by the use of compiled programs and
parallelization. To this end, we have developed PREMER
Parallel Reverse Engineering with Mutual information \&
Entropy Reduction, a software toolbox that can run in
parallel and sequential environments. It uses
information theoretic criteria to recover network
topology and determine the strength and causality of
interactions, and allows incorporating prior knowledge,
imputing missing data, and correcting outliers. PREMER
is a free, open source software tool that does not
require any commercial software. Its core algorithms
are programmed in FORTRAN 90 and implement OpenMP
directives. It has user interfaces in Python and
MATLAB/Octave, and runs on Windows, Linux, and OSX
https://sites.google.com/site/premertoolbox/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mizera:2018:APT,
author = "Andrzej Mizera and Jun Pang and Cui Su and Qixia
Yuan",
title = "{{\sf ASSA-PBN}}: a Toolbox for Probabilistic
{Boolean} Networks",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1203--1216",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2773477",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "As a well-established computational framework,
probabilistic Boolean networks PBNs are widely used for
modelling, simulation, and analysis of biological
systems. To analyze the steady-state dynamics of PBNs
is of crucial importance to explore the characteristics
of biological systems. However, the analysis of large
PBNs, which often arise in systems biology, is prone to
the infamous state-space explosion problem. Therefore,
the employment of statistical methods often remains the
only feasible solution. We present $ \mathsf {ASSA -
PBN} $, a software toolbox for modelling, simulation,
and analysis of PBNs. $ \mathsf {ASSA - PBN} $ provides
efficient statistical methods with three parallel
techniques to speed up the computation of steady-state
probabilities. Moreover, particle swarm optimisation
PSO and differential evolution DE are implemented for
the estimation of PBN parameters. Additionally, we
implement in-depth analyses of PBNs, including long-run
influence analysis, long-run sensitivity analysis,
computation of one-parameter profile likelihoods, and
the visualization of one-parameter profile likelihoods.
A PBN model of apoptosis is used as a case study to
illustrate the main functionalities of $ \mathsf {ASSA
- PBN} $ and to demonstrate the capabilities of $
\mathsf {ASSA - PBN} $ to effectively analyse
biological systems modelled as PBNs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Czeizler:2018:STC,
author = "Eugen Czeizler and Kai-Chiu Wu and Cristian Gratie and
Krishna Kanhaiya and Ion Petre",
title = "Structural Target Controllability of Linear Networks",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1217--1228",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2797271",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Computational analysis of the structure of
intra-cellular molecular interaction networks can
suggest novel therapeutic approaches for systemic
diseases like cancer. Recent research in the area of
network science has shown that network control theory
can be a powerful tool in the understanding and
manipulation of such bio-medical networks. In 2011, Liu
et al. developed a polynomial time algorithm computing
the size of the minimal set of nodes controlling a
linear network. In 2014, Gao et al. generalized the
problem for target control, minimizing the set of nodes
controlling a target within a linear network. The
authors developed a Greedy approximation algorithm
while leaving open the complexity of the optimization
problem. We prove here that the target controllability
problem is NP-hard in all practical setups, i.e., when
the control power of any individual input is bounded by
some constant. We also show that the algorithm provided
by Gao et al. fails to provide a valid solution in some
special cases, and an additional validation step is
required. We fix and improve their algorithm using
several heuristics, obtaining in the end an up to
10-fold decrease in running time and also a decrease in
the size of solutions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Milenkovic:2018:GLB,
author = "Tijana Milenkovic and Sarath Chandra Janga",
title = "{Great Lakes Bioinformatics Conference GLBIO 2015}
Special Section Editorial",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1229--1230",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2849800",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Presents information on the Great Lakes Bioinformatics
Conference GLBIO.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pease:2018:EDU,
author = "James B. Pease and Benjamin K. Rosenzweig",
title = "Encoding Data Using Biological Principles: The
Multisample Variant Format for Phylogenomics and
Population Genomics",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1231--1238",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2509997",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Rapid progress in the fields of phylogenomics and
population genomics has driven increases in both the
size of multi-genomic datasets and the number and
complexity of genome-wide analyses. We present the
Multisample Variant Format, specifically designed to
store multiple sequence alignments for phylogenomics
and population genomic analysis. The signature feature
of MVF is a distinctive encoding of aligned sites with
specific biological information content e.g.,
invariant, low-coverage. This biological pattern-based
encoding of sequence data allows for rapid filtering
and quality control of data and speeds up computation
for many analyses. Similar to other modern formats, MVF
has a simple data structure and flexible header
structure to accommodate project metadata, allowing to
also serve as an effective data publication and sharing
format. We also propose several variants of the MVF
format to accommodate protein and codon alignments,
quality scores, and a mix of de novo and
reference-aligned data. Using the MVFtools package, MVF
files can be converted from other common sequence
formats. MVFtools completes tasks ranging from simple
transformation and filtering operations to complex
genome-wide visualizations in only a few minutes, even
on large datasets. In addition to presentation of MVF
and MVFtools, we also discuss the application both in
MVF and other existing data formats of the broader
concept of using biological principles and patterns to
inform sequence data encoding.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Roth:2018:TES,
author = "Adam Roth and Sandeep Subramanian and Madhavi K.
Ganapathiraju",
title = "Towards Extracting Supporting Information About
Predicted Protein-Protein Interactions",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1239--1246",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2505278",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "One of the goals of relation extraction is to identify
protein-protein interactions PPIs in biomedical
literature. Current systems are capturing binary
relations and also the direction and type of an
interaction. Besides assisting in the curation PPIs
into databases, there has been little real-world
application of these algorithms. We describe UPSITE, a
text mining tool for extracting evidence in support of
a hypothesized interaction. Given a predicted PPI,
UPSITE uses a binary relation detector to check whether
a PPI is found in abstracts in PubMed. If it is not
found, UPSITE retrieves documents relevant to each of
the two proteins separately, and extracts contextual
information about biological events surrounding each
protein, and calculates semantic similarity of the two
proteins to provide evidential support for the
predicted PPI. In evaluations, relation extraction
achieved an Fscore of 0.88 on the HPRD50 corpus, and
semantic similarity measured with angular distance was
found to be statistically significant. With the
development of PPI prediction algorithms, the burden of
interpreting the validity and relevance of novel PPIs
is on biologists. We suggest that presenting
annotations of the two proteins in a PPI side-by-side
and a score that quantifies their similarity lessens
this burden to some extent.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yerneni:2018:IIS,
author = "Satwica Yerneni and Ishita K. Khan and Qing Wei and
Daisuke Kihara",
title = "{IAS}: Interaction Specific {GO} Term Associations for
Predicting Protein-Protein Interaction Networks",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1247--1258",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2476809",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Proteins carry out their function in a cell through
interactions with other proteins. A large scale
protein-protein interaction PPI network of an organism
provides static yet an essential structure of
interactions, which is valuable clue for understanding
the functions of proteins and pathways. PPIs are
determined primarily by experimental methods; however,
computational PPI prediction methods can supplement or
verify PPIs identified by experiment. Here, we
developed a novel scoring method for predicting PPIs
from Gene Ontology GO annotations of proteins. Unlike
existing methods that consider functional similarity as
an indication of interaction between proteins, the new
score, named the protein-protein Interaction
Association Score IAS, was computed from GO term
associations of known interacting protein pairs in 49
organisms. IAS was evaluated on PPI data of six
organisms and found to outperform existing GO
term-based scoring methods. Moreover, consensus scoring
methods that combine different scores further improved
performance of PPI prediction.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hashemikhabir:2018:FIG,
author = "Seyedsasan Hashemikhabir and Ran Xia and Yang Xiang
and Sarath Chandra Janga",
title = "A Framework for Identifying Genotypic Information from
Clinical Records: Exploiting Integrated Ontology
Structures to Transfer Annotations between {ICD} Codes
and Gene Ontologies",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1259--1269",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2480056",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Although some methods are proposed for automatic
ontology generation, none of them address the issue of
integrating large-scale heterogeneous biomedical
ontologies. We propose a novel approach for integrating
various types of ontologies efficiently and apply it to
integrate International Classification of Diseases,
Ninth Revision, Clinical Modification ICD9CM, and Gene
Ontologies. This approach is one of the early attempts
to quantify the associations among clinical terms e.g.,
ICD9 codes based on their corresponding genomic
relationships. We reconstructed a merged tree for a
partial set of GO and ICD9 codes and measured the
performance of this tree in terms of associations'
relevance by comparing them with two well-known
disease-gene datasets i.e., MalaCards and Disease
Ontology. Furthermore, we compared the genomic-based
ICD9 associations to temporal relationships between
them from electronic health records. Our analysis shows
promising associations supported by both comparisons
suggesting a high reliability. We also manually
analyzed several significant associations and found
promising support from literature.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Patra:2018:RAP,
author = "Pranjal Patra and Tatsuo Izawa and Lourdes
Pena-Castillo",
title = "{REPA}: Applying Pathway Analysis to Genome-Wide
Transcription Factor Binding Data",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1270--1283",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2453948",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Pathway analysis has been extensively applied to aid
in the interpretation of the results of genome-wide
transcription profiling studies, and has been shown to
successfully find associations between the biological
phenomena under study and biological pathways. There
are two widely used approaches of pathway analysis:
over-representation analysis, and gene set analysis.
Recently genome-wide transcription factor binding data
has become widely available allowing for the
application of pathway analysis to this type of data.
In this work, we developed regulatory enrichment
pathway analysis REPA to apply gene set analysis to
genome-wide transcription factor binding data to infer
associations between transcription factors and
biological pathways. We used the transcription factor
binding data generated by the ENCODE project, and gene
sets from the Molecular Signatures and KEGG databases.
Our results showed that 54 percent of the predictions
examined have literature support and that REPA's recall
is roughly 54 percent. This level of precision is
promising as several of REPA's predictions are expected
to be novel and can be used to guide new research
avenues. In addition, the results of our case studies
showed that REPA enhances the interpretation of
genome-wide transcription profiling studies by
suggesting putative regulators behind the observed
transcriptional responses.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ramalho:2018:PEF,
author = "Rodrigo F. Ramalho and Sujun Li and Predrag Radivojac
and Matthew W. Hahn",
title = "Proteomic Evidence for In-Frame and Out-of-Frame
Alternatively Spliced Isoforms in Human and Mouse",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1284--1289",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2480068",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In order to find evidence for translation of
alternatively spliced transcripts, especially those
that result in a change in reading frame, we collected
exon-skipping cases previously found by RNA-Seq and
applied a computational approach to screen millions of
mass spectra. These spectra came from seven human and
six mouse tissues, five of which are the same between
the two organisms: liver, kidney, lung, heart, and
brain. Overall, we detected 4 percent of all
exon-skipping events found in RNA-seq data, regardless
of their effect on reading frame. The fraction of
alternative isoforms detected did not differ between
out-of-frame and in-frame events. Moreover, the
fraction of identified alternative exon-exon junctions
and constitutive junctions were similar. Together, our
results suggest that both in-frame and out-of-frame
translation may be actively used to regulate protein
activity or localization.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Al-Ouran:2018:DGR,
author = "Rami Al-Ouran and Robert Schmidt and Ashwini Naik and
Jeffrey Jones and Frank Drews and David Juedes and
Laura Elnitski and Lonnie Welch",
title = "Discovering Gene Regulatory Elements Using
Coverage-Based Heuristics",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1290--1300",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2496261",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Data mining algorithms and sequencing methods such as
RNA-seq and ChIP-seq are being combined to discover
genomic regulatory motifs that relate to a variety of
phenotypes. However, motif discovery algorithms often
produce very long lists of putative transcription
factor binding sites, hindering the discovery of
phenotype-related regulatory elements by making it
difficult to select a manageable set of candidate
motifs for experimental validation. To address this
issue, the authors introduce the motif selection
problem and provide coverage-based search heuristics
for its solution. Analysis of 203 ChIP-seq experiments
from the ENCyclopedia of DNA Elements project shows
that our algorithms produce motifs that have high
sensitivity and specificity and reveals new insights
about the regulatory code of the human genome. The
greedy algorithm performs the best, selecting a median
of two motifs per ChIP-seq transcription factor group
while achieving a median sensitivity of 77 percent.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ironi:2018:MBT,
author = "Liliana Ironi and Ettore Lanzarone",
title = "A Model-Based Tool for the Analysis and Design of Gene
Regulatory Networks",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1301--1314",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2716942",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Computational and mathematical models have
significantly contributed to the rapid progress in the
study of gene regulatory networks GRN, but researchers
still lack a reliable model-based framework for
computer-aided analysis and design. Such tool should
both reveal the relation between network structure and
dynamics and find parameter values and/or constraints
that enable the simulated dynamics to reproduce
specific behaviors. This paper addresses these issues
and proposes a computational framework that facilitates
network analysis or design. It follows a modeling cycle
that alternates phases of hypothesis testing with
parameter space refinement to ultimately propose a
network that exhibits specified behaviors with the
highest probability. Hypothesis testing is performed
via qualitative simulation of GRNs modeled by a class
of nonlinear and temporal multiscale ODEs, where
regulation functions are expressed by steep sigmoid
functions and incompletely known parameter values by
order relations only. Parameter space refinement,
grounded on a method that considers the intrinsic
stochasticity of regulation by expressing network
uncertainty with fluctuations in parameter values only,
optimizes parameter stochastic values initialized by
probability distributions with large variances. The
power and ease of our framework is demonstrated by
working out a benchmark synthetic network to get a
synthetic oscillator.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xia:2018:STS,
author = "Chun-Qiu Xia and Ke Han and Yong Qi and Yang Zhang and
Dong-Jun Yu",
title = "A Self-Training Subspace Clustering Algorithm under
Low-Rank Representation for Cancer Classification on
Gene Expression Data",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1315--1324",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2712607",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Accurate identification of the cancer types is
essential to cancer diagnoses and treatments. Since
cancer tissue and normal tissue have different gene
expression, gene expression data can be used as an
efficient feature source for cancer classification.
However, accurate cancer classification directly using
original gene expression profiles remains challenging
due to the intrinsic high-dimension feature and the
small size of the data samples. We proposed a new
self-training subspace clustering algorithm under
low-rank representation, called SSC-LRR, for cancer
classification on gene expression data. Low-rank
representation LRR is first applied to extract
discriminative features from the high-dimensional gene
expression data; the self-training subspace clustering
SSC method is then used to generate the cancer
classification predictions. The SSC-LRR was tested on
two separate benchmark datasets in control with four
state-of-the-art classification methods. It generated
cancer classification predictions with an overall
accuracy 89.7 percent and a general correlation 0.920,
which are 18.9 and 24.4 percent higher than that of the
best control method respectively. In addition, several
genes RNF114, HLA-DRB5, USP9Y, and PTPN20 were
identified by SSC-LRR as new cancer identifiers that
deserve further clinical investigation. Overall, the
study demonstrated a new sensitive avenue to recognize
cancer classifications from large-scale gene expression
data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{He:2018:TSB,
author = "Xinyu He and Lishuang Li and Yang Liu and Xiaoming Yu
and Jun Meng",
title = "A Two-Stage Biomedical Event Trigger Detection Method
Integrating Feature Selection and Word Embeddings",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1325--1332",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2715016",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Extracting biomedical events from biomedical
literature plays an important role in the field of
biomedical text mining, and the trigger detection is a
key step in biomedical event extraction. We propose a
two-stage method for trigger detection, which divides
trigger detection into recognition stage and
classification stage, and different features are
selected in each stage. In the first stage, we select
the features which are more suitable for recognition,
and in the second stage, the features that are more
helpful to classification are adopted. Furthermore, we
integrate word embeddings to represent words
semantically and syntactically. On the multi-level
event extraction MLEE corpus test dataset, our method
achieves an F-score of 79.75 percent, which outperforms
the state-of-the-art systems.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Deznabi:2018:IAG,
author = "Iman Deznabi and Mohammad Mobayen and Nazanin Jafari
and Oznur Tastan and Erman Ayday",
title = "An Inference Attack on Genomic Data Using Kinship,
Complex Correlations, and Phenotype Information",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1333--1343",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2709740",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Individuals and their family members share partial
genomic data on public platforms. However, using
special characteristics of genomic data, background
knowledge that can be obtained from the Web, and family
relationship between the individuals, it is possible to
infer the hidden parts of shared and unshared genomes.
Existing work in this field considers simple
correlations in the genome as well as Mendel's law and
partial genomes of a victim and his family members. In
this paper, we improve the existing work on inference
attacks on genomic privacy. We mainly consider complex
correlations in the genome by using an observable
Markov model and recombination model between the
haplotypes. We also utilize the phenotype information
about the victims. We propose an efficient message
passing algorithm to consider all aforementioned
background information for the inference. We show that
the proposed framework improves inference with
significantly less information compared to existing
work.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xie:2018:AGS,
author = "Qian Xie and Xiaoyan He and Fangji Yang and Xuling Liu
and Ying Li and Yujing Liu and ZhengMeng Yang and
Jianhai Yu and Bao Zhang and Wei Zhao",
title = "Analysis of the Genome Sequence and Prediction of
{B}-Cell Epitopes of the Envelope Protein of {Middle
East} Respiratory Syndrome-Coronavirus",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1344--1350",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2702588",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The outbreak of Middle East respiratory
syndrome-coronavirus MERS-CoV in South Korea in April
2015 led to 186 infections and 37 deaths by the end of
October 2015. MERS-CoV was isolated from the imported
patient in China. The envelope E protein, a small
structural protein of MERS-CoV, plays an important role
in host recognition and infection. To identify the
conserved epitopes of the E protein, sequence analysis
was performed by comparing the E proteins from 42
MERS-CoV strains that triggered severe pandemics and
infected humans in the past. To predict the potential B
cell epitopes of E protein, three most effective online
epitope prediction programs, the ABCpred, Bepipred, and
Protean programs from the LaserGene software were used.
All the nucleotides and amino acids sequences were
obtained from the NCBI Database. One potential epitope
with a suitable length amino acids 58-82 was confirmed
and predicted to be highly antigenic. This epitope had
scores of {$ > 0.80 $} in ABCpred and level 0.35 in
Bepipred programs. Due to the lack of X-ray crystal
structure of the E protein in the PDB database, the
simulated 3D structure of the E protein were also
predicted using PHYRE 2 and Pymol programs. In
conclusion, using bioinformatics methods, we analyzed
the genome sequence of MERS-CoV and identified a
potential B-cell epitope of the E protein, which might
significantly improve our current MERS vaccine
development strategies.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zarai:2018:CAC,
author = "Yoram Zarai and Michael Margaliot and Eduardo D.
Sontag and Tamir Tuller",
title = "Controllability Analysis and Control Synthesis for the
Ribosome Flow Model",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1351--1364",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2707420",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The ribosomal density along different parts of the
coding regions of the mRNA molecule affects various
fundamental intracellular phenomena including: protein
production rates, global ribosome allocation and
organismal fitness, ribosomal drop off,
co-translational protein folding, mRNA degradation, and
more. Thus, regulating translation in order to obtain a
desired ribosomal profile along the mRNA molecule is an
important biological problem. We study this problem by
using a dynamical model for mRNA translation, called
the ribosome flow model RFM. In the RFM, the mRNA
molecule is modeled as an ordered chain of $n$ sites.
The RFM includes $n$ state-variables describing the
ribosomal density profile along the mRNA molecule, and
the transition rates from each site to the next are
controlled by $ n + 1$ positive constants. To study the
problem of controlling the density profile, we consider
some or all of the transition rates as time-varying
controls. We consider the following problem: given an
initial and a desired ribosomal density profile in the
RFM, determine the time-varying values of the
transition rates that steer the system to the desired
density profile, if they exist. More specifically, we
consider two control problems. In the first, all
transition rates can be regulated separately, and the
goal is to steer the ribosomal density profile and the
protein production rate from a given initial value to a
desired value. In the second problem, one or more
transition rates are jointly regulated by a single
scalar control, and the goal is to steer the production
rate to a desired value within a certain set of
feasible values. In the first case, we show that the
system is controllable, i.e., the control is powerful
enough to steer the system to any desired value in
finite time, and provide simple closed-form expressions
for constant positive control functions or transition
rates that asymptotically steer the system to the
desired value. In the second case, we show that the
system is controllable, and provide a simple algorithm
for determining the constant positive control value
that asymptotically steers the system to the desired
value. We discuss some of the biological implications
of these results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gao:2018:ISE,
author = "Shangce Gao and Shuangbao Song and Jiujun Cheng and
Yuki Todo and MengChu Zhou",
title = "Incorporation of Solvent Effect into Multi-Objective
Evolutionary Algorithm for Improved Protein Structure
Prediction",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1365--1378",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2705094",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The problem of predicting the three-dimensional 3-D
structure of a protein from its one-dimensional
sequence has been called the ``holy grail of molecular
biology'', and it has become an important part of
structural genomics projects. Despite the rapid
developments in computer technology and computational
intelligence, it remains challenging and fascinating.
In this paper, to solve it we propose a multi-objective
evolutionary algorithm. We decompose the protein energy
function Chemistry at HARvard Macromolecular Mechanics
force fields into bond and non-bond energies as the
first and second objectives. Considering the effect of
solvent, we innovatively adopt a solvent-accessible
surface area as the third objective. We use 66
benchmark proteins to verify the proposed method and
obtain better or competitive results in comparison with
the existing methods. The results suggest the necessity
to incorporate the effect of solvent into a
multi-objective evolutionary algorithm to improve
protein structure prediction in terms of accuracy and
efficiency.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Meyer:2018:MMP,
author = "Karlene Nicole Meyer and Michelle R. Lacey",
title = "Modeling Methylation Patterns with Long Read
Sequencing Data",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1379--1389",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2721943",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Variation in cytosine methylation at CpG dinucleotides
is often observed in genomic regions, and analysis
typically focuses on estimating the proportion of
methylated sites observed in a given region and
comparing these levels across samples to determine
association with conditions of interest. While sites
are tacitly treated as independent, when observed at
the level of individual molecules methylation patterns
exhibit strong evidence of local spatial dependence. We
previously developed a neighboring sites model to
account for correlation and clustering behavior
observed in two tandem repeat regions in a collection
of ovarian carcinomas. We now introduce extensions of
the model that account for the effect of distance
between sites as well as asymmetric correlation in de
novo methylation and demethylation rates. We apply our
models to published data from a whole genome bisulfite
sequencing experiment using long reads, estimating
model parameters for a selection of CpG-dense regions
spanning between 21 and 67 sites. Our methods detect
evidence of local spatial correlation as a function of
site-to-site distance and demonstrate the added value
of employing long read sequencing data in epigenetic
research.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2018:NPN,
author = "Guoxian Yu and Guangyuan Fu and Jun Wang and Yingwen
Zhao",
title = "{NewGOA}: Predicting New {GO} Annotations of Proteins
by Bi-Random Walks on a Hybrid Graph",
journal = j-TCBB,
volume = "15",
number = "4",
pages = "1390--1402",
month = jul,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2715842",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:45 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A remaining key challenge of modern biology is
annotating the functional roles of proteins. Various
computational models have been proposed for this
challenge. Most of them assume the annotations of
annotated proteins are complete. But in fact, many of
them are incomplete. We proposed a method called NewGOA
to predict new Gene Ontology GO annotations for
incompletely annotated proteins and for completely
un-annotated ones. NewGOA employs a hybrid graph,
composed of two types of nodes proteins and GO terms,
to encode interactions between proteins, hierarchical
relationships between terms and available annotations
of proteins. To account for structural difference
between GO terms subgraph and proteins subgraph, NewGOA
applies a bi-random walks algorithm, which executes
asynchronous random walks on the hybrid graph, to
predict new GO annotations of proteins. Experimental
study on archived GO annotations of two model species
H. Sapiens and S. cerevisiae shows that NewGOA can more
accurately and efficiently predict new annotations of
proteins than other related methods. Experimental
results also indicate the bi-random walks can explore
and further exploit the structural difference between
GO terms subgraph and proteins subgraph. The
supplementary files and codes of NewGOA are available
at: http://mlda.swu.edu.cn/codes.php?name=NewGOA.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ayday:2018:GIW,
author = "Erman Ayday and Xiaoqian Jiang and Bradley Malin",
title = "{GenoPri'16}: International Workshop on Genome Privacy
and Security",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1403--1404",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2856959",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Backes:2018:SLS,
author = "Michael Backes and Pascal Berrang and Mathias Humbert
and Xiaoyu Shen and Verena Wolf",
title = "Simulating the Large-Scale Erosion of Genomic Privacy
Over Time",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1405--1412",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2859380",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The dramatically decreasing costs of DNA sequencing
have triggered more than a million humans to have their
genotypes sequenced. Moreover, these individuals
increasingly make their genomic data publicly
available, thereby creating privacy threats for
themselves and their relatives because of their DNA
similarities. More generally, an entity that gains
access to a significant fraction of sequenced genotypes
might be able to infer even the genomes of unsequenced
individuals. In this paper, we propose a
simulation-based model for quantifying the impact of
continuously sequencing and publicizing personal
genomic data on a population's genomic privacy. Our
simulation probabilistically models data sharing and
takes into account events such as migration and
interracial mating. We exemplarily instantiate our
simulation with a sample population of 1,000
individuals and evaluate the privacy under multiple
settings over 6,000 genomic variants and a subset of
phenotype-related variants. Our findings demonstrate
that an increasing sharing rate in the future entails a
substantial negative effect on the privacy of all older
generations. Moreover, we find that mixed populations
face a less severe erosion of privacy over time than
more homogeneous populations. Finally, we demonstrate
that genomic-data sharing can be much more detrimental
for the privacy of the phenotype-related variants.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Raisaro:2018:PPS,
author = "Jean Louis Raisaro and Gwangbae Choi and Sylvain
Pradervand and Raphael Colsenet and Nathalie Jacquemont
and Nicolas Rosat and Vincent Mooser and Jean-Pierre
Hubaux",
title = "Protecting Privacy and Security of Genomic Data in
i2b2 with Homomorphic Encryption and Differential
Privacy",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1413--1426",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2854782",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Re-use of patients' health records can provide
tremendous benefits for clinical research. Yet, when
researchers need to access sensitive/identifying data,
such as genomic data, in order to compile cohorts of
well-characterized patients for specific studies,
privacy and security concerns represent major obstacles
that make such a procedure extremely difficult if not
impossible. In this paper, we address the challenge of
designing and deploying in a real operational setting
an efficient privacy-preserving explorer for genetic
cohorts. Our solution is built on top of the i2b2
Informatics for Integrating Biology and the Bedside
framework and leverages cutting-edge privacy-enhancing
technologies such as homomorphic encryption and
differential privacy. Solutions involving homomorphic
encryption are often believed to be costly and immature
for use in operational environments. Here, we show
that, for specific applications, homomorphic encryption
is actually a very efficient enabler. Indeed, our
solution outperforms prior work by enabling a
researcher to securely compute simple statistics on
more than 3,000 encrypted genetic variants
simultaneously for a cohort of 5,000 individuals in
less than 5 seconds with commodity hardware. To the
best of our knowledge, our privacy-preserving solution
is the first to also be successfully deployed and
tested in a operation setting Lausanne University
Hospital.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bogdanov:2018:IEA,
author = "Dan Bogdanov and Liina Kamm and Sven Laur and Ville
Sokk",
title = "Implementation and Evaluation of an Algorithm for
Cryptographically Private Principal Component Analysis
on Genomic Data",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1427--1432",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2858818",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We improve the quality of cryptographically
privacy-preserving genome-wide association studies by
correctly handling population stratification-the
inherent genetic difference of patient groups, e.g.,
people with different ancestries. Our approach is to
use principal component analysis to reduce the
dimensionality of the problem so that we get less
spurious correlations between traits of interest and
certain positions in the genome. While this approach is
commonplace in practical genomic analysis, it has not
been used within a privacy-preserving setting. In this
paper, we use cryptographically secure multi-party
computation to tackle principal component analysis, and
present an implementation and experimental results
showing the performance of the approach.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huang:2018:GES,
author = "De-Shuang Huang and Vitoantonio Bevilacqua and M.
Michael Gromiha",
title = "Guest Editorial for Special Section on the {12th
International Conference on Intelligent Computing
ICIC}",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1433--1435",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2848322",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kulandaisamy:2018:IAK,
author = "A. Kulandaisamy and Ambuj Srivastava and Pradeep Kumar
and R. Nagarajan and S. Binny Priya and M. Michael
Gromiha",
title = "Identification and Analysis of Key Residues in
Protein-{RNA} Complexes",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1436--1444",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2834387",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein-RNA complexes play important roles in various
biological processes. The functions of protein-RNA
complexes are dictated by their interactions, binding,
stability, and affinity. In this work, we have
identified the key residues KRs, which are involved in
both stability and binding. We found that 42 percent of
considered proteins share common binding and
stabilizing residues, whereas these residues are
distinct in 58 percent of the proteins. Overall, 5
percent of stabilizing and 3 percent of binding
residues serve as key residues. These residues are
enriched with the combination of polar, charged,
aliphatic, and aromatic residues. Analysis on
subclasses of protein-RNA complexes based on protein
structural class, function and RNA type showed that
regulatory proteins, and complexes with single stranded
RNA and rRNA have appreciable number of key residues.
Specifically, Arg, Tyr, and Thr are preferred in most
of the subclasses of protein-RNA complexes. In
addition, residues with similar chemical behavior have
different preferences to be KRs, such that Arg, Tyr,
Val, and Thr are preferred over Lys, Trp, Ile, and Ser,
respectively. Atomic level contacts revealed that
charged and polar-nonpolar contacts are dominant in
enzymes, polar in structural, and nonpolar in
regulatory proteins. On the other hand, polar-nonpolar
contacts are enriched in all these classes of
protein-RNA complexes. Further, the influence of
sequence and structural features such as conservation
score, surrounding hydrophobicity, solvent
accessibility, secondary structure, and long-range
order in key residues are also discussed. We envisage
that the present study provides insights to understand
the structural and functional aspects of protein-RNA
complexes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wu:2018:UDL,
author = "Hongjie Wu and Chengyuan Cao and Xiaoyan Xia and Qiang
Lu",
title = "Unified Deep Learning Architecture for Modeling
Biology Sequence",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1445--1452",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2760832",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Prediction of the spatial structure or function of
biological macromolecules based on their sequences
remains an important challenge in bioinformatics. When
modeling biological sequences using traditional
sequencing models, long-range interaction, complicated
and variable output of labeled structures, and variable
length of biological sequences usually lead to
different solutions on a case-by-case basis. This study
proposed a unified deep learning architecture based on
long short-term memory or a gated recurrent unit to
capture long-range interactions. The architecture
designs the optional reshape operator to adapt to the
diversity of the output labels and implements a
training algorithm to support the training of sequence
models capable of processing variable-length sequences.
The merging and pooling operators enhances the ability
of capturing short-range interactions between basic
units of biological sequences. The proposed
deep-learning architecture and its training algorithm
might be capable of solving currently variable
biological sequence-modeling problems under a unified
framework. We validated the model on one of the most
difficult biological sequence-modeling problems,
protein residue interaction prediction. The results
indicate that the accuracy of obtaining the residue
interactions of the model exceeded popular approaches
by 10 percent on multiple widely-used benchmarks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bao:2018:MFP,
author = "Wenzheng Bao and Chang-An Yuan and Youhua Zhang and
Kyungsook Han and Asoke K. Nandi and Barry Honig and
De-Shuang Huang",
title = "Mutli-Features Prediction of Protein Translational
Modification Sites",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1453--1460",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2752703",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Post translational modification plays a significant
role in the biological processing. The potential post
translational modification is composed of the center
sites and the adjacent amino acid residues which are
fundamental protein sequence residues. It can be
helpful to perform their biological functions and
contribute to understanding the molecular mechanisms
that are the foundations of protein design and drug
design. The existing algorithms of predicting modified
sites often have some shortcomings, such as lower
stability and accuracy. In this paper, a combination of
physical, chemical, statistical, and biological
properties of a protein have been utilized as the
features, and a novel framework is proposed to predict
a protein's post translational modification sites. The
multi-layer neural network and support vector machine
are invoked to predict the potential modified sites
with the selected features that include the
compositions of amino acid residues, the E-H
description of protein segments, and several properties
from the AAIndex database. Being aware of the possible
redundant information, the feature selection is
proposed in the preprocessing step in this research.
The experimental results show that the proposed method
has the ability to improve the accuracy in this
classification issue.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lee:2018:SBP,
author = "Wook Lee and Byungkyu Park and Kyungsook Han",
title = "Sequence-Based Prediction of Putative Transcription
Factor Binding Sites in {DNA} Sequences of Any Length",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1461--1469",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2773075",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A transcription factor TF is a protein that regulates
gene expression by binding to specific DNA sequences.
Despite recent advances in experimental techniques for
identifying transcription factor binding sites TFBS in
DNA sequences, a large number of TFBS are to be
unveiled in many species. Several computational methods
developed for predicting TFBS in DNA are tissue- or
species-specific methods, and therefore cannot be used
without prior knowledge of tissue or species. Some
computational methods are applicable to identifying
TFBS in short DNA sequences only. In this paper, we
propose a new learning method for predicting TFBS in
DNA of any length using the composition, transition,
and distribution of nucleotides and amino acids in DNA
and TF sequences. In independent testing of the method
on datasets that were not used in training the method,
the accuracy and MCC were as high as 81.84 percent and
0.634, respectively. The proposed method can be a
useful aid for selecting potential TFBS in a large
amount of DNA sequences before conducting biochemical
experiments to empirically determine TFBS. The program
and data sets are available at
http://bclab.inha.ac.kr/TFbinding.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lin:2018:PHR,
author = "Xiaoli Lin and Xiaolong Zhang",
title = "Prediction of Hot Regions in {PPIs} Based on Improved
Local Community Structure Detecting",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1470--1479",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2793858",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The hot regions in PPIs are some assembly regions
which are composed of the tightly packed HotSpots. The
discovery of hot regions helps to understand life
activities and has very important value for biological
applications. The identification of hot regions is the
basis for protein design and cancer prevention. The
existing algorithms of predicting hot regions often
have some defects, such as low accuracy and
unstability. This paper proposes a novel hot region
prediction method based on diverse biological
characteristics. First, feature evaluation is employed
by using an improved mRMR method. Then, SVM is adopted
to create cassification model based on the features
selected. In addition, a new clustering algorithm,
namely LCSD Local community structure detecting, is
developed to detect and analyze the conformation of hot
regions. In the clustering process, the link similarity
of protein residues is introduced to handle the
boundary nodes. This algorithm can effectively deal
with the missing residue nodes and control the local
community boundaries. The results indicate that the
spatial structure of hot regions can be obtained more
effectively, and that our method is more effective than
previous methods for precise identification of hot
regions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Deng:2018:IIG,
author = "Su-Ping Deng and Wenxing Hu and Vince D. Calhoun and
Yu-Ping Wang",
title = "Integrating Imaging Genomic Data in the Quest for
Biomarkers of Schizophrenia Disease",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1480--1491",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2748944",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "It's increasingly important but difficult to determine
potential biomarkers of schizophrenia SCZ disease,
owing to the complex pathophysiology of this disease.
In this study, a network-fusion based framework was
proposed to identify genetic biomarkers of the SCZ
disease. A three-step feature selection was applied to
single nucleotide polymorphisms SNPs, DNA methylation,
and functional magnetic resonance imaging fMRI data to
select important features, which were then used to
construct two gene networks in different states for the
SNPs and DNA methylation data, respectively. Two health
networks one is for SNP data and the other is for DNA
methylation data were combined into one health network
from which health minimum spanning trees MSTs were
extracted. Two disease networks also followed the same
procedures. Those genes with significant changes were
determined as SCZ biomarkers by comparing MSTs in two
different states and they were finally validated from
five aspects. The effectiveness of the proposed
discovery framework was also demonstrated by comparing
with other network-based discovery methods. In summary,
our approach provides a general framework for
discovering gene biomarkers of the complex diseases by
integrating imaging genomic data, which can be applied
to the diagnosis of the complex diseases in the
future.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Russo:2018:CPN,
author = "Giulia Russo and Marzio Pennisi and Roberta Boscarino
and Francesco Pappalardo",
title = "Continuous {Petri} Nets and {microRNA} Analysis in
Melanoma",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1492--1499",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2733529",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Personalized target therapies represent one of the
possible treatment strategies to fight the ongoing
battle against cancer. New treatment interventions are
still needed for an effective and successful cancer
therapy. In this scenario, we simulated and analyzed
the dynamics of BRAF V600E melanoma patients treated
with BRAF inhibitors in order to find potentially
interesting targets that may make standard treatments
more effective in particularly aggressive tumors that
may not respond to selective inhibitor drugs. To this
aim, we developed a continuous Petri Net model that
simulates fundamental signalling cascades involved in
melanoma development, such as MAPK and PI3K/AKT, in
order to deeply analyze these complex kinase cascades
and predict new crucial nodes involved in
melanomagenesis. The model pointed out that some
microRNAs, like hsa-mir-132, downregulates expression
levels of p120RasGAP: under high concentrations of
p120RasGAP, MAPK pathway activation is significantly
decreased and consequently also PI3K/PDK1/AKT
activation. Furthermore, our analysis carried out
through the Genomic Data Commons GDC Data Portal shows
the evidence that hsa-mir-132 is significantly
associated with clinical outcome in melanoma cancer
genomic data sets of BRAF-mutated patients. In
conclusion, targeting miRNAs through antisense
oligonucleotides technology may suggest the way to
enhance the action of BRAF-inhibitors.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2018:LRS,
author = "Jian Liu and Yuhu Cheng and Xuesong Wang and Xiaoluo
Cui and Yi Kong and Junping Du",
title = "Low Rank Subspace Clustering via Discrete Constraint
and Hypergraph Regularization for Tumor Molecular
Pattern Discovery",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1500--1512",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2834371",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Tumor clustering is a powerful approach for cancer
class discovery which is crucial to the effective
treatment of cancer. Many traditional clustering
methods such as NMF-based models, have been widely used
to identify tumors. However, they cannot achieve
satisfactory results. Recently, subspace clustering
approaches have been proposed to improve the
performance by dividing the original space into
multiple low-dimensional subspaces. Among them, low
rank representation is becoming a popular approach to
attain subspace clustering. In this paper, we propose a
novel Low Rank Subspace Clustering model via Discrete
Constraint and Hypergraph Regularization DHLRS. The
proposed method learns the cluster indicators directly
by using discrete constraint, which makes the
clustering task simple. For each subspace, we adopt
Schatten $p$-norm to better approximate the low rank
constraint. Moreover, Hypergraph Regularization is
adopted to infer the complex relationship between genes
and intrinsic geometrical structure of gene expression
data in each subspace. Finally, the molecular pattern
of tumor gene expression data sets is discovered
according to the optimized cluster indicators.
Experiments on both synthetic data and real tumor gene
expression data sets prove the effectiveness of
proposed DHLRS.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2018:GEA,
author = "Lusheng Wang and Shuai Cheng Li and Yi-Ping Phoebe
Chen",
title = "Guest Editorial for the {15th Asia Pacific
Bioinformatics Conference}",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1513--1514",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2843838",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Paszek:2018:EAG,
author = "Jaroslaw Paszek and Pawel Gorecki",
title = "Efficient Algorithms for Genomic Duplication Models",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1515--1524",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2706679",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "An important issue in evolutionary molecular biology
is to discover genomic duplication episodes and their
correspondence to the species tree. Existing approaches
vary in the two fundamental aspects: the choice of
evolutionary scenarios that model allowed locations of
duplications in the species tree, and the rules of
clustering gene duplications from gene trees into a
single multiple duplication event. Here we study the
method of clustering called minimum episodes for
several models of allowed evolutionary scenarios with a
focus on interval models in which every gene
duplication has an interval consisting of allowed
locations in the species tree. We present mathematical
foundations for general genomic duplication problems.
Next, we propose the first linear time and space
algorithm for minimum episodes clustering jointly for
any interval model and the algorithm for the most
general model in which every evolutionary scenario is
allowed. We also present a comparative study of
different models of genomic duplication based on
simulated and empirical datasets. We provided
algorithms and tools that could be applied to solve
efficiently minimum episodes clustering problems. Our
comparative study helps to identify which model is the
most reasonable choice in inferring genomic duplication
events.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mizera:2018:RTS,
author = "Andrzej Mizera and Jun Pang and Qixia Yuan",
title = "Reviving the Two-State {Markov} Chain Approach",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1525--1537",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2704592",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Probabilistic Boolean networks PBNs is a
well-established computational framework for modelling
biological systems. The steady-state dynamics of PBNs
is of crucial importance in the study of such systems.
However, for large PBNs, which often arise in systems
biology, obtaining the steady-state distribution poses
a significant challenge. In this paper, we revive the
two-state Markov chain approach to solve this problem.
This paper contributes in three aspects. First, we
identify a problem of generating biased results with
the approach and we propose a few heuristics to avoid
such a pitfall. Second, we conduct an extensive
experimental comparison of the extended two-state
Markov chain approach and another approach based on the
Skart method. We analyze the results with machine
learning techniques and we show that statistically the
two-state Markov chain approach has a better
performance. Finally, we demonstrate the potential of
the extended two-state Markov chain approach on a case
study of a large PBN model of apoptosis in
hepatocytes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{An:2018:LNN,
author = "Shuai An and Jun Wang and Jinmao Wei",
title = "Local-Nearest-Neighbors-Based Feature Weighting for
Gene Selection",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1538--1548",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2712775",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Selecting functional genes is essential for analyzing
microarray data. Among many available feature gene
selection approaches, the ones on the basis of the
large margin nearest neighbor receive more attention
due to their low computational costs and high
accuracies in analyzing the high-dimensional data. Yet,
there still exist some problems that hamper the
existing approaches in sifting real target genes,
including selecting erroneous nearest neighbors, high
sensitivity to irrelevant genes, and inappropriate
evaluation criteria. Previous pioneer works have partly
addressed some of the problems, but none of them are
capable of solving these problems simultaneously. In
this paper, we propose a new
local-nearest-neighbors-based feature weighting
approach to alleviate the above problems. The proposed
approach is based on the trick of locally minimizing
the within-class distances and maximizing the
between-class distances with the $k$ nearest neighbors
rule. We further define a feature weight vector, and
construct it by minimizing the cost function with a
regularization term. The proposed approach can be
applied naturally to the multi-class problems and does
not require extra modification. Experimental results on
the UCI and the open microarray data sets validate the
effectiveness and efficiency of the new approach.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jiang:2018:BET,
author = "Nan Jiang and Wenge Rong and Yifan Nie and Yikang Shen
and Zhang Xiong",
title = "Biological Event Trigger Identification with Noise
Contrastive Estimation",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1549--1559",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2710048",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Biological Event Extraction is an important task
towards the goal of extracting biomedical knowledge
from the scientific publications by capturing
biomedical entities and their complex relations from
the texts. As a crucial step in event extraction, event
trigger identification, assigning words with suitable
trigger category, has recently attracted substantial
attention. As triggers are scattered in large corpus,
traditional linguistic parsers are hard to generate
syntactic features from them. Thereby, trigger sparsity
problem restricts the model's learning process and
becomes one of the main hinder in trigger
identification. In this paper, we employ Noise
Contrastive Estimation with Multi-Layer Perceptron
model for solving triggers' sparsity problem.
Meanwhile, in the light of recent advance in word
distributed representation, word-embedding feature
generated by language model is utilized for semantic
and syntactic information extraction. Finally,
experimental study on commonly used MLEE dataset
against baseline methods has demonstrated its promising
result.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lafond:2018:GTC,
author = "Manuel Lafond and Cedric Chauve and Nadia El-Mabrouk
and Aida Ouangraoua",
title = "Gene Tree Construction and Correction Using
{SuperTree} and Reconciliation",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1560--1570",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2720581",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The supertree problem asking for a tree displaying a
set of consistent input trees has been largely
considered for the reconstruction of species trees.
Here, we rather explore this framework for the sake of
reconstructing a gene tree from a set of input gene
trees on partial data. In this perspective, the
phylogenetic tree for the species containing the genes
of interest can be used to choose among the many
possible compatible ``supergenetrees'', the most
natural criteria being to minimize a reconciliation
cost. We develop a variety of algorithmic solutions for
the construction and correction of gene trees using the
supertree framework. A dynamic programming supertree
algorithm for constructing or correcting gene trees,
exponential in the number of input trees, is first
developed for the less constrained version of the
problem. It is then adapted to gene trees with nodes
labeled as duplication or speciation, the additional
constraint being to preserve the orthology and paralogy
relations between genes. Then, a quadratic time
algorithm is developed for efficiently correcting an
initial gene tree while preserving a set of ``trusted''
subtrees, as well as the relative phylogenetic distance
between them, in both cases of labeled or unlabeled
input trees. By applying these algorithms to the set of
Ensembl gene trees, we show that this new correction
framework is particularly useful to correct
weakly-supported duplication nodes. The C++ source code
for the algorithms and simulations described in the
paper are available at
https://github.com/UdeM-LBIT/SuGeT.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mykowiecka:2018:IGS,
author = "Agnieszka Mykowiecka and Pawel Szczesny and Pawel
Gorecki",
title = "Inferring Gene-Species Assignments in the Presence of
Horizontal Gene Transfer",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1571--1578",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2707083",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Background: Microbial communities from environmental
samples show great diversity as bacteria quickly
responds to changes in their ecosystems. To assess the
scenario of the actual changes, metagenomics
experiments aimed at sequencing genomic DNA from such
samples are performed. These new obtained sequences
together with already known are used to infer
phylogenetic trees assessing the taxonomic groups the
species with these genes belong to. Here, we propose
the first approach to the gene-species assignment
problem by using reconciliation with horizontal gene
transfer. Results: We propose efficient algorithms that
search for optimal gene-species mappings taking into
account gene duplication, loss and transfer events
under two tractable models of HGT reconciliation.
Conclusions: We calculate both the optimal cost and all
possible optimal scenarios. Furthermore as the number
of optimal reconstructions can be large, we use a
Monte-Carlo method for the inference of approximate
distributions of gene-species assignments. We
demonstrate the applicability on empirical and
simulated datasets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2018:EMS,
author = "Yue Zhang and Chunfang Zheng and David Sankoff",
title = "Evolutionary Model for the Statistical Divergence of
Paralogous and Orthologous Gene Pairs Generated by
Whole Genome Duplication and Speciation",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1579--1584",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2712695",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We outline a principled approach to the analysis of
duplicate gene similarity distributions, based on a
model integrating sequence divergence and the process
of fractionation of duplicate genes resulting from
whole genome duplication WGD. This model allows us to
predict duplicate gene similarity distributions for a
series of two or three WGD, for whole genome
triplication followed by a WGD, and for triplication,
followed by speciation, followed by WGD. We calculate
the probabilities of all possible fates of a gene pair
as its two members proliferate or are lost, predicting
the number of surviving pairs from each event. We
discuss how to calculate maximum likelihood estimators
for the parameters of these models, illustrating with
an analysis of the distribution of paralog similarities
in the poplar genome.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hartmann:2018:GRI,
author = "Tom Hartmann and Nicolas Wieseke and Roded Sharan and
Martin Middendorf and Matthias Bernt",
title = "Genome Rearrangement with {ILP}",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1585--1593",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2708121",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The weighted Genome Sorting Problem wGSP is to find a
minimum-weight sequence of rearrangement operations
that transforms a given gene order into another given
gene order using rearrangement operations that are
associated with a predefined weight. This paper
presents a polynomial sized Integer Linear Program ---
called GeRe-ILP --- for solving the wGSP for the
following three types of rearrangement operations:
inversion, transposition, and inverse transposition.
GeRe-ILP uses $ O(n^3) $ variables and $ O(n^3) $
constraints for gene orders of length $n$. It is
studied experimentally on simulated data how different
weighting schemes influence the reconstructed
scenarios. The influences of the length of the gene
orders and of the size of the reconstructed scenarios
on the runtime of GeRe-ILP are studied as well.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sedaghat:2018:CSU,
author = "Nafiseh Sedaghat and Mahmood Fathy and Mohammad
Hossein Modarressi and Ali Shojaie",
title = "Combining Supervised and Unsupervised Learning for
Improved {miRNA} Target Prediction",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1594--1604",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2727042",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "MicroRNAs miRNAs are short non-coding RNAs which bind
to mRNAs and regulate their expression. MiRNAs have
been found to be associated with initiation and
progression of many complex diseases. Investigating
miRNAs and their targets can thus help develop new
therapies by designing anti-miRNA oligonucleotides.
While existing computational approaches can predict
miRNA targets, these predictions have low accuracy. In
this paper, we propose a two-step approach to refine
the results of sequence-based prediction algorithms.
The first step, which is based on our previous work,
uses an ensemble learning approach that combines
multiple existing methods. The second step utilizes
support vector machine SVM classifiers in one- and
two-class modes to infer miRNA-mRNA interactions based
on both binding features, as well as network features
extracted from gene regulatory network. Experimental
results using two real data sets from TCGA indicate
that the use of two-class SVM classification
significantly improves the precision of miRNA-mRNA
prediction.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hamad:2018:DWU,
author = "Safwat Hamad and Ahmed Elhadad and Amal Khalifa",
title = "{DNA} Watermarking Using Codon Postfix Technique",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1605--1610",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2754496",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "DNA watermarking is a data hiding technique that aims
to protect the copyright of DNA sequences and ensures
the security of private genetic information. In this
paper, we proposed a novel DNA watermarking technique
that can be used to embed binary bits into real DNA
sequences. The proposed technique mutates the codon
postfix according to the embedded bit. Our method was
tested for a sample set of DNA sequences and the
extracted bits showed robustness against mutation.
Furthermore, the proposed DNA watermarking method
proved to be secured, undetectable, resistance, and
preservative to biological functions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Duarte-Sanchez:2018:HAM,
author = "Jorge E. Duarte-Sanchez and Jaime Velasco-Medina and
Pedro A. Moreno",
title = "Hardware Accelerator for the Multifractal Analysis of
{DNA} Sequences",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1611--1624",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2731339",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The multifractal analysis has allowed to quantify the
genetic variability and non-linear stability along the
human genome sequence. It has some implications in
explaining several genetic diseases given by some
chromosome abnormalities, among other genetic
particularities. The multifractal analysis of a genome
is carried out by dividing the complete DNA sequence in
smaller fragments and calculating the generalized
dimension spectrum of each fragment using the chaos
game representation and the box-counting method. This
is a time consuming process because it involves the
processing of large data sets using floating-point
representation. In order to reduce the computation
time, we designed an application-specific processor,
here called multifractal processor, which is based on
our proposed hardware-oriented algorithm for
calculating efficiently the generalized dimension
spectrum of DNA sequences. The multifractal processor
was implemented on a low-cost SoC-FPGA and was verified
by processing a complete human genome. The execution
time and numeric results of the Multifractal processor
were compared with the results obtained from the
software implementation executed in a 20-core
workstation, achieving a speed up of 2.6x and an
average error of 0.0003 percent.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nguyen:2018:HTN,
author = "Nha Nguyen and An Vo and Haibin Sun and Heng Huang",
title = "Heavy-Tailed Noise Suppression and Derivative Wavelet
Scalogram for Detecting {DNA} Copy Number Aberrations",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1625--1635",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2723884",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Most existing array comparative genomic hybridization
array CGH data processing methods and evaluation models
assumed that the probability density function pdf of
noise in array CGH data is a Gaussian distribution.
However, in practice, such noise distribution is peaky
and heavy-tailed. Therefore, a Gaussian pdf is not
adequate to approximate the noise in array CGH data and
hence introduces wrong detections of chromosomal
aberrations and leads misunderstanding on disease
pathogenesis. A more accurate and sufficient model of
noise in array CGH data is necessary and beneficial to
the detection of DNA copy number variations. We analyze
the real array CGH data from different platforms and
show that the distribution of noise in array CGH data
is fitted very well by generalized Gaussian
distribution GGD. Based on our new noise model, we
propose a novel array CGH processing method combining
the advantages of both the smoothing and segmentation
approaches. The new method uses generalized Gaussian
bivariate shrinkage function and one-directional
derivative wavelet scalogram in generalized Gaussian
noise. In the smoothing step, with the new generalized
Gaussian noise model, we derive the heavy-tailed noise
suppression algorithm in stationary wavelet domain. In
the segmentation step, the 1D Gaussian derivative
wavelet scalogram is employed to detect break points.
Both real and simulated array CGH data with different
noises such as Gaussian noise, GGD noise, and real
noise are used in our experiments. We demonstrate that
our new method outperforms other state-of-the-art
methods, in terms of both root mean squared errors and
receiver operating characteristic curves.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ma:2018:ICS,
author = "Xiaoke Ma and Penggang Sun and Guimin Qin",
title = "Identifying Condition-Specific Modules by Clustering
Multiple Networks",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1636--1648",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2761339",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Condition-specific modules in multiple networks must
be determined to reveal the underlying molecular
mechanisms of diseases. Current algorithms exhibit
limitations such as low accuracy and high sensitivity
to the number of networks because these algorithms
discover condition-specific modules in multiple
networks by separating specificity and modularity of
modules. To overcome these limitations, we characterize
condition-specific module as a group of genes whose
connectivity is strong in the corresponding network and
weak in other networks; this strategy can accurately
depict the topological structure of condition-specific
modules. We then transform the condition-specific
module discovery problem into a clustering problem in
multiple networks. We develop an efficient heuristic
algorithm for the Specific Modules in Multiple N
etworks SMMN, which discovers the condition-specific
modules by considering multiple networks. By using the
artificial networks, we demonstrate that SMMN
outperforms state-of-the-art methods. In breast cancer
networks, stage-specific modules discovered by SMMN are
more discriminative in predicting cancer stages than
those obtained by other techniques. In pan-cancer
networks, cancer-specific modules are more likely to
associate with survival time of patients, which is
critical for cancer therapy.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2018:IAD,
author = "Jin Liu and Jianxin Wang and Zhenjun Tang and Bin Hu
and Fang-Xiang Wu and Yi Pan",
title = "Improving {Alzheimer}'s Disease Classification by
Combining Multiple Measures",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1649--1659",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2731849",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Several anatomical magnetic resonance imaging MRI
markers for Alzheimer's disease AD have been
identified. Cortical gray matter volume, cortical
thickness, and subcortical volume have been used
successfully to assist the diagnosis of Alzheimer's
disease including its early warning and developing
stages, e.g., mild cognitive impairment MCI including
MCI converted to AD MCIc and MCI not converted to AD
MCInc. Currently, these anatomical MRI measures have
mainly been used separately. Thus, the full potential
of anatomical MRI scans for AD diagnosis might not yet
have been used optimally. Meanwhile, most studies
currently only focused on morphological features of
regions of interest ROIs or interregional features
without considering the combination of them. To further
improve the diagnosis of AD, we propose a novel
approach of extracting ROI features and interregional
features based on multiple measures from MRI images to
distinguish AD, MCI including MCIc and MCInc, and
health control HC. First, we construct six individual
networks based on six different anatomical measures
i.e., CGMV, CT, CSA, CC, CFI, and SV and Automated
Anatomical Labeling AAL atlas for each subject. Then,
for each individual network, we extract all node ROI
features and edge interregional features, and denoted
as node feature set and edge feature set, respectively.
Therefore, we can obtain six node feature sets and six
edge feature sets from six different anatomical
measures. Next, each feature within a feature set is
ranked by $F$-score in descending order, and the top
ranked features of each feature set are applied to
MKBoost algorithm to obtain the best classification
accuracy. After obtaining the best classification
accuracy, we can get the optimal feature subset and the
corresponding classifier for each node or edge feature
set. Afterwards, to investigate the classification
performance with only node features, we proposed a
weighted multiple kernel learning wMKL framework to
combine these six optimal node feature subsets, and
obtain a combined classifier to perform AD
classification. Similarly, we can obtain the
classification performance with only edge features.
Finally, we combine both six optimal node feature
subsets and six optimal edge feature subsets to further
improve the classification performance. Experimental
results show that the proposed method outperforms some
state-of-the-art methods in AD classification, and
demonstrate that different measures contain
complementary information.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ren:2018:IAS,
author = "Shuai Ren and Yan Shi and Maolin Cai and Weiqing Xu",
title = "Influence of Airway Secretion on Airflow Dynamics of
Mechanical Ventilated Respiratory System",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1660--1668",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2737621",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Secretions in the airways of mechanical ventilated
patients are extremely dangerous to patients' health.
In recent studies, the continuous constant airflow is
adopted, however, it is not consistent with a clinical
situation. To study respiratory airflow dynamic
characteristics with secretion in the airways, a
mathematical model based on clinical mechanical
ventilation is established in this paper. To illustrate
the secretion's influence on the airflow dynamics of
mechanical ventilated respiratory system, three key
parameters which are cross section area ratio of
secretion/ pipe, air-secretion contact area, and
secretion viscosity are involved in the study. Through
the experimental study, the accuracy and dependability
of the model are confirmed. By the simulation study, we
find that: based on the model which combines two
airways and two model lungs, when one of the airways
was covered with secretion, the maximum pressure of the
model lung which is attached to the end of this airway
maintains constant when the cross section area ratio is
less than 66 percent, and then it tends to decline
sharply with the ratio increasing, but it remains
constant with the augment of air-secretion contact
area, the maximum flow declines both with the
increasing of cross section area ratio and
air-secretion contact area. Furthermore, as for the
other airway, the maximum pressure of the model lung
has no significant changes with the augment of area
ratio and air-secretion contact area, however, along
with the increasing of area ratio and air-secretion
contact area, the maximum flow rises up. Moreover, the
secretion viscosity has barely any influence on airflow
dynamics. According to our analysis results, we
conclude that the cross section area ratio of
secretion/pipe has bigger influence on airflow dynamic
characteristics than air-secretion contact area and
secretion viscosity. This paper lays the foundation for
the further study of efficacy and safety in mechanical
ventilation and the secretion clearance of mechanical
ventilated patients. In addition, the mathematical
model proposed in this paper can also be referred to
study on the secretion movement in human airways.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Vijayan:2018:MNA,
author = "Vipin Vijayan and Tijana Milenkovic",
title = "Multiple Network Alignment via {MultiMAGNA++}",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1669--1682",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2740381",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Network alignment NA aims to find a node mapping that
identifies topologically or functionally similar
network regions between molecular networks of different
species. Analogous to genomic sequence alignment, NA
can be used to transfer biological knowledge from well-
to poorly-studied species between aligned network
regions. Pairwise NA PNA finds similar regions between
two networks while multiple NA MNA can align more than
two networks. We focus on MNA. Existing MNA methods aim
to maximize total similarity over all aligned nodes
node conservation. Then, they evaluate alignment
quality by measuring the amount of conserved edges, but
only after the alignment is constructed. Directly
optimizing edge conservation during alignment
construction in addition to node conservation may
result in superior alignments. Thus, we present a novel
MNA method called multiMAGNA++ that can achieve this.
Indeed, multiMAGNA++ outperforms or is on par with
existing MNA methods, while often completing faster
than existing methods. That is, multiMAGNA++ scales
well to larger network data and can be parallelized
effectively. During method evaluation, we also
introduce new MNA quality measures to allow for more
fair MNA method comparison compared to the existing
alignment quality measures. The multiMAGNA++ code is
available on the method's web page at
http://nd.edu/~cone/multiMAGNA++/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2018:NFN,
author = "Chunjiang Yu and Wentao Wu and Jing Wang and Yuxin Lin
and Yang and Jiajia Chen and Fei Zhu and Bairong Shen",
title = "{NGS-FC}: a Next-Generation Sequencing Data Format
Converter",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1683--1691",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2722442",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/java2010.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "With the widespread implementation of next-generation
sequencing NGS technologies, millions of sequences have
been produced. A lot of databases were created to store
and organize the high-throughput sequencing data.
Numerous analysis software programs and tools have been
developed over the past years. Most of them use
specific formats for data representation and storage.
Data interoperability becomes a crucial challenge and
many tools have been developed to convert NGS data from
one format to another. However, most of them were
developed for specific and limited formats. Here, we
present NGS-FC Next-Generation Sequencing Format
Converter, which provides a framework to support the
conversion between several formats. It supports 14
formats now and provides interfaces to enable users to
improve the existing converters and add new ones.
Moreover, NGS-FC achieved the overall competitive
performance in comparison with some existing converters
in terms of RAM usage and running time. The software is
written in Java and can be executed standalone. The
source code and documentation are freely available at
http://sysbio.suda.edu.cn/NGS-FC.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jamil:2018:OPQ,
author = "Hasan M. Jamil",
title = "Optimizing Phylogenetic Queries for Performance",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1692--1705",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2743706",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The vast majority of phylogenetic databases do not
support declarative querying using which their contents
can be flexibly and conveniently accessed and the
template based query interfaces they support do not
allow arbitrary speculative queries. They therefore
also do not support query optimization leveraging
unique phylogeny properties. While a small number of
graph query languages such as XQuery, Cypher, and
GraphQL exist for computer savvy users, most are too
general and complex to be useful for biologists, and
too inefficient for large phylogeny querying. In this
paper, we discuss a recently introduced visual query
language, called PhyQL, that leverages phylogeny
specific properties to support essential and powerful
constructs for a large class of phylogentic queries. We
develop a range of pruning aids, and propose a
substantial set of query optimization strategies using
these aids suitable for large phylogeny querying. A
hybrid optimization technique that exploits a set of
indices and ``graphlet'' partitioning is discussed. A
``fail soonest'' strategy is used to avoid hopeless
processing and is shown to produce dividends. Possible
novel optimization techniques yet to be explored are
also discussed.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2018:CPT,
author = "Min Liu and Yue He and Weili Qian and Yangliu Wei and
Xiaoyan Liu",
title = "Cell Population Tracking in a Honeycomb Structure
Using an {IMM} Filter Based {$3$D} Local Graph Matching
Model",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1706--1717",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2760300",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Developing algorithms for plant cell population
tracking is very critical for the modeling of plant
cell growth pattern and gene expression dynamics. The
tracking of plant cells in microscopic image stacks is
very challenging for several reasons: 1 plant cells are
densely packed in a specific honeycomb structure; 2
they are frequently dividing; and 3 they are imaged in
different layers within 3D image stacks. Based on an
existing 2D local graph matching algorithm, this paper
focuses on building a 3D plant cell matching model, by
exploiting the cells' 3D spatiotemporal context.
Furthermore, the Interacting Multi-Model filter IMM is
combined with the 3D local graph matching model to
track the plant cell population simultaneously. Because
our tracking algorithm does not require the
identification of ``tracking seeds'', the tracking
stability and efficiency are greatly enhanced. Last,
the plant cell lineages are achieved by associating the
cell tracklets, using a maximum-a-posteriori MAP
method. Compared with the 2D matching method, the
experimental results on multiple datasets show that our
proposed approach does not only greatly improve the
tracking accuracy by 18 percent, but also successfully
tracks the plant cells located at the high curvature
primordial region, which is not addressed in previous
work.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2018:SCF,
author = "Shifu Chen and Ming Liu and Xiaoni Zhang and Renwen
Long and Yixing Wang and Yue Han and Shiwei Zhang and
Mingyan Xu and Jia Gu",
title = "A Study of Cell-Free {DNA} Fragmentation Pattern and
Its Application in {DNA} Sample Type Classification",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1718--1722",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2723388",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Plasma cell-free DNA cfDNA has certain fragmentation
patterns, which can bring non-random base content
curves of the sequencing data's beginning cycles. We
studied the patterns and found that we could determine
whether a sample is cfDNA or not by just looking into
the first 10 cycles of its base content curves. We
analyzed 3,189 FastQ files, including 1,442 plasma
cfDNA, 1,234 genomic DNA, 507 FFPE tumour DNA, and 6
urinary cfDNA. By deep analyzing these data, we found
the patterns were stable enough to distinguish cfDNA
from other kinds of DNA samples. Based on this finding,
we built classification models to recognize cfDNA
samples by their sequencing data. Pattern recognition
models were then trained with different classification
algorithms like k-nearest neighbors KNN, random forest,
and support vector machine SVM. The result of 1,000
iteration .632+ bootstrapping showed that all these
classifiers could give an average accuracy higher than
98 percent, indicating that the cfDNA patterns are
unique and can make the dataset highly separable. The
best result was obtained using a random forest
classifier with a 99.89 percent average accuracy $
\sigma = 0.00068 $. A tool called CfdnaPattern
http://github.com/OpenGene/CfdnaPattern has been
developed to train the model and to predict whether a
sample is cfDNA or not.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gorecki:2018:BDG,
author = "Pawel Gorecki and Oliver Eulenstein",
title = "Bijective Diameters of Gene Tree Parsimony Costs",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1723--1727",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2735968",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Synthesizing median trees from a collection of gene
trees under the biologically motivated gene tree
parsimony GTP costs has provided credible species tree
estimates. GTP costs are defined for each of the
classic evolutionary processes. These costs count the
minimum number of events necessary to reconcile the
gene tree with the species tree where the leaf-genes
are mapped to the leaf-species through a function
called labeling. To better understand the synthesis of
median trees under these costs, there is an increased
interest in analyzing their diameters. The diameters of
a GTP cost between a gene tree and a species tree are
the maximum values of this cost of one or both
topologies of the trees involved. We are concerned
about the diameters of the GTP costs under bijective
labelings. While these diameters are linear time
computable for the gene duplication and deep
coalescence costs, this has been unknown for the
classic gene duplication and loss, and for the loss
cost. For the first time, we show how to compute these
diameters and proof that this can be achieved in linear
time, and thus, completing the computational time
analysis for all of the bijective diameters under the
GTP costs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2018:FWI,
author = "Youyuan Li and Yingping Zhuang",
title = "{fmpRPMF}: a {Web} Implementation for Protein
Identification by Robust Peptide Mass Fingerprinting",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1728--1731",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2762682",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Peptide mass fingerprinting continues to play an
important role in current proteomics studies based on
its good performance in sample throughput, specificity
for single peptides, and insensitivity to unexpected
post-translational modifications as compared with MSn.
We previously proposed and evaluated the use of
feature-matching pattern-based support vector machines
SVMs for robust protein identification. This approach
is now facilitated with an updated web server fmpRPMF
incorporated with several newly developed or improved
modules and workflows allowing identification of
proteins from MS1 data. Development of the latest
fmpRPMF web tool successfully provides a rapid and
effective strategy for narrowing the range of candidate
proteins. First, a mass-scanning procedure screens all
candidate proteins matching the theoretical peptide
mass at least three times, thereby reducing the number
of candidate proteins from tens of thousands to
thousands. Second, a crude ranking procedure screens
true-positive proteins among the top six ranked times
of candidates based on 17 selected features to reduce
the number used for SVM prediction from thousands to
tens. The improvement of forecasting efficiency met the
requirements of multi-user and multi-task
identification for web services. The updated fmpRPMF
server is freely available at
http://bioinformatics.datawisdom.net/fmp.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gonzalez-Dominguez:2018:MPC,
author = "Jorge Gonzalez-Dominguez and Maria J. Martin",
title = "{MPIGeneNet}: Parallel Calculation of Gene
Co-Expression Networks on Multicore Clusters",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1732--1737",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2761340",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pvm.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this work, we present MPIGeneNet, a parallel tool
that applies Pearson's correlation and Random Matrix
Theory to construct gene co-expression networks. It is
based on the state-of-the-art sequential tool
RMTGeneNet, which provides networks with high
robustness and sensitivity at the expenses of
relatively long runtimes for large scale input
datasets. MPIGeneNet returns the same results as
RMTGeneNet but improves the memory management, reduces
the I/O cost, and accelerates the two most
computationally demanding steps of co-expression
network construction by exploiting the compute
capabilities of common multicore CPU clusters. Our
performance evaluation on two different systems using
three typical input datasets shows that MPIGeneNet is
significantly faster than RMTGeneNet. As an example,
our tool is up to 175.41 times faster on a cluster with
eight nodes, each one containing two 12-core Intel
Haswell processors. The source code of MPIGeneNet, as
well as a reference manual, are available at
https://sourceforge.net/projects/mpigenenet/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Shekhar:2018:STE,
author = "Shubhanshu Shekhar and Sebastien Roch and Siavash
Mirarab",
title = "Species Tree Estimation Using {ASTRAL}: How Many Genes
Are Enough?",
journal = j-TCBB,
volume = "15",
number = "5",
pages = "1738--1747",
month = sep,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2757930",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Nov 8 06:18:46 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Species tree reconstruction from genomic data is
increasingly performed using methods that account for
sources of gene tree discordance such as incomplete
lineage sorting. One popular method for reconstructing
species trees from unrooted gene tree topologies is
ASTRAL. In this paper, we derive theoretical sample
complexity results for the number of genes required by
ASTRAL to guarantee reconstruction of the correct
species tree with high probability. We also validate
those theoretical bounds in a simulation study. Our
results indicate that ASTRAL requires $ O(f^{-2} \log
n) $ gene trees to reconstruct the species tree
correctly with high probability where $n$ is the number
of species and $f$ is the length of the shortest branch
in the species tree. Our simulations, some under the
anomaly zone, show trends consistent with the
theoretical bounds and also provide some practical
insights on the conditions where ASTRAL works well.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tian:2018:GES,
author = "Tianhai Tian and Jingshan Huang",
title = "Guest Editorial for Special Section on {BIBM 2015}",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1752--1753",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2870626",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The six papers in this special section were presented
at the IEEE BIBM 2015 conference that was held in
Washington, D.C., November 9-12, 2015. The scientific
program highlighted five themes to provide breadth,
depth, and synergy for research collaboration: 1
genomics and molecular structure, function, and
evolution; 2 computational systems biology; 3 medical
informatics and translational bioinformatics; 4
cross-cutting computational methods and bioinformatics
infrastructures; and 5 healthcare informatics,",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chiu:2018:ADR,
author = "Yu-Chiao Chiu and Tzu-Hung Hsiao and Li-Ju Wang and
Yidong Chen and Eric Y. Chuang",
title = "Analyzing Differential Regulatory Networks Modulated
by Continuous-State Genomic Features in Glioblastoma
Multiforme",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1754--1764",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2635646",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gene regulatory networks are a global representation
of complex interactions between molecules that dictate
cellular behavior. Study of a regulatory network
modulated by single or multiple modulators' expression
levels, including microRNAs miRNAs and transcription
factors TFs, in different conditions can further reveal
the modulators' roles in diseases such as cancers.
Existing computational methods for identifying such
modulated regulatory networks are typically carried out
by comparing groups of samples dichotomized with
respect to the modulator status, ignoring the fact that
most biological features are intrinsically continuous
variables. Here, we devised a sliding window-based
regression scheme and proposed the Regression-based
Inference of Modulation RIM algorithm to infer the
dynamic gene regulation modulated by continuous-state
modulators. We demonstrated the improvement in
performance as well as computation efficiency achieved
by RIM. Applying RIM to genome-wide expression profiles
of 520 glioblastoma multiforme GBM tumors, we
investigated miRNA- and TF-modulated gene regulatory
networks and showed their association with dynamic
cellular processes and brain-related functions in GBM.
Overall, the proposed algorithm provides an efficient
and robust scheme for comprehensively studying
modulated gene regulatory networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hu:2018:FSO,
author = "Bin Hu and Yongqiang Dai and Yun Su and Philip Moore
and Xiaowei Zhang and Chengsheng Mao and Jing Chen and
Lixin Xu",
title = "Feature Selection for Optimized High-Dimensional
Biomedical Data Using an Improved Shuffled Frog Leaping
Algorithm",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1765--1773",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2602263",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "High dimensional biomedical datasets contain thousands
of features which can be used in molecular diagnosis of
disease, however, such datasets contain many irrelevant
or weak correlation features which influence the
predictive accuracy of diagnosis. Without a feature
selection algorithm, it is difficult for the existing
classification techniques to accurately identify
patterns in the features. The purpose of feature
selection is to not only identify a feature subset from
an original set of features [without reducing the
predictive accuracy of classification algorithm] but
also reduce the computation overhead in data mining. In
this paper, we present our improved shuffled frog
leaping algorithm which introduces a chaos memory
weight factor, an absolute balance group strategy, and
an adaptive transfer factor. Our proposed approach
explores the space of possible subsets to obtain the
set of features that maximizes the predictive accuracy
and minimizes irrelevant features in high-dimensional
biomedical data. To evaluate the effectiveness of our
proposed method, we have employed the K-nearest
neighbor method with a comparative analysis in which we
compare our proposed approach with genetic algorithms,
particle swarm optimization, and the shuffled frog
leaping algorithm. Experimental results show that our
improved algorithm achieves improvements in the
identification of relevant subsets and in
classification accuracy.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lan:2018:PMD,
author = "Wei Lan and Jianxin Wang and Min Li and Jin Liu and
Fang-Xiang Wu and Yi Pan",
title = "Predicting {MicroRNA}-Disease Associations Based on
Improved {MicroRNA} and Disease Similarities",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1774--1782",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2586190",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "MicroRNAs miRNAs are a type of non-coding RNAs with
about ~22nt nucleotides. Increasing evidences have
shown that miRNAs play critical roles in many human
diseases. The identification of human disease-related
miRNAs is helpful to explore the underlying
pathogenesis of diseases. More and more experimental
validated associations between miRNAs and diseases have
been reported in the recent studies, which provide
useful information for new miRNA-disease association
discovery. In this study, we propose a computational
framework, KBMF-MDI, to predict the associations
between miRNAs and diseases based on their
similarities. The sequence and function information of
miRNAs are used to measure similarity among miRNAs
while the semantic and function information of disease
are used to measure similarity among diseases,
respectively. In addition, the kernelized Bayesian
matrix factorization method is employed to infer
potential miRNA-disease associations by integrating
these data sources. We applied this method to 6,084
known miRNA-disease associations and utilized 5-fold
cross validation to evaluate the performance. The
experimental results demonstrate that our method can
effectively predict unknown miRNA-disease
associations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Maximova:2018:SGP,
author = "Tatiana Maximova and Erion Plaku and Amarda Shehu",
title = "Structure-Guided Protein Transition Modeling with a
Probabilistic Roadmap Algorithm",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1783--1796",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2586044",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Proteins are macromolecules in perpetual motion,
switching between structural states to modulate their
function. A detailed characterization of the precise
yet complex relationship between protein structure,
dynamics, and function requires elucidating transitions
between functionally-relevant states. Doing so
challenges both wet and dry laboratories, as protein
dynamics involves disparate temporal scales. In this
paper, we present a novel, sampling-based algorithm to
compute transition paths. The algorithm exploits two
main ideas. First, it leverages known structures to
initialize its search and define a reduced conformation
space for rapid sampling. This is key to address the
insufficient sampling issue suffered by sampling-based
algorithms. Second, the algorithm embeds samples in a
nearest-neighbor graph where transition paths can be
efficiently computed via queries. The algorithm adapts
the probabilistic roadmap framework that is popular in
robot motion planning. In addition to efficiently
computing lowest-cost paths between any given
structures, the algorithm allows investigating
hypotheses regarding the order of experimentally-known
structures in a transition event. This novel
contribution is likely to open up new venues of
research. Detailed analysis is presented on
multiple-basin proteins of relevance to human disease.
Multiscaling and the AMBER ff14SB force field are used
to obtain energetically-credible paths at atomistic
detail.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2018:IBI,
author = "Bo Xu and Hongfei Lin and Yuan Lin and Yunlong Ma and
Liang Yang and Jian Wang and Zhihao Yang",
title = "Improve Biomedical Information Retrieval Using
Modified Learning to Rank Methods",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1797--1809",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2578337",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In these years, the number of biomedical articles has
increased exponentially, which becomes a problem for
biologists to capture all the needed information
manually. Information retrieval technologies, as the
core of search engines, can deal with the problem
automatically, providing users with the needed
information. However, it is a great challenge to apply
these technologies directly for biomedical retrieval,
because of the abundance of domain specific
terminologies. To enhance biomedical retrieval, we
propose a novel framework based on learning to rank.
Learning to rank is a series of state-of-the-art
information retrieval techniques, and has been proved
effective in many information retrieval tasks. In the
proposed framework, we attempt to tackle the problem of
the abundance of terminologies by constructing ranking
models, which focus on not only retrieving the most
relevant documents, but also diversifying the searching
results to increase the completeness of the resulting
list for a given query. In the model training, we
propose two novel document labeling strategies, and
combine several traditional retrieval models as
learning features. Besides, we also investigate the
usefulness of different learning to rank approaches in
our framework. Experimental results on TREC Genomics
datasets demonstrate the effectiveness of our framework
for biomedical information retrieval.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2018:DED,
author = "Hongbo Zhang and Lin Zhu and De-Shuang Huang",
title = "{DiscMLA}: an Efficient Discriminative {Motif}
Learning Algorithm over High-Throughput Datasets",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1810--1820",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2561930",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The transcription factors TFs can activate or suppress
gene expression by binding to specific sites, hence are
crucial regulatory elements for transcription.
Recently, series of discriminative motif finders have
been tailored to offering promising strategy for
harnessing the power of large quantities of accumulated
high-throughput experimental data. However, in order to
achieve high speed, these algorithms have to sacrifice
accuracy by employing simplified statistical models
during the searching process. In this paper, we propose
a novel approach named Discriminative Motif Learning
via AUC DiscMLA to discover motifs on high-throughput
datasets. Unlike previous approaches, DiscMLA tries to
optimize with a more comprehensive criterion AUC during
motifs searching. In addition, based on an experimental
observation of motif identification on large-scale
datasets, some novel procedures are designed to
accelerate DiscMLA. The experimental results on 52
real-world datasets demonstrate that our approach
substantially outperforms previous methods on
discriminative motif learning problems. DiscMLA'
stability, discriminability, and validity will help to
exploit high-throughput datasets and answer many
fundamental biological questions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kim:2018:ESP,
author = "Sun Kim",
title = "Editorial for Selected Papers of a {Joint Conferences,
Genome Informatics Workshop\slash International
Conference on Bioinformatics GIW\slash InCoB 2015}",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1821--1821",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2880126",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The four papers in this special section were presented
at the joint 2015 Genome Informatics
Workshop/International Conference on Bioinformatics
GIW/InCoB.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hirose:2018:SCT,
author = "Osamu Hirose and Shotaro Kawaguchi and Terumasa
Tokunaga and Yu Toyoshima and Takayuki Teramoto and
Sayuri Kuge and Takeshi Ishihara and Yuichi Iino and
Ryo Yoshida",
title = "{SPF-CellTracker}: Tracking Multiple Cells with
Strongly-Correlated Moves Using a Spatial Particle
Filter",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1822--1831",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2782255",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Tracking many cells in time-lapse 3D image sequences
is an important challenging task of bioimage
informatics. Motivated by a study of brain-wide 4D
imaging of neural activity in C. elegans, we present a
new method of multi-cell tracking. Data types to which
the method is applicable are characterized as follows:
i cells are imaged as globular-like objects, ii it is
difficult to distinguish cells on the basis of shape
and size only, iii the number of imaged cells in the
several-hundred range, iv movements of nearly-located
cells are strongly correlated, and v cells do not
divide. We developed a tracking software suite that we
call SPF-CellTracker. Incorporating dependency on the
cells' movements into the prediction model is the key
for reducing the tracking errors: the cell switching
and the coalescence of the tracked positions. We model
the target cells' correlated movements as a Markov
random field and we also derive a fast computation
algorithm, which we call spatial particle filter. With
the live-imaging data of the nuclei of C. elegans
neurons in which approximately 120 nuclei of neurons
were imaged, the proposed method demonstrated improved
accuracy compared to the standard particle filter and
the method developed by Tokunaga et al. 2014.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cheng:2018:EIC,
author = "Zhanzhan Cheng and Shuigeng Zhou and Yang Wang and Hui
Liu and Jihong Guan and Yi-Ping Phoebe Chen",
title = "Effectively Identifying Compound-Protein Interactions
by Learning from Positive and Unlabeled Examples",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1832--1843",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2570211",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Prediction of compound-protein interactions CPIs is to
find new compound-protein pairs where a protein is
targeted by at least a compound, which is a crucial
step in new drug design. Currently, a number of machine
learning based methods have been developed to predict
new CPIs in the literature. However, as there is not
yet any publicly available set of validated negative
CPIs, most existing machine learning based approaches
use the unknown interactions not validated CPIs
selected randomly as the negative examples to train
classifiers for predicting new CPIs. Obviously, this is
not quite reasonable and unavoidably impacts the CPI
prediction performance. In this paper, we simply take
the unknown CPIs as unlabeled examples, and propose a
new method called PUCPI the abbreviation of PU learning
for Compound-Protein Interaction identification that
employs biased-SVM Support Vector Machine to predict
CPIs using only positive and unlabeled examples. PU
learning is a class of learning methods that leans from
positive and unlabeled PU samples. To the best of our
knowledge, this is the first work that identifies CPIs
using only positive and unlabeled examples. We first
collect known CPIs as positive examples and then
randomly select compound-protein pairs not in the
positive set as unlabeled examples. For each
CPI/compound-protein pair, we extract protein domains
as protein features and compound substructures as
chemical features, then take the tensor product of the
corresponding compound features and protein features as
the feature vector of the CPI/compound-protein pair.
After that, biased-SVM is employed to train classifiers
on different datasets of CPIs and compound-protein
pairs. Experiments over various datasets show that our
method outperforms six typical classifiers, including
random forest, L1- and L2-regularized logistic
regression, naive Bayes, SVM and $k$-nearest neighbor
kNN, and three types of existing CPI prediction models.
More information can be found at
http://admis.fudan.edu.cn/projects/pucpi.html.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ismail:2018:RNR,
author = "Hamid D. Ismail and Hiroto Saigo and Dukka B. KC",
title = "{RF-NR}: Random Forest Based Approach for Improved
Classification of Nuclear Receptors",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1844--1852",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2773063",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The Nuclear Receptor NR superfamily plays an important
role in key biological, developmental, and
physiological processes. Developing a method for the
classification of NR proteins is an important step
towards understanding the structure and functions of
the newly discovered NR protein. The recent studies on
NR classification are either unable to achieve optimum
accuracy or are not designed for all the known NR
subfamilies. In this study, we developed RF-NR, which
is a Random Forest based approach for improved
classification of nuclear receptors. The RF-NR can
predict whether a query protein sequence belongs to one
of the eight NR subfamilies or it is a non-NR sequence.
The RF-NR uses spectrum-like features namely: Amino
Acid Composition, Di-peptide Composition, and
Tripeptide Composition. Benchmarking on two independent
datasets with varying sequence redundancy reduction
criteria, the RF-NR achieves better or comparable
accuracy than other existing methods. The added
advantage of our approach is that we can also obtain
biological insights about the important features that
are required to classify NR subfamilies. RF-NR is
freely available at http://bcb.ncat.edu/RF_NR/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tamura:2018:CMR,
author = "Takeyuki Tamura and Wei Lu and Jiangning Song and
Tatsuya Akutsu",
title = "Computing Minimum Reaction Modifications in a
{Boolean} Metabolic Network",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1853--1862",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2777456",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In metabolic network modification, we newly add
enzymes or/and knock-out genes to maximize the biomass
production with minimum side-effect. Although this
problem has been studied for various problem settings
via mathematical models including flux balance
analysis, elementary mode, and Boolean models, some
important problem settings still remain to be studied.
In this paper, we consider the Boolean Reaction
Modification BRM problem, where a host metabolic
network and a reference metabolic network are given in
the Boolean model. The host network initially produces
some toxic compounds and cannot produce some necessary
compounds, but the reference network can produce the
necessary compounds, and we should minimize the total
number of removed reactions from the host network and
added reactions from the reference network so that the
toxic compounds are not producible, but the necessary
compounds are producible in the resulting host network.
We developed integer linear programming ILP-based
methods for BRM, and compared them with OptStrain and
SimOptStrain. The results show that our method
performed better for reducing the total number of added
and removed reactions, while OptStrain and SimOptStrain
performed better for optimizing the production of the
target compound. Our developed software is freely
available at
``http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/solBRM/solBRM.html''.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tan:2018:SSS,
author = "Ying Tan and Yuhui Shi",
title = "Special Section on Swarm-Based Algorithms and
Applications in Computational Biology and
Bioinformatics",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1863--1864",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2879422",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The seven papers in this special section were
presented at the ICSI 2016 Conference. These articles
are primarily dealing with either novel bioinspired
swarm intelligence algorithms and their improvements
aswell as some practical applications inmulti-objective
optimization, network community detection, curve
fitting, and swarm robotics, etc.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Niu:2018:CSR,
author = "Ben Niu and Jing Liu and Teresa Wu and Xianghua Chu
and Zhengxu Wang and Yanmin Liu",
title = "Coevolutionary Structure-Redesigned-Based Bacterial
Foraging Optimization",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1865--1876",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2742946",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper presents a Coevolutionary
Structure-Redesigned-Based Bacteria Foraging
Optimization CSRBFO based on the natural phenomenon
that most living creatures tend to cooperate with each
other so as to fulfill tasks more effectively. Aiming
at lowering computational complexity while maintaining
the critical search capability of standard bacterial
foraging optimization BFO, we employ a general loop to
replace the nested loop and eliminate the reproduction
step of BFO. Hence, the proposed CSRBFO only consists
of two main steps: 1 chemotaxis and 2 elimination \&
dispersal. A coevolutionary strategy by which all
bacteria can learn from each other and search for
optima cooperatively is incorporated into the
chemotactic step to accelerate convergence and
facilitate accurate search. In the elimination \&
dispersal step, the three-stage evolutionary strategy
with different learning methods for maintaining
diversity is studied. An evaluation of the convergence
status is then added to determine whether bacteria
should move on to the next stage or not. The
combination of coevolutionary strategy and convergence
status evaluation is expected to balance exploration
and exploitation. Experimental results comparing seven
well-known heuristic algorithms on 24 benchmark
functions demonstrate that the proposed CSRBFO
outperforms the comparison algorithms significantly in
most of the cases.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2018:ESB,
author = "Biao Xu and Yong Zhang and Dunwei Gong and Yinan Guo
and Miao Rong",
title = "Environment Sensitivity-Based Cooperative
Co-Evolutionary Algorithms for Dynamic Multi-Objective
Optimization",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1877--1890",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2652453",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Dynamic multi-objective optimization problems DMOPs
not only involve multiple conflicting objectives, but
these objectives may also vary with time, raising a
challenge for researchers to solve them. This paper
presents a cooperative co-evolutionary strategy based
on environment sensitivities for solving DMOPs. In this
strategy, a new method that groups decision variables
is first proposed, in which all the decision variables
are partitioned into two subcomponents according to
their interrelation with environment. Adopting two
populations to cooperatively optimize the two
subcomponents, two prediction methods, i.e.,
differential prediction and Cauchy mutation, are then
employed respectively to speed up their responses on
the change of the environment. Furthermore, two
improved dynamic multi-objective optimization
algorithms, i.e., DNSGAII-CO and DMOPSO-CO, are
proposed by incorporating the above strategy into
NSGA-II and multi-objective particle swarm
optimization, respectively. The proposed algorithms are
compared with three state-of-the-art algorithms by
applying to seven benchmark DMOPs. Experimental results
reveal that the proposed algorithms significantly
outperform the compared algorithms in terms of
convergence and distribution on most DMOPs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Guo:2018:RDM,
author = "Yi-Nan Guo and Jian Cheng and Sha Luo and Dunwei Gong
and Yu Xue",
title = "Robust Dynamic Multi-Objective Vehicle Routing
Optimization Method",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1891--1903",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2685320",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "For dynamic multi-objective vehicle routing problems,
the waiting time of vehicle, the number of serving
vehicles, and the total distance of routes were
normally considered as the optimization objectives.
Except for the above objectives, fuel consumption that
leads to the environmental pollution and energy
consumption was focused on in this paper. Considering
the vehicles' load and the driving distance, a
corresponding carbon emission model was built and set
as an optimization objective. Dynamic multi-objective
vehicle routing problems with hard time windows and
randomly appeared dynamic customers, subsequently, were
modeled. In existing planning methods, when the new
service demand came up, global vehicle routing
optimization method was triggered to find the optimal
routes for non-served customers, which was
time-consuming. Therefore, a robust dynamic
multi-objective vehicle routing method with two-phase
is proposed . Three highlights of the novel method are:
i After finding optimal robust virtual routes for all
customers by adopting multi-objective particle swarm
optimization in the first phase, static vehicle routes
for static customers are formed by removing all dynamic
customers from robust virtual routes in next phase. ii
The dynamically appeared customers append to be served
according to their service time and the vehicles'
statues. Global vehicle routing optimization is
triggered only when no suitable locations can be found
for dynamic customers. iii A metric measuring the
algorithms robustness is given. The statistical results
indicated that the routes obtained by the proposed
method have better stability and robustness, but may be
sub-optimum. Moreover, time-consuming global vehicle
routing optimization is avoided as dynamic customers
appear.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Guo:2018:GPS,
author = "Weian Guo and Chengyong Si and Yu Xue and Yanfen Mao
and Lei Wang and Qidi Wu",
title = "A Grouping Particle Swarm Optimizer with
Personal-Best-Position Guidance for Large Scale
Optimization",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1904--1915",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2701367",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Particle Swarm Optimization PSO is a popular algorithm
which is widely investigated and well implemented in
many areas. However, the canonical PSO does not perform
well in population diversity maintenance so that
usually leads to a premature convergence or local
optima. To address this issue, we propose a variant of
PSO named Grouping PSO with Personal-Best-Position $
P_{best} $ Guidance GPSO-PG which maintains the
population diversity by preserving the diversity of
exemplars. On one hand, we adopt uniform random
allocation strategy to assign particles into different
groups and in each group the losers will learn from the
winner. On the other hand, we employ personal
historical best position of each particle in social
learning rather than the current global best particle.
In this way, the exemplars diversity increases and the
effect from the global best particle is eliminated. We
test the proposed algorithm to the benchmarks in CEC
2008 and CEC 2010, which concern the large scale
optimization problems LSOPs. By comparing several
current peer algorithms, GPSO-PG exhibits a competitive
performance to maintain population diversity and
obtains a satisfactory performance to the problems.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gao:2018:NCD,
author = "Chao Gao and Mingxin Liang and Xianghua Li and Zili
Zhang and Zhen Wang and Zhili Zhou",
title = "Network Community Detection Based on the
{Physarum}-Inspired Computational Framework",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1916--1928",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2638824",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Community detection is a crucial and essential problem
in the structure analytics of complex networks, which
can help us understand and predict the characteristics
and functions of complex networks. Many methods,
ranging from the optimization-based algorithms to the
heuristic-based algorithms, have been proposed for
solving such a problem. Due to the inherent complexity
of identifying network structure, how to design an
effective algorithm with a higher accuracy and a lower
computational cost still remains an open problem.
Inspired by the computational capability and positive
feedback mechanism in the wake of foraging process of
Physarum, a kind of slime, a general Physarum-based
computational framework for community detection is
proposed in this paper. Based on the proposed
framework, the inter-community edges can be identified
from the intra-community edges in a network and the
positive feedback of solving process in an algorithm
can be further enhanced, which are used to improve the
efficiency of original optimization-based and
heuristic-based community detection algorithms,
respectively. Some typical algorithms e.g., genetic
algorithm, ant colony optimization algorithm, and
Markov clustering algorithm and real-world datasets
have been used to estimate the efficiency of our
proposed computational framework. Experiments show that
the algorithms optimized by Physarum-inspired
computational framework perform better than the
original ones, in terms of accuracy and computational
cost.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Iglesias:2018:IAF,
author = "Andres Iglesias and Akemi Galvez and Andreina Avila",
title = "Immunological Approach for Full {NURBS} Reconstruction
of Outline Curves from Noisy Data Points in Medical
Imaging",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1929--1942",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2688444",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Curve reconstruction from data points is an important
issue for advanced medical imaging techniques, such as
computer tomography CT and magnetic resonance imaging
MRI. The most powerful fitting functions for this
purpose are the NURBS non-uniform rational B-splines.
Solving the general reconstruction problem with NURBS
requires computing all free variables of the problem
data parameters, breakpoints, control points, and their
weights. This leads to a very difficult non-convex,
nonlinear, high-dimensional, multimodal, and continuous
optimization problem. Previous methods simplify the
problem by guessing the values for some variables and
computing only the remaining ones. As a result,
unavoidable approximations errors are introduced. In
this paper, we describe the first method in the
literature to solve the full NURBS curve reconstruction
problem in all its generality. Our method is based on a
combination of two techniques: an immunological
approach to perform data parameterization, breakpoint
placement, and weight calculation, and least squares
minimization to compute the control points. This
procedure is repeated iteratively until no further
improvement is achieved for higher accuracy. The method
has been applied to reconstruct some outline curves
from MRI brain images with satisfactory results.
Comparative work shows that our method outperforms the
previous related approaches in the literature for all
instances in our benchmark.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tang:2018:SRS,
author = "Qirong Tang and Lu Ding and Fangchao Yu and Yuan Zhang
and Yinghao Li and Haibo Tu",
title = "Swarm Robots Search for Multiple Targets Based on an
Improved Grouping Strategy",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1943--1950",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2682161",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Swarm robots search for multiple targets in
collaboration in unknown environments has been
addressed in this paper. An improved grouping strategy
based on constriction factors Particle Swarm
Optimization is proposed. Robots are grouped under this
strategy after several iterations of stochastic
movements, which considers the influence range of
targets and environmental information they have sensed.
The group structure may change dynamically and each
group focuses on searching one target. All targets are
supposed to be found finally. Obstacle avoidance is
considered during the search process. Simulation
compared with previous method demonstrates the
adaptability, accuracy, and efficiency of the proposed
strategy in multiple targets searching.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Gao:2018:MMA,
author = "Xin Gao and Jake Y. Chen and Mohammed J. Zaki",
title = "Multiscale and Multimodal Analysis for Computational
Biology",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1951--1952",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2838658",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The papers in this special section were presented at
the 16th International Workshop on Data Mining in
Bioinformatics BIOKDD17. The BIOKDD17 Workshop was
organized in conjunction with the ACM SIGKDD
International Conference on Knowledge Discovery and
Data Mining on August 14, 2017 in Halifax, Canada. It
brought together international researchers in the
interacting disciplines of data mining, medical
informatics, and bioinformatics at the World Trade and
Convention Centre venue. The goal of this workshop is
to encourage Knowledge Discovery and Data mining KDD
researchers to take on the numerous challenges that
bioinformatics offers. Bioinformatics is the science of
managing, mining, and interpreting information from
biological data. Various genome projects have
contributed to an exponential growth in DNA and protein
sequence databases. Rapid advances in high-throughput
technologies, such as microarrays, mass spectrometry,
and new/next-generation sequencing, can monitor
quantitatively the presence or activity of thousands of
genes, RNAs, proteins, metabolites, and compounds in a
given biological state. The ongoing influx of these
data, the pressing need to address complex biomedical
challenges, and the gap between the two have
collectively created exciting opportunities for data
mining researchers.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Monteiro:2018:UML,
author = "Miguel Monteiro and Ana Catarina Fonseca and Ana
Teresa Freitas and Teresa {Pinho e Melo} and Alexandre
P. Francisco and Jose M. Ferro and Arlindo L.
Oliveira",
title = "Using Machine Learning to Improve the Prediction of
Functional Outcome in Ischemic Stroke Patients",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1953--1959",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2811471",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Ischemic stroke is a leading cause of disability and
death worldwide among adults. The individual prognosis
after stroke is extremely dependent on treatment
decisions physicians take during the acute phase. In
the last five years, several scores such as the ASTRAL,
DRAGON, and THRIVE have been proposed as tools to help
physicians predict the patient functional outcome after
a stroke. These scores are rule-based classifiers that
use features available when the patient is admitted to
the emergency room. In this paper, we apply machine
learning techniques to the problem of predicting the
functional outcome of ischemic stroke patients, three
months after admission. We show that a pure machine
learning approach achieves only a marginally superior
Area Under the ROC Curve AUC $ 0.808 \pm 0.085 $ than
that of the best score $ 0.771 \pm 0.056 $ when using
the features available at admission. However, we
observed that by progressively adding features
available at further points in time, we can
significantly increase the AUC to a value above 0.90.
We conclude that the results obtained validate the use
of the scores at the time of admission, but also point
to the importance of using more features, which require
more advanced methods, when possible.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2018:AMR,
author = "Annie Wang and Hansaim Lim and Shu-Yuan Cheng and Lei
Xie",
title = "{ANTENNA}, a Multi-Rank, Multi-Layered Recommender
System for Inferring Reliable Drug-Gene-Disease
Associations: Repurposing Diazoxide as a Targeted
Anti-Cancer Therapy",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1960--1967",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2812189",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Existing drug discovery processes follow a
reductionist model of
``one-drug-one-gene-one-disease,'' which is inadequate
to tackle complex diseases involving multiple
malfunctioned genes. The availability of big omics data
offers opportunities to transform drug discovery
process into a new paradigm of systems pharmacology
that focuses on designing drugs to target molecular
interaction networks instead of a single gene. Here, we
develop a reliable multi-rank, multi-layered
recommender system, ANTENNA, to mine large-scale
chemical genomics and disease association data for
prediction of novel drug-gene-disease associations.
ANTENNA integrates a novel tri-factorization based
dual-regularized weighted and imputed One Class
Collaborative Filtering OCCF algorithm, tREMAP, with a
statistical framework based on Random Walk with Restart
and assess the reliability of specific predictions. In
the benchmark, tREMAP clearly outperforms the
single-rank OCCF. We apply ANTENNA to a real-world
problem: repurposing old drugs for new clinical
indications without effective treatments. We discover
that FDA-approved drug diazoxide can inhibit multiple
kinase genes responsible for many diseases including
cancer and kill triple negative breast cancer TNBC
cells efficiently $ {\text {IC}}_{50} = {{0.87}} \,
{{\mu } \text {M}} $. TNBC is a deadly disease without
effective targeted therapies. Our finding demonstrates
the power of big data analytics in drug discovery and
developing a targeted therapy for TNBC.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2018:PHR,
author = "Haishuai Wang and Zhicheng Cui and Yixin Chen and
Michael Avidan and Arbi {Ben Abdallah} and Alexander
Kronzer",
title = "Predicting Hospital Readmission via Cost-Sensitive
Deep Learning",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1968--1978",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2827029",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "With increased use of electronic medical records EMRs,
data mining on medical data has great potential to
improve the quality of hospital treatment and increase
the survival rate of patients. Early readmission
prediction enables early intervention, which is
essential to preventing serious or life-threatening
events, and act as a substantial contributor to reduce
healthcare costs. Existing works on predicting
readmission often focus on certain vital signs and
diseases by extracting statistical features. They also
fail to consider skewness of class labels in medical
data and different costs of misclassification errors.
In this paper, we recur to the merits of convolutional
neural networks CNN to automatically learn features
from time series of vital sign, and categorical feature
embedding to effectively encode feature vectors with
heterogeneous clinical features, such as demographics,
hospitalization history, vital signs, and laboratory
tests. Then, both learnt features via CNN and
statistical features via feature embedding are fed into
a multilayer perceptron MLP for prediction. We use a
cost-sensitive formulation to train MLP during
prediction to tackle the imbalance and skewness
challenge. We validate the proposed approach on two
real medical datasets from Barnes-Jewish Hospital, and
all data is taken from historical EMR databases and
reflects the kinds of data that would realistically be
available at the clinical prediction system in
hospitals. We find that early prediction of readmission
is possible and when compared with state-of-the-art
existing methods used by hospitals, our methods perform
significantly better. For example, using the general
hospital wards data for 30-day readmission prediction,
the area under the curve AUC for the proposed model was
0.70, significantly higher than all the baseline
methods. Based on these results, a system is being
deployed in hospital settings with the proposed
forecasting algorithms to support treatment.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Halioui:2018:BWE,
author = "Ahmed Halioui and Petko Valtchev and Abdoulaye Banire
Diallo",
title = "Bioinformatic Workflow Extraction from Scientific
Texts based on Word Sense Disambiguation",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1979--1990",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2847336",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper introduces a method for automatic workflow
extraction from texts using Process-Oriented Case-Based
Reasoning POCBR. While the current workflow management
systems implement mostly different complicated
graphical tasks based on advanced distributed solutions
e.g., cloud computing and grid computation, workflow
knowledge acquisition from texts using case-based
reasoning represents more expressive and semantic case
representations. We propose in this context, an
ontology-based workflow extraction framework to acquire
processual knowledge from texts. Our methodology
extends the classic NLP techniques to extract and
disambiguate complex tasks and relations in texts.
Using a graph-based representation of workflows and a
domain ontology, our extraction process uses a
context-aware approach to recognize workflow components
in texts: data and control flows. We applied our
framework in a technical domain in bioinformatics:
i.e., phylogenetic analyses. An evaluation based on
workflow semantic similarities in a gold standard
proves that our approach provides promising results in
the process extraction domain. Both data and
implementation of our framework are available in:
http://labo.bioinfo.uqam.ca/tgowler.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yue:2018:SGS,
author = "Zongliang Yue and Michael T. Neylon and Thanh Nguyen
and Timothy Ratliff and Jake Y. Chen",
title = "{``Super Gene Set''} Causal Relationship Discovery
from Functional Genomics Data",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1991--1998",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2858755",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this article, we present a computational framework
to identify ``causal relationships'' among super gene
sets. For ``causal relationships,'' we refer to both
stimulatory and inhibitory regulatory relationships,
regardless of through direct or indirect mechanisms.
For super gene sets, we refer to ``pathways, annotated
lists, and gene signatures,'' or PAGs. To identify
causal relationships among PAGs, we extend the previous
work on identifying PAG-to-PAG regulatory relationships
by further requiring them to be significantly enriched
with gene-to-gene co-expression pairs across the two
PAGs involved. This is achieved by developing a
quantitative metric based on PAG-to-PAG Co-expressions
PPC, which we use to infer the likelihood that
PAG-to-PAG relationships under examination are
causal-either stimulatory or inhibitory. Since true
causal relationships are unknown, we approximate the
overall performance of inferring causal relationships
with the performance of recalling known r-type
PAG-to-PAG relationships from causal PAG-to-PAG
inference, using a functional genomics benchmark
dataset from the GEO database. We report the
area-under-curve AUC performance for both precision and
recall being 0.81. By applying our framework to a
myeloid-derived suppressor cells MDSC dataset, we
further demonstrate that this framework is effective in
helping build multi-scale biomolecular systems models
with new insights on regulatory and causal links for
downstream biological interpretations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Alazmi:2018:SBD,
author = "Meshari Alazmi and Ahmed Abbas and Xianrong Guo and
Ming Fan and Lihua Li and Xin Gao",
title = "A Slice-based $^{13}$C-detected {NMR} Spin System
Forming and Resonance Assignment Method",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "1999--2008",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2849728",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Nuclear magnetic resonance NMR spectroscopy is
attracting more attention in the field of computational
structural biology. Till recently, $^1$H-detected
experiments are the dominant NMR technique used due to
the high sensitivity of $^1$H nuclei. However, the
current availability of high magnetic fields and
cryogenically cooled probe heads allow researchers to
overcome the low sensitivity of $^{13}$C nuclei.
Consequently, $^{13}$C-detected experiments have become
a popular technique in different NMR applications
especially resonance assignment and structure
determination of large proteins. In this paper, we
propose the first spin system forming method for
$^{13}$C-detected NMR spectra. Our method is able to
accurately form spin systems based on as few as two
$^{13}$C-detected spectra, CBCACON, and CBCANCO. Our
method picks slices from the more trusted spectrum and
uses them as feedback to direct the slice picking in
the less trusted one. This feedback leads to picking
the accurate slices that consequently helps to form
better spin systems. We tested our method on a real
dataset of `Ubiquitin' and a benchmark simulated
dataset consisting of 12 proteins. We fed our spin
systems as inputs to a genetic algorithm to generate
the chemical shift assignment, and obtained 92 percent
correct chemical shift assignment for Ubiquitin. For
the simulated dataset, we obtained an average recall of
86 percent and an average precision of 88 percent.
Finally, our chemical shift assignment of Ubiquitin was
given as an input to CS-ROSETTA server that generated
structures close to the experimentally determined
structure.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mukund:2018:CEN,
author = "Kavitha Mukund and Samuel R. Ward and Richard L.
Lieber and Shankar Subramaniam",
title = "Co-Expression Network Approach to Studying the Effects
of {Botulinum Neurotoxin-A}",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "2009--2016",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2763949",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Botulinum Neurotoxin A BoNT-A is a potent neurotoxin
with several clinical applications. The goal of this
study was to utilize co-expression network theory to
analyze temporal transcriptional data from skeletal
muscle after BoNT-A treatment. Expression data for 2000
genes extracted using a ranking heuristic served as the
basis for this analysis. Using weighted gene
co-expression network analysis WGCNA, we identified 19
co-expressed modules, further hierarchically clustered
into five groups. Quantifying average expression and
co-expression patterns across these groups revealed
temporal aspects of muscle's response to BoNT-A.
Functional analysis revealed enrichment of group 1 with
metabolism; group 5 with contradictory functions of
atrophy and cellular recovery; and groups 2 and 3 with
extracellular matrix ECM and non-fast fiber isoforms.
Topological positioning of two highly ranked,
significantly expressed genes-Dclk1 and Ostalpha-within
group 5 suggested possible mechanistic roles in
recovery from BoNT-A induced atrophy. Phenotypic
correlations of groups with titin and myosin protein
content further emphasized the effect of BoNT-A on the
sarcomeric contraction machinery in early phase of
chemodenervation. In summary, our approach revealed a
hierarchical functional response to BoNT-A induced
paralysis with early metabolic and later ECM responses
and identified putative biomarkers associated with
chemodenervation. Additionally, our results provide an
unbiased validation of the response documented in our
previous work.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Martins:2018:CMT,
author = "Daniel P. Martins and Michael Taynnan Barros and
Massimiliano Pierobon and Meenakshisundaram Kandhavelu
and Pietro Lio' and Sasitharan Balasubramaniam",
title = "Computational Models for Trapping {Ebola} Virus Using
Engineered Bacteria",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "2017--2027",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2836430",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The outbreak of the Ebola virus in recent years has
resulted in numerous research initiatives to seek new
solutions to contain the virus. A number of approaches
that have been investigated include new vaccines to
boost the immune system. An alternative post-exposure
treatment is presented in this paper. The proposed
approach for clearing the Ebola virus can be developed
through a microfluidic attenuator, which contains the
engineered bacteria that traps Ebola flowing through
the blood onto its membrane. The paper presents the
analysis of the chemical binding force between the
virus and a genetically engineered bacterium
considering the opposing forces acting on the
attachment point, including hydrodynamic tension and
drag force. To test the efficacy of the technique,
simulations of bacterial motility within a confined
area to trap the virus were performed. More than 60
percent of the displaced virus could be collected
within 15 minutes. While the proposed approach
currently focuses on in vitro environments for trapping
the virus, the system can be further developed into a
future treatment system whereby blood can be cycled out
of the body into a microfluidic device that contains
the engineered bacteria to trap viruses.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2018:GGS,
author = "Juntao Li and Wenpeng Dong and Deyuan Meng",
title = "Grouped Gene Selection of Cancer via Adaptive Sparse
Group Lasso Based on Conditional Mutual Information",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "2028--2038",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2761871",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper deals with the problems of cancer
classification and grouped gene selection. The weighted
gene co-expression network on cancer microarray data is
employed to identify modules corresponding to
biological pathways, based on which a strategy of
dividing genes into groups is presented. Using the
conditional mutual information within each divided
group, an integrated criterion is proposed and the
data-driven weights are constructed. They are shown
with the ability to evaluate both the individual gene
significance and the influence to improve correlation
of all the other pairwise genes in each group.
Furthermore, an adaptive sparse group lasso is
proposed, by which an improved blockwise descent
algorithm is developed. The results on four cancer data
sets demonstrate that the proposed adaptive sparse
group lasso can effectively perform classification and
grouped gene selection.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wu:2018:NCG,
author = "Ho-Chun Wu and Xi-Guang Wei and Shing-Chow Chan",
title = "Novel Consensus Gene Selection Criteria for
Distributed {GPU} Partial Least Squares-Based Gene
Microarray Analysis in Diffused {Large B Cell Lymphoma
DLBCL} and Related Findings",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "2039--2052",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2760827",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper proposes a novel consensus gene selection
criteria for partial least squares-based gene
microarray analysis. By quantifying the extent of
consistency and distinctiveness of the differential
gene expressions across different double cross
validations CV or randomizations in terms of occurrence
and randomization p-values, the proposed criteria are
able to identify a more comprehensive genes associated
with the underlying disease. A Distributed GPU
implementation has been proposed to accelerate the gene
selection problem and about 8-11 times speed up has
been achieved based on the microarray datasets
considered. Simulation results using various cancer
gene microarray datasets show that the proposed
approach is able to achieve highly comparable
classification accuracy in comparing with many
conventional approaches. Furthermore, enrichment
analysis on the selected genes for Diffused Large B
Cell Lymphoma DLBCL and Prostate Cancer datasets and
show that only the proposed approach is able to
identify gene lists enriched in different pathways with
significant p-values. In contrast, sufficient
statistical significance cannot be found for
conventional SVM-RFE and the t-test. The reliability in
identifying and establishing statistical significance
of the gene findings makes the proposed approach an
attractive alternative for cancer related researches
based on gene expression profiling or other similar
data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Biswas:2018:BOR,
author = "Surama Biswas and Sriyankar Acharyya",
title = "A Bi-Objective {RNN} Model to Reconstruct Gene
Regulatory Network: a Modified Multi-Objective
Simulated Annealing Approach",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "2053--2059",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2771360",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gene Regulatory Network GRN is a virtual network in a
cellular context of an organism, comprising a set of
genes and their internal relationships to regulate
protein production rate gene expression level of each
other through coded proteins. Computational
Reconstruction of GRN from gene expression data is a
widely-applied research area. Recurrent Neural Network
RNN is a useful modeling scheme for GRN reconstruction.
In this research, the RNN formulation of GRN
reconstruction having single objective function has
been modified to incorporate a new objective function.
An existing multi-objective meta-heuristic algorithm,
called Archived Multi Objective Simulated Annealing
AMOSA, has been modified and applied to this
bi-objective RNN formulation. Executing the resulting
algorithm called AMOSA-GRN on a gene expression
dataset, a collection termed as Archive of
non-dominated GRNs has been obtained. Ensemble
averaging has been applied on the archives, and
obtained through a sequence of executions of AMOSA-GRN.
Accuracy of GRNs in the averaged archive, with respect
to gold standard GRN, varies in the range 0.875 --- 1.0
87.5 --- 100 percent.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Djeddi:2018:NCA,
author = "Warith Eddine Djeddi and Sadok {Ben Yahia} and
Engelbert Mephu Nguifo",
title = "A Novel Computational Approach for Global Alignment
for Multiple Biological Networks",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "2060--2066",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2808529",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Due to the rapid progress of biological networks for
modeling biological systems, a lot of biomolecular
networks have been producing more and more
protein-protein interaction PPI data. Analyzing
protein-protein interaction networks aims to find
regions of topological and functional dissimilarities
between molecular networks of different species. The
study of PPI networks has the potential to teach us as
much about life process and diseases at the molecular
level. Although few methods have been developed for
multiple PPI network alignment and thus, new network
alignment methods are of a compelling need. In this
paper, we propose a novel algorithm for a global
alignment of multiple protein-protein interaction
networks called MAPPIN. The latter relies on
information available for the proteins in the networks,
such as sequence, function, and network topology. Our
algorithm is perfectly designed to exploit current
multi-core CPU architectures, and has been extensively
tested on a real data eight species. Our experimental
results show that MAPPIN significantly outperforms
NetCoffee in terms of coverage. Nevertheless, MAPPIN is
handicapped by the time required to load the gene
annotation file. An extensive comparison versus the
pioneering PPI methods also show that MAPPIN is often
efficient in terms of coverage, mean entropy, or mean
normalized.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Frith:2018:SDC,
author = "Martin C. Frith and Anish M. S. Shrestha",
title = "A Simplified Description of Child Tables for Sequence
Similarity Search",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "2067--2073",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2796064",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Finding related nucleotide or protein sequences is a
fundamental, diverse, and incompletely-solved problem
in bioinformatics. It is often tackled by
seed-and-extend methods, which first find ``seed''
matches of diverse types, such as spaced seeds, subset
seeds, or minimizers. Seeds are usually found using an
index of the reference sequences, which stores seed
positions in a suffix array or related data structure.
A child table is a fundamental way to achieve fast
lookup in an index, but previous descriptions have been
overly complex. This paper aims to provide a more
accessible description of child tables, and demonstrate
their generality: they apply equally to all the
above-mentioned seed types and more. We also show that
child tables can be used without LCP longest common
prefix tables, reducing the memory requirement.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2018:AIP,
author = "Xizhe Zhang",
title = "Altering Indispensable Proteins in Controlling
Directed Human Protein Interaction Network",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "2074--2078",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2796572",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The numerous interconnections within complex systems
enable us to control networks towards a desired state
through a few suitable selected nodes, which are called
driver nodes. Recent works analyzed directed human
Protein-Protein Interaction PPI network based on
structural control theory. They found that
indispensable proteins, whose removal increase the
number of driver nodes, are the primary targets of
human viruses and drugs. However, the human PPI network
is usually incomplete and may include many
false-positive or false-negative interactions. That
prompts us to ask whether these indispensable proteins
are stable to possible structural changes. Here, we
present a method to alter the type of indispensable
proteins and thereby investigate the stability of
indispensable proteins. By comparing the sets of
indispensable proteins before and after structural
changes to the network, we find that very few added or
removed interactions can change the type of many
indispensable nodes. Furthermore, some indispensable
proteins are very sensitive to structural changes and
have significantly lower interactions than the other
indispensable proteins. The results indicate that
indispensable proteins are sensitive to structural
changes. Therefore, approaches based on structural
control theory should be used with caution because of
the incomplete nature of these networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2018:IGN,
author = "Ting Xu and Le Ou-Yang and Xiaohua Hu and Xiao-Fei
Zhang",
title = "Identifying Gene Network Rewiring by Integrating Gene
Expression and Gene Network Data",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "2079--2085",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2809603",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Exploring the rewiring pattern of gene regulatory
networks between different pathological states is an
important task in bioinformatics. Although a number of
computational approaches have been developed to infer
differential networks from high-throughput data, most
of them only focus on gene expression data. The
valuable static gene regulatory network data
accumulated in recent biomedical researches are
neglected. In this study, we propose a new Gaussian
graphical model-based method to infer differential
networks by integrating gene expression and static gene
regulatory network data. We first evaluate the
empirical performance of our method by comparing with
the state-of-the-art methods using simulation data. We
also apply our method to The Cancer Genome Atlas data
to identify gene network rewiring between ovarian
cancers with different platinum responses, and rewiring
between breast cancers of luminal A subtype and
basal-like subtype. Hub genes in the estimated
differential networks rediscover known genes associated
with platinum resistance in ovarian cancer and
signatures of the breast cancer intrinsic subtypes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jiao:2018:RGC,
author = "Hongmei Jiao and Liping Zhang and Qikun Shen and Junwu
Zhu and Peng Shi",
title = "Robust Gene Circuit Control Design for Time-Delayed
Genetic Regulatory Networks Without {SUM} Regulatory
Logic",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "2086--2093",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2825445",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper investigates the gene circuit control
design problem of time-delayed genetic regulatory
networks. In the genetic regulatory networks, the time
delays are unknown constants, and the genetic
regulatory is not conventional SUM regulatory logic and
can be modeled to be an unknown nonlinear function of
the time-delayed states of the other genes in a cell.
By Lyapunov stability, a novel adaptive gene circuit
control design approach is proposed for the genetic
regulatory networks, where the unknown time delays are
estimated online by adaptive algorithms and the unknown
regulatory functions are approximated by neural
networks. The design approach in this paper is
delay-dependent and has less conservatism than the
delay-independent approach. From theoretical analysis,
the closed-loop system is asymptotically stable and all
the signals in the system converge to an adjustable
neighborhood of the origin. Finally, a numerical
example is given to show the effectiveness of the new
design approach.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Luhmann:2018:SAC,
author = "Nina Luhmann and Cedric Chauve and Jens Stoye and
Roland Wittler",
title = "Scaffolding of Ancient Contigs and Ancestral
Reconstruction in a Phylogenetic Framework",
journal = j-TCBB,
volume = "15",
number = "6",
pages = "2094--2100",
month = nov,
year = "2018",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2816034",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Dec 26 18:59:16 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Ancestral genome reconstruction is an important task
to analyze the evolution of genomes. Recent progress in
sequencing ancient DNA led to the publication of
so-called paleogenomes and allows the integration of
this sequencing data in genome evolution analysis.
However, the de novo assembly of ancient genomes is
usually fragmented due to DNA degradation over time
among others. Integrated phylogenetic assembly
addresses the issue of genome fragmentation in the
ancient DNA assembly while aiming to improve the
reconstruction of all ancient genomes in the phylogeny
simultaneously. The fragmented assembly of the ancient
genome can be represented as an assembly graph,
indicating contradicting ordering information of
contigs. In this setting, our approach is to compare
the ancient data with extant finished genomes. We
generalize a reconstruction approach minimizing the
Single-Cut-or-Join rearrangement distance towards
multifurcating trees and include edge lengths to
improve the reconstruction in practice. This results in
a polynomial time algorithm that includes additional
ancient DNA data at one node in the tree, resulting in
consistent reconstructions of ancestral genomes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yamanishi:2019:GEA,
author = "Yoshihiro Yamanishi and Yasubumi Sakakibara and
Yi-Ping Phoebe Chen",
title = "Guest Editorial for the {16th Asia Pacific
Bioinformatics Conference}",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "1--2",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2856940",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The eight papers in this special section were
presented at the 16th Asia Pacific Bioinformatics
Conference APBC2018, which was held in Yokohama, Japan,
15-17 January 2018. The aim of this conference is to
provide an international forum for researchers,
professionals, and industrial practitioners to share
their knowledge and ideas of how to surf the tidal wave
of information in the area of bioinformatics and
computational biology.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kimura:2019:PCB,
author = "Kouichi Kimura and Asako Koike",
title = "Parallel Computation of the {Burrows--Wheeler}
Transform of Short Reads Using Prefix Parallelism",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "3--13",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2837749",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The Burrows--Wheeler transform BWT of short-read data
has unexplored potential utilities, such as for
efficient and sensitive variation analysis against
multiple reference genome sequences, because it does
not depend on any particular reference genome sequence,
unlike conventional mapping-based methods. However,
since the amount of read data is generally much larger
than the size of the reference sequence, computation of
the BWT of reads is not easy, and this hampers
development of potential applications. For the
alleviation of this problem, a new method of computing
the BWT of reads in parallel is proposed. The BWT,
corresponding to a sorted list of suffixes of reads, is
constructed incrementally by successively including
longer and longer suffixes. The working data is divided
into more than 10,000 ``blocks'' corresponding to
sublists of suffixes with the same prefixes. Thousands
of groups of blocks can be processed in parallel while
making exclusive writes and concurrent reads into a
shared memory. Reads and writes are basically
sequential, and the read concurrency is limited to two.
Thus, a fine-grained parallelism, referred to as prefix
parallelism, is expected to work efficiently. The time
complexity for processing $n$ reads of length $ \ell $
is $ O n \ell^2$. On actual biological DNA sequence
data of about 100 Gbp with a read length of 100 bp base
pairs, a tentative implementation of the proposed
method took less than an hour on a single-node
computer; i.e., it was about three times faster than
one of the fastest programs developed so far.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Haack:2019:CDS,
author = "Jordan Haack and Eli Zupke and Andrew Ramirez and
Yi-Chieh Wu and Ran Libeskind-Hadas",
title = "Computing the Diameter of the Space of Maximum
Parsimony Reconciliations in the
Duplication--Transfer--Loss Model",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "14--22",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2849732",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Phylogenetic tree reconciliation is widely used in the
fields of molecular evolution, cophylogenetics,
parasitology, and biogeography to study the
evolutionary histories of pairs of entities. In these
contexts, reconciliation is often performed using
maximum parsimony under the Duplication-Transfer-Loss
DTL event model. In general, the number of maximum
parsimony reconciliations MPRs can grow exponentially
with the size of the trees. While a number of previous
efforts have been made to count the number of MPRs,
find representative MPRs, and compute the frequencies
of events across the space of MPRs, little is known
about the structure of MPR space. In particular, how
different are MPRs in terms of the events that they
comprise? One way to address this question is to
compute the diameter of MPR space, defined to be the
maximum number of DTL events that distinguish any two
MPRs in the solution space. We show how to compute the
diameter of MPR space in polynomial time and then apply
this algorithm to a large biological dataset to study
the variability of events.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rizzi:2019:HCA,
author = "Romeo Rizzi and Massimo Cairo and Veli Makinen and
Alexandru I. Tomescu and Daniel Valenzuela",
title = "Hardness of Covering Alignment: Phase Transition in
Post-Sequence Genomics",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "23--30",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2831691",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Covering alignment problems arise from recent
developments in genomics; so called pan-genome graphs
are replacing reference genomes, and advances in
haplotyping enable full content of diploid genomes to
be used as basis of sequence analysis. In this paper,
we show that the computational complexity will change
for natural extensions of alignments to pan-genome
representations and to diploid genomes. More broadly,
our approach can also be seen as a minimal extension of
sequence alignment to labelled directed acyclic graphs
labeled DAGs. Namely, we show that finding a covering
alignment of two labeled DAGs is NP-hard even on binary
alphabets. A covering alignment asks for two paths $
R_1 $ red and $ G_1 $ green in DAG $ D_1 $ and two
paths $ R_2 $ red and $ G_2 $ green in DAG $ D_2 $ that
cover the nodes of the graphs and maximize the sum of
the global alignment scores: $ \mathsf {as} \mathsf
{sp}R_1, \mathsf {sp}R_2 + \mathsf {as} \mathsf
{sp}G_1, \mathsf {sp}G_2 $, where $ \mathsf {sp}P $ is
the concatenation of labels on the path $P$. Pair-wise
alignment of haplotype sequences forming a diploid
chromosome can be converted to a two-path coverable
labelled DAG, and then the covering alignment models
the similarity of two diploids over arbitrary
recombinations. We also give a reduction to the other
direction, to show that such a recombination-oblivious
diploid alignment is NP-hard on alphabets of size 3.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mizera:2019:TAA,
author = "Andrzej Mizera and Jun Pang and Hongyang Qu and Qixia
Yuan",
title = "Taming Asynchrony for Attractor Detection in Large
{Boolean} Networks",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "31--42",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2850901",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Boolean networks is a well-established formalism for
modelling biological systems. A vital challenge for
analyzing a Boolean network is to identify all the
attractors. This becomes more challenging for large
asynchronous Boolean networks, due to the asynchronous
scheme. Existing methods are prohibited due to the
well-known state-space explosion problem in large
Boolean networks. In this paper, we tackle this
challenge by proposing a SCC-based decomposition
method. We prove the correctness of our proposed method
and demonstrate its efficiency with two real-life
biological networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2019:AIR,
author = "Tiancong Wang and Bin Ma",
title = "Adjacent {Y}-Ion Ratio Distributions and Its
Application in Peptide Sequencing",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "43--51",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2864647",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A scoring function plays a critical role in software
for peptide identification with mass spectrometry. We
present a general scoring feature that can be
incorporated in the scoring functions of other peptide
identification software. The scoring feature is based
on the intensity ratios between two adjacent y-ions in
the spectrum. A method is proposed to obtain the
probability distributions of such ratios, and to
calculate the scoring feature based on the
distributions. To demonstrate the performance of the
method, the new feature is incorporated with X!Tandem
[1] , [2] and Novor [3] and significantly improved the
database search and de novo sequencing performances on
the testing data, respectively.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hartmann:2019:EAS,
author = "Tom Hartmann and Matthias Bernt and Martin
Middendorf",
title = "An Exact Algorithm for Sorting by Weighted Preserving
Genome Rearrangements",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "52--62",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2831661",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The preserving Genome Sorting Problem pGSP asks for a
shortest sequence of rearrangement operations that
transforms a given gene order into another given gene
order by using rearrangement operations that preserve
common intervals, i.e., groups of genes that form an
interval in both given gene orders. The wpGSP is the
weighted version of the problem were each type of
rearrangement operation has a weight and a minimum
weight sequence of rearrangement operations is sought.
An exact algorithm --- called CREx2 --- is presented,
which solves the wpGSP for arbitrary gene orders and
the following types of rearrangement operations:
inversions, transpositions, inverse transpositions, and
tandem duplication random loss operations. CREx2 has a
worst case exponential runtime, but a linear runtime
for problem instances where the common intervals are
organized in a linear structure. The efficiency of
CREx2 and its usefulness for phylogenetic analysis is
shown empirically for gene orders of fungal
mitochondrial genomes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2019:IRF,
author = "Lei Li and Mukul S. Bansal",
title = "An Integrated Reconciliation Framework for Domain,
Gene, and Species Level Evolution",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "63--76",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2846253",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The majority of genes in eukaryotes consists of one or
more protein domains that can be independently lost or
gained during evolution. This gain and loss of protein
domains, through domain duplications, transfers, or
losses, has important evolutionary and functional
consequences. Yet, even though it is well understood
that domains evolve inside genes and genes inside
species, there do not exist any computational
frameworks to simultaneously model the evolution of
domains, genes, and species and account for their
inter-dependency. Here, we develop an integrated model
of domain evolution that explicitly captures the
interdependence of domain-, gene-, and species-level
evolution. Our model extends the classical phylogenetic
reconciliation framework, which infers gene family
evolution by comparing gene trees and species trees, by
explicitly considering domain-level evolution and
decoupling domain-level events from gene-level events.
In this paper, we i introduce the new integrated
reconciliation framework, ii prove that the associated
optimization problem is NP-hard, iii devise an
efficient heuristic solution for the problem, iv apply
our algorithm to a large biological dataset, and v
demonstrate the impact of using our new computational
framework compared to existing approaches. The
implemented software is freely available from
http://compbio.engr.uconn.edu/software/seadog/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nishiyama:2019:RCN,
author = "Yuhei Nishiyama and Aleksandar Shurbevski and Hiroshi
Nagamochi and Tatsuya Akutsu",
title = "Resource Cut, a New Bounding Procedure to Algorithms
for Enumerating Tree-Like Chemical Graphs",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "77--90",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2832061",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Enumerating chemical compounds with given structural
properties plays an important role in structure
elucidation, with applications such as drug design. We
focus on the problem of enumerating tree-like chemical
graphs specified by upper and lower bounds on feature
vectors, where chemical graphs represent compounds, and
a feature vector characterizes frequencies of finite
paths in a graph. Building on the branch-and-bound
algorithm proposed in earlier work, we propose a new
bounding procedure, called Resource Cut, to speed up
the enumeration process. Tree-like chemical graphs are
modeled as vertex-colored trees, colors representing
chemical elements. The algorithm is based on a scheme
of generating each unique colored tree with a specified
number $n$ of vertices. A colored tree is constructed
by repeatedly appending vertices. Given a set $
\mathcal {R}$ of $n$ colored vertices, we found that
the algorithm often constructs trees that cannot be
extended to a unique representation of a colored tree
no matter how the remaining unused colored vertices in
the set $ \mathcal {R}$ are appended. We derive a
mathematical condition to detect and discard such
trees. Experimental results show that Resource Cut
significantly reduces the search space. We have been
able to obtain exact numbers of chemical graphs with up
to 17 vertices excluding hydrogen atoms.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Shen:2019:MSS,
author = "Bairong Shen and Xiaoqian Jiang and Xingming Zhao",
title = "Modeling and Simulation Studies of Complex Biological
Systems for Precision Medicine and Healthcare",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "91--92",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2850078",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The papers in this special section focus on modeling
and simulation research of complex biological systems
that are forged by precision medicine and healthcare.
Recently, big data driven precision medicine has become
one of the frontiers in biomedical study, but the
complex diseases caused by interactions between genes,
environments, and lifestyles are still difficult to be
understood by traditional methods. Although we have
more and more high-throughput molecular data measured
and accumulated, we are still lacking fine and
personalized clinical phenotype data. There is a long
way to go from data to precision medicine/ healthcare,
since the biomedical process is dynamic, evolutionary,
and systematic. It is a big challenge to make these big
data useful to the precision prognosis, diagnosis, and
treatment of complex disease. Modeling and simulation
will be an essential and important method to the
investigation of the mechanisms and dynamic evolution
of complex diseases even with big data available. The
prevention and the early diagnosis of complex diseases
will be essential to the coming aging society. The
shifting from clinical management to precision
healthcare will be also the next challenge for
scientific researches.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sadat:2019:SSG,
author = "Md Nazmus Sadat and Md Momin {Al Aziz} and Noman
Mohammed and Feng Chen and Xiaoqian Jiang and Shuang
Wang",
title = "{SAFETY: Secure gwAs in Federated Environment through
a hYbrid Solution}",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "93--102",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2829760",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recent studies demonstrate that effective healthcare
can benefit from using the human genomic information.
Consequently, many institutions are using statistical
analysis of genomic data, which are mostly based on
genome-wide association studies GWAS. GWAS analyze
genome sequence variations in order to identify genetic
risk factors for diseases. These studies often require
pooling data from different sources together in order
to unravel statistical patterns, and relationships
between genetic variants and diseases. Here, the
primary challenge is to fulfill one major objective:
accessing multiple genomic data repositories for
collaborative research in a privacy-preserving manner.
Due to the privacy concerns regarding the genomic data,
multi-jurisdictional laws and policies of cross-border
genomic data sharing are enforced among different
countries. In this article, we present SAFETY, a hybrid
framework, which can securely perform GWAS on federated
genomic datasets using homomorphic encryption and
recently introduced secure hardware component of Intel
Software Guard Extensions to ensure high efficiency and
privacy at the same time. Different experimental
settings show the efficacy and applicability of such
hybrid framework in secure conduction of GWAS. To the
best of our knowledge, this hybrid use of homomorphic
encryption along with Intel SGX is not proposed to this
date. SAFETY is up to 4.82 times faster than the best
existing secure computation technique.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mao:2019:MEP,
author = "Chengsheng Mao and Yuan Zhao and Mengxin Sun and Yuan
Luo",
title = "Are My {EHRs} Private Enough? {Event}-Level Privacy
Protection",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "103--112",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2850037",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Privacy is a major concern in sharing human subject
data to researchers for secondary analyses. A simple
binary consent opt-in or not may significantly reduce
the amount of sharable data, since many patients might
only be concerned about a few sensitive medical
conditions rather than the entire medical records. We
propose event-level privacy protection, and develop a
feature ablation method to protect event-level privacy
in electronic medical records. Using a list of 13
sensitive diagnoses, we evaluate the feasibility and
the efficacy of the proposed method. As feature
ablation progresses, the identifiability of a sensitive
medical condition decreases with varying speeds on
different diseases. We find that these sensitive
diagnoses can be divided into three categories: 1 five
diseases have fast declining identifiability AUC below
0.6 with less than 400 features excluded; 2 seven
diseases with progressively declining identifiability
AUC below 0.7 with between 200 and 700 features
excluded; and 3 one disease with slowly declining
identifiability AUC above 0.7 with 1,000 features
excluded. The fact that the majority 12 out of 13 of
the sensitive diseases fall into the first two
categories suggests the potential of the proposed
feature ablation method as a solution for event-level
record privacy protection.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jiang:2019:SSL,
author = "Yichen Jiang and Jenny Hamer and Chenghong Wang and
Xiaoqian Jiang and Miran Kim and Yongsoo Song and Yuhou
Xia and Noman Mohammed and Md Nazmus Sadat and Shuang
Wang",
title = "{SecureLR}: Secure Logistic Regression Model via a
Hybrid Cryptographic Protocol",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "113--123",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2833463",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Machine learning applications are intensively utilized
in various science fields, and increasingly the
biomedical and healthcare sector. Applying predictive
modeling to biomedical data introduces privacy and
security concerns requiring additional protection to
prevent accidental disclosure or leakage of sensitive
patient information. Significant advancements in secure
computing methods have emerged in recent years,
however, many of which require substantial
computational and/or communication overheads, which
might hinder their adoption in biomedical applications.
In this work, we propose SecureLR, a novel framework
allowing researchers to leverage both the computational
and storage capacity of Public Cloud Servers to conduct
learning and predictions on biomedical data without
compromising data security or efficiency. Our model
builds upon homomorphic encryption methodologies with
hardware-based security reinforcement through Software
Guard Extensions SGX, and our implementation
demonstrates a practical hybrid cryptographic solution
to address important concerns in conducting machine
learning with public clouds.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sun:2019:EMM,
author = "Mengmeng Sun and Tao Ding and Xu-Qing Tang and Yu
Keming",
title = "An Efficient Mixed-Model for Screening Differentially
Expressed Genes of Breast Cancer Based on {LR--RF}",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "124--130",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2829519",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "To screen differentially expressed genes quickly and
efficiently in breast cancer, two gene microarray
datasets of breast cancer, GSE15852 and GSE45255, were
downloaded from GEO. By combining the Logistic
Regression and Random Forest algorithm, this paper
proposed a novel method named LR-RF to select
differentially expressed genes of breast cancer on
microarray data by the Bonferroni test of FWER error
measure. Comparing with Logistic Regression and Random
Forest, our study shows that LR-FR has a great facility
in selecting differentially expressed genes. The
average prediction accuracy of the proposed LR-RF from
replicating random test 10 times surprisingly reaches $
{{93.11}} $ percent with variance as low as $
{{0.00045}} $. The prediction accuracy rate reaches a
maximum 95.57 percent when threshold value $ \alpha =
0.2 $ in the random forest algorithm process of ranking
genes' importance score, and the differentially
expressed genes are relatively few in number. In
addition, through analyzing the gene interaction
networks, most of the top 20 genes we selected were
found to involve in the development of breast cancer.
All of these results demonstrate the reliability and
efficiency of LR-RF. It is anticipated that LR-RF would
provide new knowledge and method for biologists,
medical scientists, and cognitive computing researchers
to identify disease-related genes of breast cancer.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhu:2019:ARC,
author = "Wenliang Zhu and Xiaohe Chen and Yan Wang and Lirong
Wang",
title = "Arrhythmia Recognition and Classification Using {ECG}
Morphology and Segment Feature Analysis",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "131--138",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2846611",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this work, arrhythmia appearing with the presence
of abnormal heart electrical activity is efficiently
recognized and classified. A novel method is proposed
for accurate recognition and classification of cardiac
arrhythmias. Firstly, P-QRS-T waves is segmented from
ECG waveform; secondly, morphological features are
extracted from P-QRS-T waves, and ECG segment features
are extracted from the selected ECG segment by using
PCA and dynamic time warping DTW; finally, SVM is
applied to the features and automatic diagnosis results
is presented. ECG data set used is derived from the
MIT-BIH in which ECG signals are divided into the four
classes: normal beatsN, supraventricular ectopic beats
SVEBs, ventricular ectopic beats VEBs and fusion of
ventricular and normal F. Our proposed method can
distinguish N, SVEBs, VEBs and F with an accuracy of
97.80 percent. The sensitivities for the classes N,
SVEBs, VEBs and F are 99.27, 87.47, 94.71, and 73.88
percent and the positive predictivities are 98.48,
95.25, 95.22 and 86.09 percent respectively. The
detection sensitivity of SVEBs and VEBs has a better
performance by combining proposed features than by
using the ECG morphology or ECG segment features
separately. The proposed method is compared with four
selected peer algorithms and delivers solid results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zeng:2019:NLP,
author = "Zexian Zeng and Yu Deng and Xiaoyu Li and Tristan
Naumann and Yuan Luo",
title = "Natural Language Processing for {EHR}-Based
Computational Phenotyping",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "139--153",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2849968",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This article reviews recent advances in applying
natural language processing NLP to Electronic Health
Records EHRs for computational phenotyping. NLP-based
computational phenotyping has numerous applications
including diagnosis categorization, novel phenotype
discovery, clinical trial screening, pharmacogenomics,
drug-drug interaction DDI, and adverse drug event ADE
detection, as well as genome-wide and phenome-wide
association studies. Significant progress has been made
in algorithm development and resource construction for
computational phenotyping. Among the surveyed methods,
well-designed keyword search and rule-based systems
often achieve good performance. However, the
construction of keyword and rule lists requires
significant manual effort, which is difficult to scale.
Supervised machine learning models have been favored
because they are capable of acquiring both
classification patterns and structures from data.
Recently, deep learning and unsupervised learning have
received growing attention, with the former favored for
its performance and the latter for its ability to find
novel phenotypes. Integrating heterogeneous data
sources have become increasingly important and have
shown promise in improving model performance. Often,
better performance is achieved by combining multiple
modalities of information. Despite these many advances,
challenges and opportunities remain for NLP-based
computational phenotyping, including better model
interpretability and generalizability, and proper
characterization of feature relations in clinical
narratives.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2019:DDR,
author = "Yin-Ying Wang and Chunfeng Cui and Liqun Qi and Hong
Yan and Xing-Ming Zhao",
title = "{DrPOCS}: Drug Repositioning Based on Projection Onto
Convex Sets",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "154--162",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2830384",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Drug repositioning, i.e., identifying new indications
for known drugs, has attracted a lot of attentions
recently and is becoming an effective strategy in drug
development. In literature, several computational
approaches have been proposed to identify potential
indications of old drugs based on various types of data
sources. In this paper, by formulating the drug-disease
associations as a low-rank matrix, we propose a novel
method, namely DrPOCS, to identify candidate
indications of old drugs based on projection onto
convex sets POCS. With the integration of drug
structure and disease phenotype information, DrPOCS
predicts potential associations between drugs and
diseases with matrix completion. Benchmarking results
demonstrate that our proposed approach outperforms
popular existing approaches with high accuracy. In
addition, a number of novel predicted indications are
validated with various types of evidences, indicating
the predictive power of our proposed approach.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ding:2019:CSN,
author = "Dewu Ding and Xiao Sun",
title = "A Comparative Study of Network Motifs in the
Integrated Transcriptional Regulation and Protein
Interaction Networks of \bioname{Shewanella}",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "163--171",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2804393",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The Shewanella species shows a remarkable respiratory
versatility with a great variety of extracellular
electron acceptors termed Extracellular Electron
Transfer, EET. To explore relevant mechanisms from the
network motif view, we constructed the integrated
networks that combined transcriptional regulation
interactions TRIs and protein-protein interactions PPIs
for 13 Shewanella species, identified and compared the
network motifs in these integrated networks. We found
that the network motifs were evolutionary conserved in
these integrated networks. The functional significance
of the highly conserved motifs was discussed,
especially the important ones that were potentially
involved in the Shewanella EET processes. More
importantly, we found that: 1 the motif co-regulated
PPI took a role in the ``standby mode'' of protein
utilization, which will be helpful for cells to rapidly
response to environmental changes; and 2 the type II
cofactors, which involved in the motif TRI interacting
with a third protein, mainly carried out a signalling
role in Shewanella oneidensis MR-1.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2019:MNL,
author = "Juan Wang and Jin-Xing Liu and Chun-Hou Zheng and
Ya-Xuan Wang and Xiang-Zhen Kong and Chang-Gang Wen",
title = "A Mixed-Norm {Laplacian} Regularized Low-Rank
Representation Method for Tumor Samples Clustering",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "172--182",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2769647",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Tumor samples clustering based on biomolecular data is
a hot issue of cancer classifications discovery. How to
extract the valuable information from high dimensional
genomic data is becoming an urgent problem in tumor
samples clustering. In this paper, we introduce
manifold regularization into low-rank representation
model and present a novel method named Mixed-norm
Laplacian regularized Low-Rank Representation MLLRR to
identify the differentially expressed genes for tumor
clustering based on gene expression data. Then, in
order to advance the accuracy and stability of tumor
clustering, we establish the clustering model based on
Penalized Matrix Decomposition PMD and propose a novel
cluster method named MLLRR-PMD. In this method, the
cancer clustering research includes three steps. First,
the matrix of gene expression data is decomposed into a
low rank representation matrix and a sparse matrix by
MLLRR. Second, the differentially expressed genes are
identified based on the sparse matrix. Finally, the PMD
is applied to cluster the samples based on the
differentially expressed genes. The experiment results
on simulation data and real genomic data illustrate
that MLLRR method enhances the robustness to outliers
and achieves remarkable performance in the extraction
of differentially expressed genes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Paul:2019:IAI,
author = "Sushmita Paul and Dhanajit Brahma",
title = "An Integrated Approach for Identification of
Functionally Similar {MicroRNAs} in Colorectal Cancer",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "183--192",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2765332",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Colorectal cancer CRC is one of the most prevalent
cancers around the globe. However, the molecular
reasons for pathogenesis of CRC are still poorly
understood. Recently, the role of microRNAs or miRNAs
in the initiation and progression of CRC has been
studied. MicroRNAs are small, endogenous noncoding RNAs
found in plants, animals, and some viruses, which
function in RNA silencing and posttranscriptional
regulation of gene expression. Their role in CRC
development is studied and they are found to be
potential biomarkers in diagnosis and treatment of CRC.
Therefore, identification of functionally similar CRC
related miRNAs may help in the development of a
prognostic tool. In this regard, this paper presents a
new algorithm, called $ \mu $Sim. It is an integrative
approach for identification of functionally similar
miRNAs associated with CRC. It integrates judiciously
the information of miRNA expression data and
miRNA-miRNA functionally synergistic network data. The
functional similarity is calculated based on both miRNA
expression data and miRNA-miRNA functionally
synergistic network data. The effectiveness of the
proposed method in comparison to other related methods
is shown on four CRC miRNA data sets. The proposed
method selected more significant miRNAs related to CRC
as compared to other related methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Karbalayghareh:2019:CSC,
author = "Alireza Karbalayghareh and Ulisses Braga-Neto and
Edward R. Dougherty",
title = "Classification of Single-Cell Gene Expression
Trajectories from Incomplete and Noisy Data",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "193--207",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2763946",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "This paper studies classification of gene-expression
trajectories coming from two classes, healthy and
mutated cancerous using Boolean networks with
perturbation BNps to model the dynamics of each class
at the state level. Each class has its own BNp, which
is partially known based on gene pathways. We employ a
Gaussian model at the observation level to show the
expression values of the genes given the hidden binary
states at each time point. We use expectation
maximization EM to learn the BNps and the unknown model
parameters, derive closed-form updates for the
parameters, and propose a learning algorithm. After
learning, a plug-in Bayes classifier is used to
classify unlabeled trajectories, which can have missing
data. Measuring gene expressions at different times
yields trajectories only when measurements come from a
single cell. In multiple-cell scenarios, the expression
values are averages over many cells with possibly
different states. Via the central-limit theorem, we
propose another model for expression data in
multiple-cell scenarios. Simulations demonstrate that
single-cell trajectory data can outperform
multiple-cell average expression data relative to
classification error, especially in high-noise
situations. We also consider data generated via a
mammalian cell-cycle network, both the wild-type and
with a common mutation affecting p27.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cheng:2019:CBC,
author = "Kin-On Cheng and Ngai-Fong Law and Wan-Chi Siu",
title = "Clustering-Based Compression for Population {DNA}
Sequences",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "208--221",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2762302",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Due to the advancement of DNA sequencing techniques,
the number of sequenced individual genomes has
experienced an exponential growth. Thus, effective
compression of this kind of sequences is highly
desired. In this work, we present a novel compression
algorithm called Reference-based Compression algorithm
using the concept of Clustering RCC. The rationale
behind RCC is based on the observation about the
existence of substructures within the population
sequences. To utilize these substructures, $k$-means
clustering is employed to partition sequences into
clusters for better compression. A reference sequence
is then constructed for each cluster so that sequences
in that cluster can be compressed by referring to this
reference sequence. The reference sequence of each
cluster is also compressed with reference to a sequence
which is derived from all the reference sequences.
Experiments show that RCC can further reduce the
compressed size by up to 91.0 percent when compared
with state-of-the-art compression approaches. There is
a compromise between compressed size and processing
time. The current implementation in Matlab has time
complexity in a factor of thousands higher than the
existing algorithms implemented in C/C++. Further
investigation is required to improve processing time in
future.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Luo:2019:DGP,
author = "Ping Luo and Li-Ping Tian and Jishou Ruan and
Fang-Xiang Wu",
title = "Disease Gene Prediction by Integrating {PPI} Networks,
Clinical {RNA}-Seq Data and {OMIM} Data",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "222--232",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2770120",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Disease gene prediction is a challenging task that has
a variety of applications such as early diagnosis and
drug development. The existing machine learning methods
suffer from the imbalanced sample issue because the
number of known disease genes positive samples is much
less than that of unknown genes which are typically
considered to be negative samples. In addition, most
methods have not utilized clinical data from patients
with a specific disease to predict disease genes. In
this study, we propose a disease gene prediction
algorithm called dgSeq by combining protein-protein
interaction PPI network, clinical RNA-Seq data, and
Online Mendelian Inheritance in Man OMIN data. Our
dgSeq constructs differential networks based on
rewiring information calculated from clinical RNA-Seq
data. To select balanced sets of non-disease genes
negative samples, a disease-gene network is also
constructed from OMIM data. After features are
extracted from the PPI networks and differential
networks, the logistic regression classifiers are
trained. Our dgSeq obtains AUC values of 0.88, 0.83,
and 0.80 for identifying breast cancer genes, thyroid
cancer genes, and Alzheimer's disease genes,
respectively, which indicates its superiority to other
three competing methods. Both gene set enrichment
analysis and predicted results demonstrate that dgSeq
can effectively predict new disease genes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yan:2019:DMP,
author = "Cheng Yan and Jianxin Wang and Peng Ni and Wei Lan and
Fang-Xiang Wu and Yi Pan",
title = "{DNRLMF--MDA}: Predicting {microRNA-Disease}
Associations Based on Similarities of {microRNAs} and
Diseases",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "233--243",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2776101",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "MicroRNAs miRNAs are a class of non-coding RNAs about
$ \sim $22nt nucleotides. Studies have proven that
miRNAs play key roles in many human complex diseases.
Therefore, discovering miRNA-disease associations is
beneficial to understanding disease mechanisms,
developing drugs, and treating complex diseases. It is
well known that it is a time-consuming and expensive
process to discover the miRNA-disease associations via
biological experiments. Alternatively, computational
models could provide a low-cost and high-efficiency way
for predicting miRNA-disease associations. In this
study, we propose a method called DNRLMF-MDA to predict
miRNA-disease associations based on dynamic
neighborhood regularized logistic matrix factorization.
DNRLMF-MDA integrates known miRNA-disease associations,
functional similarity and Gaussian Interaction Profile
GIP kernel similarity of miRNAs, and functional
similarity and GIP kernel similarity of diseases.
Especially, positive observations known miRNA-disease
associations are assigned higher importance levels than
negative observations unknown miRNA-disease
associations.DNRLMF-MDA computes the probability that a
miRNA would interact with a disease by a logistic
matrix factorization method, where latent vectors of
miRNAs and diseases represent the properties of miRNAs
and diseases, respectively, and further improve
prediction performance via dynamic neighborhood
regularized. The 5-fold cross validation is adopted to
assess the performance of our DNRLMF-MDA, as well as
other competing methods for comparison. The
computational experiments show that DNRLMF-MDA
outperforms the state-of-art method PBMDA. The AUC
values of DNRLMF-MDA on three datasets are 0.9357,
0.9411, and 0.9416, respectively, which are superior to
the PBMDA's results of 0.9218, 0.9187, and 0.9262. The
average computation times per 5-fold cross validation
of DNRLMF-MDA on three datasets are 38, 46, and 50
seconds, which are shorter than the PBMDA's average
computation times of 10869, 916, and 8448 seconds,
respectively. DNRLMF-MDA also can predict potential
diseases for new miRNAs. Furthermore, case studies
illustrate that DNRLMF-MDA is an effective method to
predict miRNA-disease associations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ju:2019:EDA,
author = "Ronghui Ju and Chenhui Hu and Pan Zhou and Quanzheng
Li",
title = "Early Diagnosis of {Alzheimer}'s Disease Based on
Resting-State Brain Networks and Deep Learning",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "244--257",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2776910",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Computerized healthcare has undergone rapid
development thanks to the advances in medical imaging
and machine learning technologies. Especially, recent
progress on deep learning opens a new era for
multimedia based clinical decision support. In this
paper, we use deep learning with brain network and
clinical relevant text information to make early
diagnosis of Alzheimer's Disease AD. The clinical
relevant text information includes age, gender, and $ A
p o E $ gene of the subject. The brain network is
constructed by computing the functional connectivity of
brain regions using resting-state functional magnetic
resonance imaging R-fMRI data. A targeted autoencoder
network is built to distinguish normal aging from mild
cognitive impairment, an early stage of AD. The
proposed method reveals discriminative brain network
features effectively and provides a reliable classifier
for AD detection. Compared to traditional classifiers
based on R-fMRI time series data, about 31.21 percent
improvement of the prediction accuracy is achieved by
the proposed deep learning method, and the standard
deviation reduces by 51.23 percent in the best case
that means our prediction model is more stable and
reliable compared to the traditional methods. Our work
excavates deep learning's advantages of classifying
high-dimensional multimedia data in medical services,
and could help predict and prevent AD at an early
stage.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pesantez-Cabrera:2019:EDC,
author = "Paola Pesantez-Cabrera and Ananth Kalyanaraman",
title = "Efficient Detection of Communities in Biological
Bipartite Networks",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "258--271",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2765319",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Methods to efficiently uncover and extract community
structures are required in a number of biological
applications where networked data and their
interactions can be modeled as graphs, and observing
tightly-knit groups of vertices ``communities'' can
offer insights into the structural and functional
building blocks of the underlying network. Classical
applications of community detection have largely
focused on unipartite networks --- i.e., graphs built
out of a single type of objects. However, due to
increased availability of biological data from various
sources, there is now an increasing need for handling
heterogeneous networks which are built out of multiple
types of objects. In this paper, we address the problem
of identifying communities from biological bipartite
networks --- i.e., networks where interactions are
observed between two different types of objects e.g.,
genes and diseases, drugs and protein complexes, plants
and pollinators, and hosts and pathogens. Toward
detecting communities in such bipartite networks, we
make the following contributions: i metric we propose a
variant of bipartite modularity; ii algorithms we
present an efficient algorithm called biLouvain that
implements a set of heuristics toward fast and precise
community detection in bipartite networks
https://github.com/paolapesantez/biLouvain; and iii
experiments we present a thorough experimental
evaluation of our algorithm including comparison to
other state-of-the-art methods to identify communities
in bipartite networks. Experimental results show that
our biLouvain algorithm identifies communities that
have a comparable or better quality as measured by
bipartite modularity than existing methods, while
significantly reducing the time-to-solution between one
and four orders of magnitude.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2019:EGW,
author = "Xiangtao Li and Ka-Chun Wong",
title = "Elucidating Genome-Wide Protein-{RNA} Interactions
Using Differential Evolution",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "272--282",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2776224",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "RNA-binding proteins RBPs play an important role in
the post-transcriptional control of RNAs, such as
splicing, polyadenylation, mRNA stabilization, mRNA
localization, and translation. Thanks to the recent
breakthrough, non-negative matrix factorization NMF has
been developed to combine multiple data sources to
discover non-overlapping and class-specific RNA binding
patterns. However, several challenges still exist in
determining the number of latent dimensions in the
factorization steps. In most circumstances, it is often
assumed that the number of latent dimensions or
components is given. Such trial-and-error procedures
can be tedious in practice. In order to address this
problem, differential evolution algorithm is proposed
as the model selection method to choose the suitable
number of ranks, which can adaptively decompose the
input protein-RNA data matrix into different
nonnegative components. Experimental results
demonstrate that the proposed algorithms can improve
the factorization quality over the recent
state-of-the-arts. The effectiveness of the proposed
algorithms are supported by comprehensive performance
benchmarking on 31 genome-wide cross-linking
immunoprecipitation CLIP coupled with high-throughput
sequencing CLIP-seq datasets. In addition, time
complexity analysis and parameter analysis are
conducted to demonstrate the robustness of the proposed
methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2019:MPM,
author = "Xuan Zhang and Quan Zou and Alfonso Rodriguez-Paton
and Xiangxiang Zeng",
title = "Meta-Path Methods for Prioritizing Candidate Disease
{miRNAs}",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "283--291",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2776280",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "MicroRNAs miRNAs play critical roles in regulating
gene expression at post-transcriptional levels.
Numerous experimental studies indicate that alterations
and dysregulations in miRNAs are associated with
important complex diseases, especially cancers.
Predicting potential miRNA-disease association is
beneficial not only to explore the pathogenesis of
diseases, but also to understand biological processes.
In this work, we propose two methods that can
effectively predict potential miRNA-disease
associations using our reconstructed miRNA and disease
similarity networks, which are based on the latest
experimental data. We reconstruct a miRNA functional
similarity network using the following biological
information: the miRNA family information, miRNA
cluster information, experimentally valid miRNA-target
association and disease-miRNA information. We also
reconstruct a disease similarity network using disease
functional information and disease semantic
information. We present Katz with specific weights and
Katz with machine learning, on the comprehensive
heterogeneous network. These methods, which achieve
corresponding AUC values of 0.897 and 0.919, exhibit
performance superior to the existing methods.
Comprehensive data networks and reasonable
considerations guarantee the high performance of our
methods. Contrary to several methods, which cannot work
in such situations, the proposed methods also predict
associations for diseases without any known related
miRNAs. A web service for the download and prediction
of relationships between diseases and miRNAs is
available at http://lab.malab.cn/soft/MDPredict/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2019:PRH,
author = "Bin Liu and Junjie Chen and Mingyue Guo and Xiaolong
Wang",
title = "Protein Remote Homology Detection and Fold Recognition
Based on Sequence-Order Frequency Matrix",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "292--300",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2765331",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein remote homology detection and fold recognition
are two critical tasks for the studies of protein
structures and functions. Currently, the profile-based
methods achieve the state-of-the-art performance in
these fields. However, the widely used sequence
profiles, like position-specific frequency matrix PSFM
and position-specific scoring matrix PSSM, ignore the
sequence-order effects along protein sequence. In this
study, we have proposed a novel profile, called
sequence-order frequency matrix SOFM, to extract the
sequence-order information of neighboring residues from
multiple sequence alignment MSA. Combined with two
profile feature extraction approaches, top-n-grams and
the Smith-Waterman algorithm, the SOFMs are applied to
protein remote homology detection and fold recognition,
and two predictors called SOFM-Top and SOFM-SW are
proposed. Experimental results show that SOFM contains
more information content than other profiles, and these
two predictors outperform other state-of-the-art
methods. It is anticipated that SOFM will become a very
useful profile in the studies of protein structures and
functions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chen:2019:QNS,
author = "Minghan Chen and Brandon D. Amos and Layne T. Watson
and John J. Tyson and Young Cao and Clifford A. Shaffer
and Michael W. Trosset and Cihan Oguz and Gisella
Kakoti",
title = "Quasi-{Newton} Stochastic Optimization Algorithm for
Parameter Estimation of a Stochastic Model of the
Budding Yeast Cell Cycle",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "301--311",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2773083",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Parameter estimation in discrete or continuous
deterministic cell cycle models is challenging for
several reasons, including the nature of what can be
observed, and the accuracy and quantity of those
observations. The challenge is even greater for
stochastic models, where the number of simulations and
amount of empirical data must be even larger to obtain
statistically valid parameter estimates. The two main
contributions of this work are 1 stochastic model
parameter estimation based on directly matching
multivariate probability distributions, and 2 a new
quasi-Newton algorithm class QNSTOP for stochastic
optimization problems. QNSTOP directly uses the random
objective function value samples rather than creating
ensemble statistics. QNSTOP is used here to directly
match empirical and simulated joint probability
distributions rather than matching summary statistics.
Results are given for a current state-of-the-art
stochastic cell cycle model of budding yeast, whose
predictions match well some summary statistics and
one-dimensional distributions from empirical data, but
do not match well the empirical joint distributions.
The nature of the mismatch provides insight into the
weakness in the stochastic model.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2019:SPL,
author = "Cheng Liu and Hau San Wong",
title = "Structured Penalized Logistic Regression for Gene
Selection in Gene Expression Data Analysis",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "312--321",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2767589",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In gene expression data analysis, the problems of
cancer classification and gene selection are closely
related. Successfully selecting informative genes will
significantly improve the classification performance.
To identify informative genes from a large number of
candidate genes, various methods have been proposed.
However, the gene expression data may include some
important correlation structures, and some of the genes
can be divided into different groups based on their
biological pathways. Many existing methods do not take
into consideration the exact correlation structure
within the data. Therefore, from both the knowledge
discovery and biological perspectives, an ideal gene
selection method should take this structural
information into account. Moreover, the better
generalization performance can be obtained by
discovering correlation structure within data. In order
to discover structure information among data and
improve learning performance, we propose a structured
penalized logistic regression model which
simultaneously performs feature selection and model
learning for gene expression data analysis. An
efficient coordinate descent algorithm has been
developed to optimize the model. The numerical
simulation studies demonstrate that our method is able
to select the highly correlated features. In addition,
the results from real gene expression datasets show
that the proposed method performs competitively with
respect to previous approaches.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lopez-Lopera:2019:SLF,
author = "Andres F. Lopez-Lopera and Mauricio A. Alvarez",
title = "Switched Latent Force Models for Reverse-Engineering
Transcriptional Regulation in Gene Expression Data",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "322--335",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2764908",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "To survive environmental conditions, cells transcribe
their response activities into encoded mRNA sequences
in order to produce certain amounts of protein
concentrations. The external conditions are mapped into
the cell through the activation of special proteins
called transcription factors TFs. Due to the difficult
task to measure experimentally TF behaviors, and the
challenges to capture their quick-time dynamics,
different types of models based on differential
equations have been proposed. However, those approaches
usually incur in costly procedures, and they present
problems to describe sudden changes in TF regulators.
In this paper, we present a switched dynamical latent
force model for reverse-engineering transcriptional
regulation in gene expression data which allows the
exact inference over latent TF activities driving some
observed gene expressions through a linear differential
equation. To deal with discontinuities in the dynamics,
we introduce an approach that switches between
different TF activities and different dynamical
systems. This creates a versatile representation of
transcription networks that can capture discrete
changes and non-linearities. We evaluate our model on
both simulated data and real data e.g., microaerobic
shift in E. coli, yeast respiration, concluding that
our framework allows for the fitting of the expression
data while being able to infer continuous-time TF
profiles.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2019:TGP,
author = "Jiao Zhang and Sam Kwong and Ka-Chun Wong",
title = "{ToBio}: Global Pathway Similarity Search Based on
Topological and Biological Features",
journal = j-TCBB,
volume = "16",
number = "1",
pages = "336--349",
month = jan,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2769642",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Mon Mar 11 18:45:00 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Pathway similarity search plays a vital role in the
post-genomics era. Unfortunately, pathway similarity
search involves the graph isomorphism problem which is
NP-complete. Therefore, efficient search algorithms are
desirable. In this work, we propose a novel global
pathway similarity search approach named ToBio, which
considers both topological and biological features for
effective global pathway similarity search.
Specifically, as motivated from nature, various
topological and biological features including subgraph
signature similarities, sequence similarities, and gene
ontology similarities are considered in ToBio. Since
different features carry different functional
importance and dependences, we report three schemes of
ToBio using different sets of features. In addition, to
enhance the existing search algorithms for rigorous
comparisons, post-processing pipelines are also
proposed to investigate how different features can
contribute to the search performance. ToBio and other
state-of-the-art methods are benchmarked on the
gold-standard pathway datasets from three species. The
results demonstrate the competitive edges of ToBio over
the state-of-the-arts ranging from the topological
aspects to the biological aspects. Case studies have
been conducted to reveal mechanistic insights into the
unique search performance of ToBio.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhou:2019:E,
author = "Shuigeng Zhou and Yi-Ping Phoebe Chen and Hiroshi
Mamitsuka",
title = "Editorial",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "350--351",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2827138",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2827138",
abstract = "This special section consists of eight papers selected
from the accepted papers of the 27th International
Conference on Genome Informatics (GIW2016), which was
held in Shanghai, China, October 3-5, 2016. These
papers cover diverse topics, including gene \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2019:SWC,
author = "Xiaojun Chen and Joshua Z. Huang and Qingyao Wu and
Min Yang",
title = "Subspace Weighting Co-Clustering of Gene Expression
Data",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "352--364",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2705686",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2705686",
abstract = "Microarray technology enables the collection of vast
amounts of gene expression data from biological
experiments. Clustering algorithms have been
successfully applied to exploring the gene expression
data. Since a set of genes may be only correlated to a
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Tian:2019:REA,
author = "Bo Tian and Qiong Duan and Can Zhao and Ben Teng and
Zengyou He",
title = "{Reinforce}: an Ensemble Approach for Inferring {PPI}
Network from {AP--MS} Data",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "365--376",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2705060",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2705060",
abstract = "Affinity Purification-Mass Spectrometry (AP-MS) is one
of the most important technologies for constructing
protein-protein interaction (PPI) networks. In this
paper, we propose an ensemble method, Reinforce, for
inferring PPI network from AP-MS data set. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xu:2019:EPD,
author = "Bin Xu and Jihong Guan and Yang Wang and Zewei Wang",
title = "Essential Protein Detection by Random Walk on Weighted
Protein--Protein Interaction Networks",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "377--387",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2701824",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2701824",
abstract = "Essential proteins are critical to the development and
survival of cells. Identification of essential proteins
is helpful for understanding the minimal set of
required genes in a living cell and for designing new
drugs. To detect essential proteins, \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sun:2019:IAL,
author = "Weiping Sun and Yi Liu and Gills A. Lajoie and Bin Ma
and Kaizhong Zhang",
title = "An Improved Approach for {$N$}-Linked Glycan Structure
Identification from {HCD MS\slash MS} Spectra",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "388--395",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2701819",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2701819",
abstract = "Glycosylation is a frequently observed
post-translational modification on proteins. Currently,
tandem mass spectrometry (MS/MS) serves as an efficient
analytical technique for characterizing structures of
oligosaccharides. However, developing effective
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2019:IMH,
author = "Jingpu Zhang and Zuping Zhang and Zhigang Chen and Lei
Deng",
title = "Integrating Multiple Heterogeneous Networks for Novel
{LncRNA}-Disease Association Inference",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "396--406",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2701379",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2701379",
abstract = "Accumulating experimental evidence has indicated that
long non-coding RNAs (lncRNAs) are critical for the
regulation of cellular biological processes implicated
in many human diseases. However, only relatively few
experimentally supported lncRNA-disease \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2019:KLS,
author = "Zuping Zhang and Jingpu Zhang and Chao Fan and Yongjun
Tang and Lei Deng",
title = "{KATZLGO}: Large-Scale Prediction of {LncRNA}
Functions by Using the {KATZ} Measure Based on Multiple
Networks",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "407--416",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2704587",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2704587",
abstract = "Aggregating evidences have shown that long non-coding
RNAs (lncRNAs) generally play key roles in cellular
biological processes such as epigenetic regulation,
gene expression regulation at transcriptional and
post-transcriptional levels, cell \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2019:MSM,
author = "Min Li and Ruiqing Zheng and Yaohang Li and Fang-Xiang
Wu and Jianxin Wang",
title = "{MGT--SM}: a Method for Constructing Cellular Signal
Transduction Networks",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "417--424",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2705143",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2705143",
abstract = "A cellular signal transduction network is an important
means to describe biological responses to environmental
stimuli and exchange of biological signals.
Constructing the cellular signal transduction network
provides an important basis for the study of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Peng:2019:CMC,
author = "Shaoliang Peng and Yingbo Cui and Shunyun Yang and
Wenhe Su and Xiaoyu Zhang and Tenglilang Zhang and
Weiguo Liu and Xing-Ming Zhao",
title = "A {CPU--MIC} Collaborated Parallel Framework for
{GROMACS} on {Tianhe-2} Supercomputer",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "425--433",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2713362",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/super.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2713362",
abstract = "Molecular Dynamics (MD) is the simulation of the
dynamic behavior of atoms and molecules. As the most
popular software for molecular dynamics, GROMACS cannot
work on large-scale data because of limit computing
resources. In this paper, we propose a CPU \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2019:FSS,
author = "Jie Zhang and Zhigen Zhao and Kai Zhang and Zhi Wei",
title = "A Feature Sampling Strategy for Analysis of High
Dimensional Genomic Data",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "434--441",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2779492",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2779492",
abstract = "With the development of high throughput technology, it
has become feasible and common to profile tens of
thousands of gene activities simultaneously. These
genomic data typically have sample size of hundreds or
fewer, which is much less than the feature \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2019:UMJ,
author = "Kefei Liu and Jieping Ye and Yang Yang and Li Shen and
Hui Jiang",
title = "A Unified Model for Joint Normalization and
Differential Gene Expression Detection in {RNA}-Seq
Data",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "442--454",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2790918",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2790918",
abstract = "The RNA-sequencing (RNA-seq) is becoming increasingly
popular for quantifying gene expression levels. Since
the RNA-seq measurements are relative in nature,
between-sample normalization is an essential step in
differential expression (DE) analysis. The \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2019:IFI,
author = "Wei Zhang and Shu-Lin Wang",
title = "An Integrated Framework for Identifying Mutated Driver
Pathway and Cancer Progression",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "455--464",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2788016",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2788016",
abstract = "Next-generation sequencing (NGS) technologies provide
amount of somatic mutation data in a large number of
patients. The identification of mutated driver pathway
and cancer progression from these data is a challenging
task because of the heterogeneity of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Michels:2019:RBM,
author = "Tim Michels and Dimitri Berh and Xiaoyi Jiang",
title = "An {RJMCMC}-Based Method for Tracking and Resolving
Collisions of \bioname{Drosophila} Larvae",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "465--474",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2779141",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2779141",
abstract = "Drosophila melanogaster is an important model organism
for ongoing research in neuro- and behavioral biology.
Especially the locomotion analysis has become an
integral part of such studies and thus elaborated
automated tracking systems have been proposed
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2019:BNC,
author = "Lu Zhang and Qiuping Pan and Yue Wang and Xintao Wu
and Xinghua Shi",
title = "{Bayesian} Network Construction and Genotype-Phenotype
Inference Using {GWAS} Statistics",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "475--489",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2779498",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2779498",
abstract = "Genome-wide association studies (GWASs) have received
increasing attention to understand how genetic
variation affects different human traits. In this
paper, we study whether and to what extent exploiting
the GWAS statistics can be used for inferring
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Mandal:2019:BIC,
author = "Koyel Mandal and Rosy Sarmah and Dhruba Kumar
Bhattacharyya",
title = "Biomarker Identification for Cancer Disease Using
Biclustering Approach: an Empirical Study",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "490--509",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2820695",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2820695",
abstract = "This paper presents an exhaustive empirical study to
identify biomarkers using two approaches:
frequency-based and network-based, over 17 different
biclustering algorithms and six different cancer
expression datasets. To systematically analyze the
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Andersen:2019:CTM,
author = "Jakob L. Andersen and Christoph Flamm and Daniel
Merkle and Peter F. Stadler",
title = "Chemical Transformation Motifs --- Modelling Pathways
as Integer Hyperflows",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "510--523",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2781724",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2781724",
abstract = "We present an elaborate framework for formally
modelling pathways in chemical reaction networks on a
mechanistic level. Networks are modelled mathematically
as directed multi-hypergraphs, with vertices
corresponding to molecules and hyperedges to \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Boluki:2019:CPB,
author = "Shahin Boluki and Mohammad Shahrokh Esfahani and
Xiaoning Qian and Edward R. Dougherty",
title = "Constructing Pathway-Based Priors within a {Gaussian}
Mixture Model for {Bayesian} Regression and
Classification",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "524--537",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2778715",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2778715",
abstract = "Gene-expression-based classification and regression
are major concerns in translational genomics. If the
feature-label distribution is known, then an optimal
classifier can be derived. If the predictor-target
distribution is known, then an optimal \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2019:DPD,
author = "Huanan Zhang and David Roe and Rui Kuang",
title = "Detecting Population-Differentiation Copy Number
Variants in Human Population Tree by Sparse Group
Selection",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "538--549",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2779481",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2779481",
abstract = "Copy-number variants (CNVs) account for a substantial
proportion of human genetic variations. Understanding
the CNV diversities across populations is a
computational challenge because CNV patterns are often
present in several related populations and only
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Caudai:2019:ESC,
author = "Claudia Caudai and Emanuele Salerno and Monica
Zopp{\`e} and Anna Tonazzini",
title = "Estimation of the Spatial Chromatin Structure Based on
a Multiresolution Bead-Chain Model",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "550--559",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2791439",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2791439",
abstract = "We present a method to infer 3D chromatin
configurations from Chromosome Conformation Capture
data. Quite a few methods have been proposed to
estimate the structure of the nuclear dna in
homogeneous populations of cells from this kind of
data. Many of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Nizami:2019:EAS,
author = "Bilal Nizami and Elham Mousavinezhad Sarasia and
Mehbub I. K. Momin and Bahareh Honarparvar",
title = "Estrogenic Active Stilbene Derivatives as Anti-Cancer
Agents: a {DFT} and {QSAR} Study",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "560--568",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2779505",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2779505",
abstract = "Exploring different quantum chemical quantities for
lead compounds is an ongoing approach in identifying
crucial structural activity related features that are
contributing into their biological activities. Herein,
activity-related quantum chemical \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2019:FAC,
author = "Biing-Feng Wang and Chih-Yu Li",
title = "Fast Algorithms for Computing Path-Difference
Distances",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "569--582",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2790957",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2790957",
abstract = "Tree comparison metrics are an important tool for the
study of phylogenetic trees. Path-difference distances
measure the dissimilarity between two phylogenetic
trees (on the same set of taxa) by comparing their
path-length vectors. Various norms can be \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Foo:2019:MCG,
author = "Mathias Foo and Jongrae Kim and Declan G. Bates",
title = "Modelling and Control of Gene Regulatory Networks for
Perturbation Mitigation",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "583--595",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2771775",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2771775",
abstract = "Synthetic Biologists are increasingly interested in
the idea of using synthetic feedback control circuits
for the mitigation of perturbations to gene regulatory
networks that may arise due to disease and/or
environmental disturbances. Models employing \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Nguyen:2019:NDL,
author = "Son P. Nguyen and Zhaoyu Li and Dong Xu and Yi Shang",
title = "New Deep Learning Methods for Protein Loop Modeling",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "596--606",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2784434",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2784434",
abstract = "Computational protein structure prediction is a
long-standing challenge in bioinformatics. In the
process of predicting protein 3D structures, it is
common that parts of an experimental structure are
missing or parts of a predicted structure need to be
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Niu:2019:PPI,
author = "Yun Niu and Hongmei Wu and Yuwei Wang",
title = "Protein-Protein Interaction Identification Using a
Similarity-Constrained Graph Model",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "607--616",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2777448",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2777448",
abstract = "Protein-protein interaction (PPI) identification is an
important task in text mining. Most PPI detection
systems make predictions solely based on evidence
within a single sentence and often suffer from the
heavy burden of manual annotation. This paper
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Salmela:2019:SFG,
author = "Leena Salmela and Alexandru I. Tomescu",
title = "Safely Filling Gaps with Partial Solutions Common to
All Solutions",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "617--626",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2785831",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2785831",
abstract = "Gap filling has emerged as a natural sub-problem of
many de novo genome assembly projects. The gap filling
problem generally asks for an $s$ s-$t$ t path in an
assembly graph whose length matches the gap length
estimate. Several methods have addressed it, \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lopez-Caamal:2019:SAI,
author = "Fernando L{\'o}pez-Caamal and Heinrich J. Huber",
title = "Stable {IL}-$ 1 \beta 1 \beta $-Activation in an
Inflammasome Signalling Model Depends on Positive and
Negative Feedbacks and Tight Regulation of Protein
Production",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "627--637",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2794971",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2794971",
abstract = "Introduction. NLRP3-dependent inflammasome signalling
is a key pathway during inflammatory processes and its
deregulation is implicated in several diseases.
NLRP3-inflammasome pathway activation leads to the
rapid, phosphorylation-driven NF$ \kappa $ \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Najafi:2019:SAM,
author = "Amir Najafi and Sepehr Janghorbani and Seyed Abolfazl
Motahari and Emad Fatemizadeh",
title = "Statistical Association Mapping of
Population-Structured Genetic Data",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "638--649",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2786239",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2786239",
abstract = "Association mapping of genetic diseases has attracted
extensive research interest during the recent years.
However, most of the methodologies introduced so far
suffer from spurious inference of the associated sites
due to population inhomogeneities. In \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lin:2019:RCM,
author = "Xiaohui Lin and Xin Huang and Lina Zhou and Weijie Ren
and Jun Zeng and Weihong Yao and Xingyuan Wang",
title = "The Robust Classification Model Based on Combinatorial
Features",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "650--657",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2779512",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2779512",
abstract = "Analyzing the disease data from the view of
combinatorial features may better characterize the
disease phenotype. In this study, a novel method is
proposed to construct feature combinations and a
classification model (CFC-CM) by mining key feature
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Shao:2019:THM,
author = "Mingfu Shao and Carl Kingsford",
title = "Theory and A Heuristic for the Minimum Path Flow
Decomposition Problem",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "658--670",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2779509",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2779509",
abstract = "Motivated by multiple genome assembly problems and
other applications, we study the following minimum path
flow decomposition problem: Given a directed acyclic
graph $ G = (V, E) $G=(V,E) with source $s$ s and sink
$t$ t and a flow $f$ f, compute a set of $s$ s-$t$.
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Kaiser:2019:UDG,
author = "Florian Kaiser and Dirk Labudde",
title = "Unsupervised Discovery of Geometrically Common
Structural Motifs and Long-Range Contacts in Protein
{$3$D} Structures",
journal = j-TCBB,
volume = "16",
number = "2",
pages = "671--680",
month = mar,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2786250",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:45 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2017.2786250",
abstract = "The essential role of small evolutionarily conserved
structural units in proteins has been extensively
researched and validated. A popular example are serine
proteases, where the peptide cleavage reaction is
realized by a configuration of only three \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Martin-Vide:2019:ACB,
author = "Carlos Martin-Vide and Miguel A. Vega-Rodriguez",
title = "Algorithms for Computational Biology: Third Edition",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "701--702",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2911264",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The papers in this special section were presented at
the 3rd International Conference on Algorithms for
Computational Biology, AlCoB 2016, that was held in
Trujillo, Spain, on June 21--22, 2016.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ahmed:2019:GMS,
author = "Syed Ali Ahmed and Saad Mneimneh",
title = "{Gibbs\slash MCMC} Sampling for Multiple {RNA}
Interaction with Sub-Optimal Solutions",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "703--712",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2890519",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Multiple RNA interaction can be modeled as a problem
in combinatorial optimization, where the ``optimal''
structure is driven by an energy-minimization-like
algorithm. However, the actual structure may not be
optimal in this computational sense. Moreover, it is
not necessarily unique. Therefore, alternative
sub-optimal solutions are needed to cover the
biological ground. We present a combinatorial
formulation for the Multiple RNA Interaction problem
with approximation algorithms to handle various
interaction patterns, which when combined with Gibbs
sampling and Markov Chain Monte Carlo MCMC, can
efficiently generate a reasonable number of optimal and
sub-optimal solutions. When viable structures are far
from an optimal solution, exploring dependence among
different parts of the interaction can increase their
score and boost their candidacy for the sampling
algorithm. By clustering the solutions, we identify a
few representatives that are distinct enough to suggest
possible alternative structures.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mykowiecka:2019:CEE,
author = "Agnieszka Mykowiecka and Pawel Gorecki",
title = "Credibility of Evolutionary Events in Gene Trees",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "713--726",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2788888",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Based on the classical non-parametric bootstrapping
for phylogenetic trees, we propose a novel bootstrap
method to define support for gene duplication and
speciation events. By comparing bootstrap gene trees to
the original gene tree, we calculate support for
evolutionary events. While this approach can be used to
annotate orthology and paralogy, we show how it can be
used to verify the reliability of tree reconciliation.
We propose a linear time algorithm for the computation
of bootstrap values, and we show the correspondence of
our method with the classical non-parametric
bootstrapping. Finally, we present two computational
experiments. In the first one, based on simulated data
and nine yeast genomes, we show a comparative study of
several tree rooting methods and evaluation of their
performance by using our bootstrapping method. In the
second experiment, using data from the TreeFam
database, we tested how the reliability of the gene
trees influence the inferred supertree. We found out
that species trees inferred from gene trees having
highly supported events are more biologically
consistent.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sankoff:2019:MSD,
author = "David Sankoff and Chunfang Zheng and Yue Zhang and
Joao Meidanis and Eric Lyons and Haibao Tang",
title = "Models for Similarity Distributions of Syntenic
Homologs and Applications to Phylogenomics",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "727--737",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2849377",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We outline an integrated approach to speciation and
whole genome doubling WGD to resolve the occurrence of
these events in phylogenetic analysis. We propose a
more principled way of estimating the parameters of
gene divergence and fractionation than the standard
mixture of normals analysis. We formulate an algorithm
for resolving data on local peaks in the distributions
of duplicate gene similarities for a number of related
genomes. We illustrate with a comprehensive analysis of
WGD-origin duplicate gene data from the family
\bioname{Brassicaceae}.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Urbini:2019:ERP,
author = "Laura Urbini and Blerina Sinaimeri and Catherine
Matias and Marie-France Sagot",
title = "Exploring the Robustness of the Parsimonious
Reconciliation Method in Host-Symbiont Cophylogeny",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "738--748",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2838667",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The aim of this paper is to explore the robustness of
the parsimonious host-symbiont tree reconciliation
method under editing or small perturbations of the
input. The editing involves making different choices of
unique symbiont mapping to a host in the case where
multiple associations exist. This is made necessary by
the fact that the tree reconciliation model is
currently unable to handle such associations. The
analysis performed could however also address the
problem of errors. The perturbations are re-rootings of
the symbiont tree to deal with a possibly wrong
placement of the root specially in the case of
fast-evolving species. In order to do this robustness
analysis, we introduce a simulation scheme specifically
designed for the host-symbiont cophylogeny context, as
well as a measure to compare sets of tree
reconciliations, both of which are of interest by
themselves.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Huang:2019:GES,
author = "De-Shuang Huang and Vitoantonio Bevilacqua and M.
Michael Gromiha",
title = "Guest Editorial for Special Section on the {13th
International Conference on Intelligent Computing
ICIC}",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "749--750",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2902324",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The papers presented in this special section were
presented at the Thirteenth International Conference on
Intelligent Computing ICIC that was held in Liverpool,
UK, on August 7-10, 2017. ICIC was formed to provide an
annual forum dedicated to the emerging and challenging
topics in artificial intelligence, machine learning,
bioinformatics, and computational biology, etc. It aims
to bring together researchers and practitioners from
both academia and industry to share ideas, problems,
and solutions related to the multifaceted aspects of
intelligent computing.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wassan:2019:CSP,
author = "Jyotsna Talreja Wassan and Haiying Wang and Fiona
Browne and Huiru Zheng",
title = "A Comprehensive Study on Predicting Functional Role of
Metagenomes Using Machine Learning Methods",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "751--763",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2858808",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "``Metagenomics'' is the study of genomic sequences
obtained directly from environmental microbial
communities with the aim to linking their structures
with functional roles. The field has been aided in the
unprecedented advancement through high-throughput omics
data sequencing. The outcome of sequencing are
biologically rich data sets. Metagenomic data
consisting of microbial species which outnumber
microbial samples, lead to the ``curse of
dimensionality'' in datasets. Hence, the focus in
metagenomics studies has moved towards developing
efficient computational models using Machine Learning
ML, reducing the computational cost. In this paper, we
comprehensively assessed various ML approaches to
classifying high-dimensional human microbiota
effectively into their functional phenotypes. We
propose the application of embedded feature selection
methods, namely, Extreme Gradient Boosting and
Penalized Logistic Regression to determine important
microbial species. The resultant feature set enhanced
the performance of one of the most popular
state-of-the-art methods, Random Forest RF over
metagenomic studies. Experimental results indicate that
the proposed method achieved best results in terms of
accuracy, area under the Receiver Operating
Characteristic curve ROC-AUC, and major improvement in
processing time. It outperformed other feature
selection methods of filters or wrappers over RF and
classifiers such as Support Vector Machine SVM, Extreme
Learning Machine ELM, and $k$-Nearest Neighbors
$k$-NN.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2019:NSA,
author = "Min Li and Li Tang and Zhongxiang Liao and Junwei Luo
and Fang-Xiang Wu and Yi Pan and Jianxin Wang",
title = "A Novel Scaffolding Algorithm Based on Contig Error
Correction and Path Extension",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "764--773",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2858267",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The sequence assembly process can be divided into
three stages: contigs extension, scaffolding, and gap
filling. The scaffolding method is an essential step
during the process to infer the direction and sequence
relationships between the contigs. However, scaffolding
still faces the challenges of uneven sequencing depth,
genome repetitive regions, and sequencing errors, which
often leads to many false relationships between
contigs. The performance of scaffolding can be improved
by removing potential false conjunctions between
contigs. In this study, a novel scaffolding algorithm
which is on the basis of path extension
Loose-Strict-Loose strategy and contig error
correction, called iLSLS. iLSLS helps reduce the false
relationships between contigs, and improve the accuracy
of subsequent steps. iLSLS utilizes a scoring function,
which estimates the correctness of candidate paths by
the distribution of paired reads, and try to conduction
the extension with the path which is scored the
highest. What's more, iLSLS can precisely estimate the
gap size. We conduct experiments on two real datasets,
and the results show that LSLS strategy is efficient to
increase the correctness of scaffolds, and iLSLS
performs better than other scaffolding methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2019:EPH,
author = "Xiaolong Zhang and Xiaoli Lin and Jiafu Zhao and
Qianqian Huang and Xin Xu",
title = "Efficiently Predicting Hot Spots in {PPIs} by
Combining Random Forest and Synthetic Minority
Over-Sampling Technique",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "774--781",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2871674",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Hot spot residues bring into play the vital function
in bioinformatics to find new medications such as drug
design. However, current datasets are predominately
composed of non-hot spots with merely a tiny percentage
of hot spots. Conventional hot spots prediction methods
may face great challenges towards the problem of
imbalance training samples. This paper presents a
classification method combining with random forest
classification and oversampling strategy to improve the
training performance. A strategy with an oversampling
ability is used to generate hot spots data to balance
the given training set. Random forest classification is
then invoked to generate a set of forest trees for this
oversampled training set. The final prediction
performance can be computed recursively after the
oversampling and training process. This proposed method
is capable of randomly selecting features and
constructing a robust random forest to avoid
overfitting the training set. Experimental results from
three data sets indicate that the performance of hot
spots prediction has been significantly improved
compared with existing classification methods.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yuan:2019:IMO,
author = "Lin Yuan and Le-Hang Guo and Chang-An Yuan and Youhua
Zhang and Kyungsook Han and Asoke K. Nandi and Barry
Honig and De-Shuang Huang",
title = "Integration of Multi-Omics Data for Gene Regulatory
Network Inference and Application to Breast Cancer",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "782--791",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2866836",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Underlying a cancer phenotype is a specific gene
regulatory network that represents the complex
regulatory relationships between genes. It remains,
however, a challenge to find cancer-related gene
regulatory network because of insufficient sample sizes
and complex regulatory mechanisms in which gene is
influenced by not only other genes but also other
biological factors. With the development of
high-throughput technologies and the unprecedented
wealth of multi-omics data it gives us a new
opportunity to design machine learning method to
investigate underlying gene regulatory network. In this
paper, we propose an approach, which use Biweight
Midcorrelation to measure the correlation between
factors and make use of Nonconvex Penalty based sparse
regression for Gene Regulatory Network inference
BMNPGRN. BMNCGRN incorporates multi-omics data
including DNA methylation and copy number variation and
their interactions in gene regulatory network model.
The experimental results on synthetic datasets show
that BMNPGRN outperforms popular and state-of-the-art
methods including DCGRN, ARACNE, and CLR under false
positive control. Furthermore, we applied BMNPGRN on
breast cancer BRCA data from The Cancer Genome Atlas
database and provided gene regulatory network.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Deng:2019:IKG,
author = "Su-Ping Deng and Wei-Li Guo",
title = "Identifying Key Genes of Liver Cancer by Networking of
Multiple Data Sets",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "792--800",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2874238",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Liver cancer is one of the deadliest cancers in the
world. To find effective therapies for this cancer, it
is indispensable to identify key genes, which may play
critical roles in the incidence of the liver cancer. To
identify key genes of the liver cancer with high
accuracy, we integrated multiple microarray gene
expression data sets to compute common differentially
expressed genes, which will result more accurate than
those from individual data set. To find the main
functions or pathways that these genes are involved in,
some enrichment analyses were performed including
functional enrichment analysis, pathway enrichment
analysis, and disease association study. Based on these
genes, a protein-protein interaction network was
constructed and analyzed to identify key genes of the
liver cancer by combining the local and global
influence of nodes in the network. The identified key
genes, such as TOP2A, ESR1, and KMO, have been
demonstrated to be key biomarkers of the liver cancer
in many publications. All the results suggest that our
method can effectively identify key genes of the liver
cancer. Moreover, our method can be applied to other
types of data sets to select key genes of other complex
diseases.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wu:2019:CDM,
author = "Peng Wu and Dong Wang",
title = "Classification of a {DNA} Microarray for Diagnosing
Cancer Using a Complex Network Based Method",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "801--808",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2868341",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Applications that classify DNA microarray expression
data are helpful for diagnosing cancer. Many attempts
have been made to analyze these data; however, new
methods are needed to obtain better results. In this
study, a Complex Network CN classifier was exploited to
implement the classification task. An algorithm was
used to initialize the structure, which allowed input
variables to be selected over layered connections and
different activation functions for different nodes.
Then, a hybrid method integrated the Genetic
Programming and the Particle Swarm Optimization
algorithms was used to identify an optimal structure
with the parameters encoded in the classifier. The
single CN classifier and an ensemble of CN classifiers
were tested on four bench data sets. To ensure
diversity of the ensemble classifiers, we constructed a
base classifier using different feature sets, i.e.,
Pearson's correlation, Spearman's correlation,
euclidean distance, Cosine coefficient, and the
Fisher-ratio. The experimental results suggest that a
single classifier can be used to obtain
state-of-the-art results and the ensemble yielded
better results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{You:2019:EEL,
author = "Zhu-Hong You and Wen-Zhun Huang and Shanwen Zhang and
Yu-An Huang and Chang-Qing Yu and Li-Ping Li",
title = "An Efficient Ensemble Learning Approach for Predicting
Protein-Protein Interactions by Integrating Protein
Primary Sequence and Evolutionary Information",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "809--817",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2882423",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein-protein interactions PPIs perform a very
important function in a number of cellular processes,
including signal transduction, post-translational
modifications, apoptosis, and cell growth. Deregulation
of PPIs will lead to many diseases, including
pernicious anemia or cancers. Although a large number
of high-throughput techniques are designed to generate
PPIs data, they are generally expensive, inefficient,
and labor-intensive. Hence, there is an urgent need for
developing a computational method to accurately and
rapidly detect PPIs. In this article, we proposed a
highly efficient method to detect PPIs by integrating a
new protein sequence substitution matrix feature
representation and ensemble weighted sparse
representation model classifier. The proposed method is
demonstrated on Saccharomyces cerevisiae dataset and
achieved 99.26 percent prediction accuracy with 98.53
percent sensitivity at precision of 100 percent, which
is shown to have much higher predictive accuracy than
the state-of-the-art methods. Extensive contrast
experiments are performed with the benchmark data set
from Human and Helicobacter pylori that our proposed
method can achieve outstanding better success rates
than other existing approaches in this problem.
Experiment results illustrate that our proposed method
presents an economical approach for computational
building of PPI networks, which can be a helpful
supplementary method for future proteomics
researches.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhong:2019:FEF,
author = "Hua Zhong and Mingzhou Song",
title = "A Fast Exact Functional Test for Directional
Association and Cancer Biology Applications",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "818--826",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2809743",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Directional association measured by functional
dependency can answer important questions on
relationships between variables, for example, in
discovery of molecular interactions in biological
systems. However, when one has no prior information
about the functional form of a directional association,
there is not a widely established statistical procedure
to detect such an association. To address this issue,
here we introduce an exact functional test for
directional association by examining the strength of
functional dependency. It is effective in promoting
functional patterns by reducing statistical power on
dependent non-functional patterns. We designed an
algorithm to carry out the test using a fast
branch-and-bound strategy, which achieved a substantial
speedup over brute-force enumeration. On data from an
epidemiological study of liver cancer, the test
identified the hepatitis status of a subject as the
most influential risk factor among others for the
cancer phenotype. On human lung cancer transcriptome
data, the test selected 1068 transcription start sites
of putative noncoding RNAs directionally associated
with the presence or absence of lung cancer, stronger
than 95 percent transcription start sites of 694
curated cancer genes. These predictions include
non-monotonic interaction patterns, to which other
routine tests were insensitive. Complementing symmetric
non-directional association methods such as Fisher's
exact test, the exact functional test is a unique exact
statistical test for evaluating evidence for causal
relationships.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Samaddar:2019:MDP,
author = "Sandip Samaddar and Rituparna Sinha and Rajat K. De",
title = "A Model for Distributed Processing and Analyses of
{NGS} Data under Map-Reduce Paradigm",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "827--840",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2816022",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Massively parallel sequencing technique, introduced by
NGS technology, has resulted in an exponential growth
of sequencing data, with greatly reduced cost and
increased throughput. This huge explosion of data has
introduced new challenges in regard to its storage,
integration, processing, and analyses. In this paper,
we have proposed a novel distributed model under
Map-Reduce paradigm to address the NGS big data
problem. The architecture of the model involves
Map-Reduce based modularized approach involving three
different phases that support various analytical
pipelines. The first phase will generate detailed base
level information of various individual genomes, by
granulating the alignment data. The other two phases
independently process this base level information in
parallel. One of these two phases will provide an
integrated DNA profile of multiple individuals, whereas
the other phase will generate contigs with similar
features in an individual. Each of these three phases
will generate a repository of genomic information that
will facilitate other analytical pipelines. A simulated
and real experimental prototypes has been provided as
results to show the effectiveness of the model and its
superiority over a few existing popular models and
tools. A detailed description of the scope of
applications of this model is also included in this
article.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sun:2019:MDN,
author = "Dongdong Sun and Minghui Wang and Ao Li",
title = "A Multimodal Deep Neural Network for Human Breast
Cancer Prognosis Prediction by Integrating
Multi-Dimensional Data",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "841--850",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2806438",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Breast cancer is a highly aggressive type of cancer
with very low median survival. Accurate prognosis
prediction of breast cancer can spare a significant
number of patients from receiving unnecessary adjuvant
systemic treatment and its related expensive medical
costs. Previous work relies mostly on selected gene
expression data to create a predictive model. The
emergence of deep learning methods and
multi-dimensional data offers opportunities for more
comprehensive analysis of the molecular characteristics
of breast cancer and therefore can improve diagnosis,
treatment, and prevention. In this study, we propose a
Multimodal Deep Neural Network by integrating
Multi-dimensional Data MDNNMD for the prognosis
prediction of breast cancer. The novelty of the method
lies in the design of our method's architecture and the
fusion of multi-dimensional data. The comprehensive
performance evaluation results show that the proposed
method achieves a better performance than the
prediction methods with single-dimensional data and
other existing approaches. The source code implemented
by TensorFlow 1.0 deep learning library can be
downloaded from the Github:
https://github.com/USTC-HIlab/MDNNMD.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{VanDyck:2019:RSP,
author = "Michiel {Van Dyck} and Xavier Woot de Trixhe and An
Vermeulen and Wim Vanroose",
title = "A Robust Simulator for Physiologically Structured
Population Models",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "851--864",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2810077",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "A framework to simulate physiologically structured
population PSP models on high performance compute HPC
infrastructure is built. Based on the model of a single
cell, billions of cells can be simulated in an
efficient way, allowing fast simulation of the
interaction of an entire organ with other body parts.
Through combination of three state-of-the-art
algorithms, the simulation time is decreased with
multiple orders of magnitude. First: PSP modelling
exploits the fact that a lot of the cells behave
identically at the same time which results in multiple
orders of magnitude speed-up. The second speed-up is
achieved by using an unconditionally stable, partial
differential equation solver which allows big
time-stepping by trading off speed with precision. The
third speed-up is due to the fact that the framework is
designed with HPC cluster use in mind. The PSP
simulator is mathematically derived to have maximal
stability. Simulation results are validated and
simulation speed and accuracy are measured.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sharifi:2019:ARC,
author = "Maryam Sharifi and Arta A. Jamshidi and Nazanin Namazi
Sarvestani",
title = "An Adaptive Robust Control Strategy in a Cancer
Tumor-Immune System Under Uncertainties",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "865--873",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2803175",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this work, we propose an adaptive robust control
for a second order nonlinear model of the interaction
between cancer and immune cells of the body to control
the growth of cancer and maintain the number of immune
cells in an appropriate level. Up to now, most of the
control approaches are based on minimizing the drug
dosage based on an optimal control structure. However,
in many cases, measuring the exact quantity of the
model parameters is not possible. This is due to
limitation in measuring devices, variational and
undetermined characteristics of micro-environmental
factors and the variable nature of parameters during
the growth and treatment phases of cancer. It is of
great importance to present a control strategy that can
deal with these variables and unknown factors in a
nonlinear model. Adaptive control is a suitable choice
to achieve this goal. We assume linear uncertainties
for the model parameters and employ a sliding term for
updating the estimated parameters and the control
signals. Moreover, due to difficulties in measuring the
number of immune cells in biological experiments, an
estimation technique is applied to infer this value.
The convergence of the estimated number of immune cells
to the actual value is shown. The stability and
convergence of the number of cancer and immune cells to
the specified target values are also proved using a
time-varying Lyapunov function. Finally, we have shown
the performance of the proposed control strategy in the
context of various computational results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nanni:2019:BCH,
author = "Loris Nanni and Sheryl Brahnam and Stefano Ghidoni and
Alessandra Lumini",
title = "Bioimage Classification with Handcrafted and Learned
Features",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "874--885",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2821127",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Bioimage classification is increasingly becoming more
important in many biological studies including those
that require accurate cell phenotype recognition,
subcellular localization, and histopathological
classification. In this paper, we present a new General
Purpose GenP bioimage classification method that can be
applied to a large range of classification problems.
The GenP system we propose is an ensemble that combines
multiple texture features both handcrafted and learned
descriptors for superior and generalizable
discriminative power. Our ensemble obtains a boosting
of performance by combining local features, dense
sampling features, and deep learning features. Each
descriptor is used to train a different Support Vector
Machine that is then combined by sum rule. We evaluate
our method on a diverse set of bioimage classification
tasks each represented by a benchmark database,
including some of those available in the IICBU 2008
database. Each bioimage classification task represents
a typical subcellular, cellular, and tissue level
classification problem. Our evaluation on these
datasets demonstrates that the proposed GenP bioimage
ensemble obtains state-of-the-art performance without
any ad-hoc dataset tuning of the parameters thereby
avoiding any risk of overfitting/overtraining. To
reproduce the experiments reported in this paper, the
MATLAB code of all the descriptors is available at
https://github.com/LorisNanni and
https://www.dropbox.com/s/bguw035yrqz0pwp/ElencoCode.docx?dl=0.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cheng:2019:BGA,
author = "Haoyu Cheng and Yong Zhang and Yun Xu",
title = "{BitMapper2}: a {GPU}-Accelerated All-Mapper Based on
the Sparse $q$-Gram Index",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "886--897",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2822687",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The explosive growth of next-generation sequencing NGS
read datasets drives a need for new faster read
mappers. One class of read mappers, called all-mappers,
is designed to identify all mapping locations of each
read. Many all-mappers have been developed over the
past few years, but they are either time-consuming or
memory-consuming. Here, we present BitMapper2, a
GPU-accelerated read mapper that reports all mapping
locations of NGS reads. To make full use of the
parallel processing capability of GPUs, BitMapper2
proposes the sparse q-gram index, which reduces the
memory requirement and the data transfer time between
GPU and CPU. We also design the filtration part and the
verification part of BitMapper2 specifically for the
architecture of GPU. In addition, BitMapper2 is still
time-efficient and memory-efficient even if there is no
GPU available. Experiments show that BitMapper2 was
significantly faster than the state-of-the-art
all-mappers, while requiring less space.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Whidden:2019:CUS,
author = "Chris Whidden and Frederick A. Matsen",
title = "Calculating the Unrooted Subtree Prune-and-Regraft
Distance",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "898--911",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2802911",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The subtree prune-and-regraft SPR distance metric is a
fundamental way of comparing evolutionary trees. It has
wide-ranging applications, such as to study lateral
genetic transfer, viral recombination, and Markov chain
Monte Carlo phylogenetic inference. Although the rooted
version of SPR distance can be computed relatively
efficiently between rooted trees using
fixed-parameter-tractable maximum agreement forest MAF
algorithms, no MAF formulation is known for the
unrooted case. Correspondingly, previous algorithms are
unable to compute unrooted SPR distances larger than 7.
In this paper, we substantially advance understanding
of and computational algorithms for the unrooted SPR
distance. First, we identify four properties of optimal
SPR paths, each of which suggests that no MAF
formulation exists in the unrooted case. Then, we
introduce the replug distance, a new lower bound on the
unrooted SPR distance that is amenable to MAF methods,
and give an efficient fixed-parameter algorithm for
calculating it. Finally, we develop a ``progressive
A*'' search algorithm using multiple heuristics,
including the TBR and replug distances, to exactly
compute the unrooted SPR distance. Our algorithm is
nearly two orders of magnitude faster than previous
methods on small trees, and allows computation of
unrooted SPR distances as large as 14 on trees with 50
leaves.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2019:CAG,
author = "Pei Wang and Daojie Wang and Jinhu Lu",
title = "Controllability Analysis of a Gene Network for
\bioname{Arabidopsis thaliana} Reveals Characteristics
of Functional Gene Families",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "912--924",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2821145",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Based on structural controllability of complex
networks and a constructed gene network with 9,241
nodes for Arabidopsis thaliana, we classified nodes
into five categories via their roles in control or node
deletion, including indispensable, neutral,
dispensable, driver, and critical driver nodes. The
indispensable nodes can increase the number of drivers
after deletion, which are never drivers or critical
drivers. About 10 percent of nodes are indispensable.
However, more than 60 percent of nodes are neutral
ones. More than 62 percent of nodes are drivers, which
indicates the gene network is very difficult to be
fully controlled. Gene Ontology GO enrichment analysis
reveals that different sets of nodes have preferred
biological functions and processes. The indispensable
nodes are significantly enriched as essential genes,
drought responsive and abscisic acid ABA independent
genes, transcriptional factors TFs, core cell cycle
genes, and ABA and Gibberellin GA related genes. The
critical drivers are enriched as receptor kinase-like
genes, while shorted in WRKY TFs and functional genes
that are enriched in the indispensable nodes.
Robustness analysis based on node and edge additions,
edge rewiring indicate the obtained conclusions are
robust to network perturbations. Our investigations
clarify control roles of some gene families and provide
potential implications for identifying functional genes
in other plant species, such as drought responsive
genes and TFs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Thiele:2019:DOE,
author = "Sven Thiele and Sandra Heise and Wiebke Hessenkemper
and Hannes Bongartz and Melissa Fensky and Fred Schaper
and Steffen Klamt",
title = "Designing Optimal Experiments to Discriminate
Interaction Graph Models",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "925--935",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2812184",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Modern methods for the inference of cellular networks
from experimental data often express nondeterminism by
proposing an ensemble of candidate models with similar
properties. To further discriminate among these model
candidates, new experiments need to be carried out.
Theoretically, the number of possible experiments is
exponential in the number of possible perturbations. In
praxis, experiments are expensive and usually there
exist several constraints limiting which experiments
can be performed. Limiting factors may exist on the
combinations of perturbations that are technically
possible, which components can be measured, and
limitations on the number of affordable experiments.
Further, not all experiments are equally well suited to
discriminate model candidates. Therefore, the goal of
optimal experiment design is to determine those
experiments that discriminate most of the candidates
while minimizing the costs. We present an approach for
experiment planning with interaction graph models and
sign consistency methods. This new approach can be used
in combination with methods for network inference and
consistency checking. The proposed method determines
experiments which are most suitable to deliver results
that reduce the number of candidate models. We applied
our method to study the Erythropoietin signal
transduction in human kidney cells HEK293. We first
used simulated experiment data from an ODE model to
demonstrate in silico that our experimental design
results in the inference of the gold standard model.
Finally, we used the approach to plan in vivo
experiments that enabled us to discriminate model
candidates for the Erythropoietin signal transduction
in this cell line.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2019:DPC,
author = "Yi-Yan Zhang and Qin Li and Yi Xin and Wei-Qi Lv",
title = "Differentiating Prostate Cancer from Benign Prostatic
Hyperplasia Using {PSAD} Based on Machine Learning:
Single-Center Retrospective Study in {China}",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "936--941",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2822675",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The incidence of prostate cancer increases annually.
Prostate cancer is an underreported and emerging
problem in China. We conducted a cross-sectional study
of 392 eligible patients from 710 men with prostate
cancer or benign prostatic hyperplasia between 2000 and
2003. For total prostate-specific antigen, age, three
diameters of prostate, prostate volume and
prostate-specific antigen density seven indices,
analysis of variance, and t test were used to analyze
the difference between the groups. A decision tree with
pruning was established using the prostate-specific
antigen density, age, and transversal diameter of the
prostate to screen the patient with prostate cancer.
According to the established decision tree model,
prostate-specific antigen density was the most
important factor affecting the occurrence of prostate
cancer. In elderly people over the age of 83 years, the
transverse diameter of prostate cancer was smaller than
that of benign prostatic hyperplasia, with
prostate-specific antigen density less than 0.49 ng/L2.
No additional index was introduced, and the detection
rate of prostate cancer was 86.6 percent. The
specificity was enhanced to 78.1 percent.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kaloudas:2019:EEB,
author = "Dimitrios Kaloudas and Nikolet Pavlova and Robert
Penchovsky",
title = "{EBWS}: Essential Bioinformatics {Web} Services for
Sequence Analyses",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "942--953",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2816645",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The Essential Bioinformatics Web Services EBWS are
implemented on a new PHP-based server that provides
useful tools for analyses of DNA, RNA, and protein
sequences applying a user-friendly interface. Nine
Web-based applets are currently available on the Web
server. They include reverse complementary DNA and
random DNA/RNA/peptide oligomer generators, a pattern
sequence searcher, a DNA restriction cutter, a
prokaryotic ORF finder, and a random DNA/RNA mutation
generator. It also includes calculators of melting
temperature TM of DNA/DNA, RNA/RNA, and DNA/RNA
hybrids, a guide RNA gRNA generator for the CRISPR/Cas9
system and an annealing temperature calculator for
multiplex PCR. The pattern-searching applet has no
limitations in the number of motif inputs and applies a
toolbox of Regex quantifiers that can be used for
defining complex sequence queries of RNA, DNA, and
protein sequences. The DNA enzyme digestion program
utilizes a large database of 1,502 restriction enzymes.
The gRNA generator has a database of 25 bacterial
genomes searchable for gRNA target sequences and has an
option for searching in any genome sequence given by
the user. All programs are permanently available online
at http://penchovsky.atwebpages.com/applications.php
without any restrictions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xu:2019:LRE,
author = "Bo Xu and Hongfei Lin and Yuan Lin",
title = "Learning to Refine Expansion Terms for Biomedical
Information Retrieval Using Semantic Resources",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "954--966",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2801303",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "With the rapid development of biomedicine, the number
of biomedical articles has increased accordingly, which
presents a great challenge for biologists trying to
keep up with the latest research. Information retrieval
seeks to meet this challenge by searching among a large
number of articles based on given queries and providing
the most relevant ones to fulfill information needs. As
an effective information retrieval technique, query
expansion has some room for improvement to achieve the
desired performance when directly applied for
biomedical information retrieval because there exist
many domain-related terms both in users' queries and in
related articles. To solve this problem, we propose a
biomedical query expansion framework based on
learning-to-rank methods, in which we refine candidate
expansion terms by training term-ranking models to
select the most relevant terms. To train the
term-ranking models, we first propose a
pseudo-relevance feedback method based on MeSH to
select candidate expansion terms and then represent the
candidate terms as feature vectors by defining both the
corpus-based term features and the resource-based term
features. Experimental results obtained for TREC
genomics datasets show that our method can capture more
relevant terms to expand the original query and
effectively improve biomedical information retrieval
performance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{He:2019:MBP,
author = "Tiantian He and Keith C. C. Chan",
title = "Measuring Boundedness for Protein Complex
Identification in {PPI} Networks",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "967--979",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2822709",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The problem of identifying protein complexes in
Protein-Protein Interaction PPI networks is usually
formulated as the problem of identifying dense regions
in such networks. In this paper, we present a novel
approach, called TBPCI, to identify protein complexes
based instead on the concept of a measure of
boundedness. Such a measure is defined as an objective
function of a Jaccard Index-based connectedness measure
which takes into consideration how much two proteins
within a network are connected to each other, and an
association measure which takes into consideration how
much two connecting proteins are associated based on
their attributes found in the Gene Ontology database.
Based on the above two measures, the objective function
is derived to capture how strong the proteins can be
considered as bounded together and the objective value
is therefore referred as the aggregated degree of
boundedness. To identify protein complexes, TBPCI
computes the degree of boundedness between all possible
pairwise proteins. Then, TBPCI uses a
Breadth-First-Search method to determine whether a
protein-pair should be incorporated into the same
complex. TBPCI has been tested with several real data
sets and the experimental results show it is an
effective approach for identifying protein complexes in
PPI networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2019:MMV,
author = "Aiguo Wang and Ye Chen and Ning An and Jing Yang and
Lian Li and Lili Jiang",
title = "Microarray Missing Value Imputation: a Regularized
Local Learning Method",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "980--993",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2810205",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Microarray experiments on gene expression inevitably
generate missing values, which impedes further
downstream biological analysis. Therefore, it is key to
estimate the missing values accurately. Most of the
existing imputation methods tend to suffer from the
over-fitting problem. In this study, we propose two
regularized local learning methods for microarray
missing value imputation. Motivated by the grouping
effect of $ L_2 $ regularization, after selecting the
target gene, we train an $ L_2 $ Regularized Local
Least Squares imputation model RLLSimpute_L2 on the
target gene and its neighbors to estimate the missing
values of the target gene. Furthermore, RLLSimpute_L2
imputes the missing values in an ascending order based
on the associated missing rate with each target gene.
This contributes to fully utilizing the previously
estimated values. Besides $ L_2 $, we further explore $
L_1 $ regularization and propose an $ L_1 $ Regularized
Local Least Squares imputation model RLLSimpute_L1. To
evaluate their effectiveness, we conducted extensive
experimental studies on six benchmark datasets covering
both time series and non-time series cases. Nine
state-of-the-art imputation methods are compared with
RLLSimpute_L2 and RLLSimpute_L1 in terms of three
performance metrics. The comparative experimental
results indicate that RLLSimpute_L2 outperforms its
competitors by achieving smaller imputation errors and
better structure preservation of differentially
expressed genes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Song:2019:PGA,
author = "You Song and Siyu Yang and Jinzhi Lei",
title = "{ParaCells}: a {GPU} Architecture for Cell-Centered
Models in Computational Biology",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "994--1006",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2814570",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pvm.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In computational biology, the hierarchy of biological
systems requires the development of flexible and
powerful computational tools. Graphics processing unit
GPU architecture has been a suitable device for
parallel computing in simulating multi-cellular
systems. However, in modeling complex biological
systems, scientists often face two tasks, mathematical
formulation and skillful programming. In particular,
specific programming skills are needed for GPU
programming. Therefore, the development of an
easy-to-use computational architecture, which utilizes
GPU for parallel computing and provides intuitive
interfaces for simple implementation, is needed so that
general scientists can perform GPU simulations without
knowing much about the GPU architecture. Here, we
introduce ParaCells, a cell-centered GPU simulation
architecture for NVIDIA compute unified device
architecture CUDA. ParaCells was designed as a
versatile architecture that connects the user logic in
C++ with NVIDIA CUDA runtime and is specific to the
modeling of multi-cellular systems. An advantage of
ParaCells is its object-oriented model declaration,
which allows it to be widely applied to many biological
systems through the combination of basic biological
concepts. We test ParaCells with two applications. Both
applications are significantly faster when compared
with sequential as well as parallel OpenMP and OpenACC
implementations. Moreover, the simulation programs
based on ParaCells are cleaner and more readable than
other versions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Patoary:2019:PSD,
author = "Mohammad Nazrul Ishlam Patoary and Carl Tropper and
Robert A. McDougal and Zhongwei Lin and William W.
Lytton",
title = "Parallel Stochastic Discrete Event Simulation of
Calcium Dynamics in Neuron",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "1007--1019",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2756930",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The intra-cellular calcium signaling pathways of a
neuron depends on both biochemical reactions and
diffusions. Some quasi-isolated compartments e.g.,
spines are so small and calcium concentrations are so
low that one extra molecule diffusing in by chance can
make a nontrivial difference in concentration
percentage-wise. These rare events can affect dynamics
discretely in such a way that they cannot be evaluated
by a deterministic and continuous simulation.
Stochastic models of such a system provide a more
detailed understanding of these systems than existing
deterministic models because they capture their
behavior at a molecular level. Our research focuses on
the development of a high performance parallel discrete
event simulation environment, Neuron Time Warp NTW,
which is intended for use in the parallel simulation of
stochastic reaction-diffusion systems such as
intra-calcium signaling. NTW is integrated with NEURON,
a simulator which is widely used within the
neuroscience community. We simulate two models, a
calcium buffer and a calcium wave model. The calcium
buffer model is employed in order to verify the
correctness and performance of NTW by comparing it to a
sequential deterministic simulation in NEURON. We also
derived a discrete event calcium wave model from a
deterministic model using the stochastic $ \text {IP}_3
\text {R} $ structure.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Fang:2019:PPB,
author = "Chao Fang and Yi Shang and Dong Xu",
title = "Prediction of Protein Backbone Torsion Angles Using
Deep Residual Inception Neural Networks",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "1020--1028",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2814586",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Prediction of protein backbone torsion angles Psi and
Phi can provide important information for protein
structure prediction and sequence alignment. Existing
methods for Psi-Phi angle prediction have significant
room for improvement. In this paper, a new deep
residual inception network architecture, called
DeepRIN, is proposed for the prediction of Psi-Phi
angles. The input to DeepRIN is a feature matrix
representing a composition of physico-chemical
properties of amino acids, a 20-dimensional
position-specific substitution matrix PSSM generated by
PSI-BLAST, a 30-dimensional hidden Markov Model
sequence profile generated by HHBlits, and predicted
eight-state secondary structure features. DeepRIN is
designed based on inception networks and residual
networks that have performed well on image
classification and text recognition. The architecture
of DeepRIN enables effective encoding of local and
global interactions between amino acids in a protein
sequence to achieve accurate prediction. Extensive
experimental results show that DeepRIN outperformed the
best existing tools significantly. Compared to the
recently released state-of-the-art tool, SPIDER3,
DeepRIN reduced the Psi angle prediction error by more
than 5 degrees and the Phi angle prediction error by
more than 2 degrees on average. The executable tool of
DeepRIN is available for download at
http://dslsrv8.cs.missouri.edu/~cf797/MUFoldAngle/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Urban:2019:DLD,
author = "Gregor Urban and Kevin Bache and Duc T. T. Phan and
Agua Sobrino and Alexander K. Shmakov and Stephanie J.
Hachey and Christopher C. W. Hughes and Pierre Baldi",
title = "Deep Learning for Drug Discovery and Cancer Research:
Automated Analysis of Vascularization Images",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "1029--1035",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2841396",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Likely drug candidates which are identified in
traditional pre-clinical drug screens often fail in
patient trials, increasing the societal burden of drug
discovery. A major contributing factor to this
phenomenon is the failure of traditional in vitro
models of drug response to accurately mimic many of the
more complex properties of human biology. We have
recently introduced a new microphysiological system for
growing vascularized, perfused microtissues that more
accurately models human physiology and is suitable for
large drug screens. In this work, we develop a machine
learning model that can quickly and accurately flag
compounds which effectively disrupt vascular networks
from images taken before and after drug application in
vitro. The system is based on a convolutional neural
network and achieves near perfect accuracy while
committing potentially no expensive false negatives.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Singh:2019:EPG,
author = "Kumar Saurabh Singh and Bartlomiej J. Troczka and
Katherine Beadle and Linda M. Field and T. G. Emyr
Davies and Martin S. Williamson and Ralf Nauen and
Chris Bass",
title = "Extension of Partial Gene Transcripts by Iterative
Mapping of {RNA-Seq} Raw Reads",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "1036--1041",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2865309",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Many non-model organisms lack reference genomes and
the sequencing and de novo assembly of an organisms
transcriptome is an affordable means by which to
characterize the coding component of its genome.
Despite the advances that have made this possible,
assembling a transcriptome without a known reference
usually results in a collection of full-length and
partial gene transcripts. The downstream analysis of
genes represented as partial transcripts then often
requires further experimental work in the laboratory in
order to obtain full-length sequences. We have explored
whether partial transcripts, encoding genes of interest
present in de novo assembled transcriptomes of a model
and non-model insect species, could be further extended
by iterative mapping against the raw transcriptome
sequencing reads. Partial sequences encoding cytochrome
P450s and carboxyl/cholinesterase were used in this
analysis, because they are large multigene families and
exhibit significant variation in expression. We present
an effective method to improve the contiguity of
partial transcripts in silico that, in the absence of a
reference genome, may be a quick and cost-effective
alternative to their extension by laboratory
experimentation. Our approach resulted in the
successful extension of incompletely assembled
transcripts, often to full length. We experimentally
validated these results in silico and using real-time
PCR and sequencing.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Tu:2019:IGN,
author = "Jia-Juan Tu and Le Ou-Yang and Xiaohua Hu and Xiao-Fei
Zhang",
title = "Inferring Gene Network Rewiring by Combining Gene
Expression and Gene Mutation Data",
journal = j-TCBB,
volume = "16",
number = "3",
pages = "1042--1048",
month = may,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2834529",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Aug 23 11:22:19 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gene dependency networks often undergo changes with
respect to different disease states. Understanding how
these networks rewire between two different disease
states is an important task in genomic research.
Although many computational methods have been proposed
to undertake this task via differential network
analysis, most of them are designed for a predefined
data type. With the development of the high throughput
technologies, gene activity measurements can be
collected from different aspects e.g., mRNA expression
and DNA mutation. These different data types might
share some common characteristics and include certain
unique properties of data type. New methods are needed
to explore the similarity and difference between
differential networks estimated from different data
types. In this study, we develop a new differential
network inference model which identifies gene network
rewiring by combining gene expression and gene mutation
data. Similarities and differences between different
data types are learned via a group bridge penalty
function. Simulation studies have demonstrated that our
method consistently outperforms the competing methods.
We also apply our method to identify gene network
rewiring associated with ovarian cancer platinum
resistance from The Cancer Genome Atlas data. There are
certain differential edges common to both data types
and some differential edges unique to individual data
types. Hub genes in the differential networks inferred
by our method play important roles in ovarian cancer
drug resistance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sarkar:2019:NAC,
author = "Aisharjya Sarkar and Yuanfang Ren and Rasha Elhesha
and Tamer Kahveci",
title = "A New Algorithm for Counting Independent Motifs in
Probabilistic Networks",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1049--1062",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2821666",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Biological networks provide great potential to
understand how cells function. Motifs are topological
patterns which are repeated frequently in a specific
network. Network motifs are key structures through
which biological networks operate. However, counting
independent i.e., non-overlapping instances of a
specific motif remains to be a computationally hard
problem. Motif counting problem becomes computationally
even harder for biological networks as biological
interactions are uncertain events. The main challenge
behind this problem is that different embeddings of a
given motif in a network can share edges. Such edges
can create complex computational dependencies between
different instances of the given motif when considering
uncertainty of those edges. In this paper, we develop a
novel algorithm for counting independent instances of a
specific motif topology in probabilistic biological
networks. We present a novel mathematical model to
capture the dependency between each embedding and all
the other embeddings, which it overlaps with. We prove
the correctness of this model. We evaluate our model on
real and synthetic networks with different probability,
and topology models as well as reasonable range of
network sizes. Our results demonstrate that our method
counts non-overlapping embeddings in practical time for
a broad range of networks.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Markin:2019:CMP,
author = "Alexey Markin and Oliver Eulenstein",
title = "Computing {Manhattan} Path-Difference Median Trees: a
Practical Local Search Approach",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1063--1076",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2718507",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Median tree problems are powerful tools for inferring
large-scale phylogenetic trees that hold enormous
promise for society at large. Such problems seek a
median tree for a given collection of input trees under
some problem-specific distance. Here, we introduce a
median tree problem under the classic Manhattan
path-difference distance. We show that this problem is
NP-hard, devise an ILP formulation, and provide an
effective local search heuristic that is based on
solving a local search problem exactly. Our algorithm
for the local search problem improves asymptotically by
a factor of $n$ on the best-known na{\"\i}ve solution,
where $n$ is the overall number of taxa in the input
trees. Finally, comparative phylogenetic studies using
considerably large empirical data and an accuracy
analysis for smaller phylogenetic trees reveal the
ability of our novel heuristic.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kordi:2019:EAD,
author = "Misagh Kordi and Mukul S. Bansal",
title = "Exact Algorithms for Duplication-Transfer-Loss
Reconciliation with Non-Binary Gene Trees",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1077--1090",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2710342",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Duplication-Transfer-Loss DTL reconciliation is a
powerful method for studying gene family evolution in
the presence of horizontal gene transfer. DTL
reconciliation seeks to reconcile gene trees with
species trees by postulating speciation, duplication,
transfer, and loss events. Efficient algorithms exist
for finding optimal DTL reconciliations when the gene
tree is binary. In practice, however, gene trees are
often non-binary due to uncertainty in the gene tree
topologies, and DTL reconciliation with non-binary gene
trees is known to be NP-hard. In this paper, we present
the first exact algorithms for DTL reconciliation with
non-binary gene trees. Specifically, we i show that the
DTL reconciliation problem for non-binary gene trees is
fixed-parameter tractable in the maximum degree of the
gene tree, ii present an exponential-time, but
in-practice efficient, algorithm to track and enumerate
all optimal binary resolutions of a non-binary input
gene tree, and iii apply our algorithms to a large
empirical data set of over 4,700 gene trees from 100
species to study the impact of gene tree uncertainty on
DTL-reconciliation and to demonstrate the applicability
and utility of our algorithms. The new techniques and
algorithms introduced in this paper will help
biologists avoid incorrect evolutionary inferences
caused by gene tree uncertainty.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ghosh:2019:FFS,
author = "Priyanka Ghosh and Ananth Kalyanaraman",
title = "{FastEtch}: a Fast Sketch-Based Assembler for
Genomes",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1091--1106",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2737999",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "De novo genome assembly describes the process of
reconstructing an unknown genome from a large
collection of short or long reads sequenced from the
genome. A single run of a Next-Generation Sequencing
NGS technology can produce billions of short reads,
making genome assembly computationally demanding both
in terms of memory and time. One of the major
computational steps in modern day short read assemblers
involves the construction and use of a string data
structure called the de Bruijn graph. In fact, a
majority of short read assemblers build the complete de
Bruijn graph for the set of input reads, and
subsequently traverse and prune low-quality edges, in
order to generate genomic ``contigs''-the output of
assembly. These steps of graph construction and
traversal, contribute to well over 90 percent of the
runtime and memory. In this paper, we present a fast
algorithm, FastEtch, that uses sketching to build an
approximate version of the de Bruijn graph for the
purpose of generating an assembly. The algorithm uses
Count-Min sketch, which is a probabilistic data
structure for streaming data sets. The result is an
approximate de Bruijn graph that stores information
pertaining only to a selected subset of nodes that are
most likely to contribute to the contig generation
step. In addition, edges are not stored; instead that
fraction which contribute to our contig generation are
detected on-the-fly. This approximate approach is
intended to significantly improve performance both
execution time and memory footprint whilst possibly
compromising on the output assembly quality. We present
two main versions of the assembler-one that generates
an assembly, where each contig represents a contiguous
genomic region from one strand of the DNA, and another
that generates an assembly, where the contigs can
straddle either of the two strands of the DNA. For
further scalability, we have implemented a
multi-threaded parallel code. Experimental results
using our algorithm conducted on E. coli, Yeast, C.
elegans, and Human Chr2 and Chr2+3 genomes show that
our method yields one of the best time-memory-quality
trade-offs, when compared against many state-of-the-art
genome assemblers.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Choudhury:2019:HAE,
author = "Olivia Choudhury and Ankush Chakrabarty and Scott J.
Emrich",
title = "Highly Accurate and Efficient Data-Driven Methods for
Genotype Imputation",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1107--1116",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2708701",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "High-throughput sequencing techniques have generated
massive quantities of genotype data. Haplotype phasing
has proven to be a useful and effective method for
analyzing these data. However, the quality of phasing
is undermined due to missing information. Imputation
provides an effective means of improving the underlying
genotype information. For model organisms, imputation
can rely on an available reference genotype panel and a
physical or genetic map. For non-model organisms, which
often do not have a genotype panel, it is important to
design an imputation technique that does not rely on
reference data. Here, we present Accurate Data-Driven
Imputation Technique ADDIT, which is composed of two
data-driven algorithms capable of handling data
generated from model and non-model organisms. The
non-model variant of ADDIT referred to as ADDIT-NM
employs statistical inference methods to impute missing
genotypes, whereas the model variant referred to as
ADDIT-M leverages a supervised learning-based approach
for imputation. We demonstrate that both variants of
ADDIT are more accurate, faster, and require less
memory than leading state-of-the-art imputation tools
using model human and non-model maize, apple, and grape
genotype data. Software Availability: The source code
of ADDIT and test data sets are available at
https://github.com/NDBL/ADDIT.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Pan:2019:KFP,
author = "Tony Pan and Patrick Flick and Chirag Jain and
Yongchao Liu and Srinivas Aluru",
title = "{Kmerind}: a Flexible Parallel Library for {$K$}-mer
Indexing of Biological Sequences on Distributed Memory
Systems",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1117--1131",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2760829",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Counting and indexing fixed length substrings, or
$k$-mers, in biological sequences is a key step in many
bioinformatics tasks including genome alignment and
mapping, genome assembly, and error correction. While
advances in next generation sequencing technologies
have dramatically reduced the cost and improved latency
and throughput, few bioinformatics tools can
efficiently process the datasets at the current
generation rate of 1.8 terabases per 3-day experiment
from a single sequencer. We present Kmerind, a high
performance parallel $k$-mer indexing library for
distributed memory environments. The Kmerind library
provides a set of simple and consistent APIs with
sequential semantics and parallel implementations that
are designed to be flexible and extensible. Kmerind's
$k$-mer counter performs similarly or better than the
best existing $k$-mer counting tools even on shared
memory systems. In a distributed memory environment,
Kmerind counts $k$-mers in a 120 GB sequence read
dataset in less than 13 seconds on 1024 Xeon CPU cores,
and fully indexes their positions in approximately 17
seconds. Querying for 1 percent of the $k$-mers in
these indices can be completed in 0.23 seconds and 28
seconds, respectively. Kmerind is the first $k$-mer
indexing library for distributed memory environments,
and the first extensible library for general $k$-mer
indexing and counting. Kmerind is available at
https://github.com/ParBLiSS/kmerind.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Siragusa:2019:LTA,
author = "Enrico Siragusa and Niina Haiminen and Filippo Utro
and Laxmi Parida",
title = "Linear Time Algorithms to Construct Populations
Fitting Multiple Constraint Distributions at Genomic
Scales",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1132--1142",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2760879",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Computer simulations can be used to study population
genetic methods, models, and parameters, as well as to
predict potential outcomes. For example, in plant
populations, predicting the outcome of breeding
operations can be studied using simulations. In-silico
construction of populations with pre-specified
characteristics is an important task in breeding
optimization and other population genetic studies. We
present two linear time Simulation using Best-fit
Algorithms SimBA for two classes of problems where each
co-fits two distributions: SimBA-LD fits linkage
disequilibrium and minimum allele frequency
distributions, while SimBA-hap fits founder-haplotype
and polyploid allele dosage distributions. An
incremental gap-filling version of previously
introduced SimBA-LD is here demonstrated to accurately
fit the target distributions, allowing efficient large
scale simulations. SimBA-hap accuracy and efficiency is
demonstrated by simulating tetraploid populations with
varying numbers of founder haplotypes, we evaluate both
a linear time greedy algoritm and an optimal solution
based on mixed-integer programming. SimBA is available
on http://researcher.watson.ibm.com/project/5669.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Verma:2019:POC,
author = "Deeptak Verma and Gevorg Grigoryan and Chris
Bailey-Kellogg",
title = "{Pareto} Optimization of Combinatorial Mutagenesis
Libraries",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1143--1153",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2858794",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In order to increase the hit rate of discovering
diverse, beneficial protein variants via
high-throughput screening, we have developed a
computational method to optimize combinatorial
mutagenesis libraries for overall enrichment in two
distinct properties of interest. Given scoring
functions for evaluating individual variants, POCoM
Pareto Optimal Combinatorial Mutagenesis scores entire
libraries in terms of averages over their constituent
members, and designs optimal libraries as sets of
mutations whose combinations make the best trade-offs
between average scores. This represents the first
general-purpose method to directly design combinatorial
libraries for multiple objectives characterizing their
constituent members. Despite being rigorous in mapping
out the Pareto frontier, it is also very fast even for
very large libraries e.g., designing 30 mutation,
billion-member libraries in only hours. We here
instantiate POCoM with scores based on a target's
protein structure and its homologs' sequences, enabling
the design of libraries containing variants balancing
these two important yet quite different types of
information. We demonstrate POCoM's generality and
power in case study applications to green fluorescent
protein, cytochrome P450, and $ \beta $-lactamase.
Analysis of the POCoM library designs provides insights
into the trade-offs between structure- and
sequence-based scores, as well as the impacts of
experimental constraints on library designs. POCoM
libraries incorporate mutations that have previously
been found favorable experimentally, while diversifying
the contexts in which these mutations are situated and
maintaining overall variant quality.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rasheed:2019:SFU,
author = "Muhibur Rasheed and Nathan Clement and Abhishek
Bhowmick and Chandrajit L. Bajaj",
title = "Statistical Framework for Uncertainty Quantification
in Computational Molecular Modeling",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1154--1167",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2771240",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "As computational modeling, simulation, and predictions
are becoming integral parts of biomedical pipelines, it
behooves us to emphasize the reliability of the
computational protocol. For any reported quantity of
interest QOI, one must also compute and report a
measure of the uncertainty or error associated with the
QOI. This is especially important in molecular
modeling, since in most practical applications the
inputs to the computational protocol are often noisy,
incomplete, or low-resolution. Unfortunately, currently
available modeling tools do not account for
uncertainties and their effect on the final QOIs with
sufficient rigor. We have developed a statistical
framework that expresses the uncertainty of the QOI as
the probability that the reported value deviates from
the true value by more than some user-defined
threshold. First, we provide a theoretical approach
where this probability can be bounded using
Azuma-Hoeffding like inequalities. Second, we
approximate this probability empirically by sampling
the space of uncertainties of the input and provide
applications of our framework to bound uncertainties of
several QOIs commonly used in molecular modeling.
Finally, we also present several visualization
techniques to effectively and quantitavely visualize
the uncertainties: in the input, final QOIs, and also
intermediate states.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bai:2019:SGM,
author = "Wenruo Bai and Jeffrey Bilmes and William S. Noble",
title = "Submodular Generalized Matching for Peptide
Identification in Tandem Mass Spectrometry",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1168--1181",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2822280",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Motivation: Identification of spectra produced by a
shotgun proteomics mass spectrometry experiment is
commonly performed by searching the observed spectra
against a peptide database. The heart of this search
procedure is a score function that evaluates the
quality of a hypothesized match between an observed
spectrum and a theoretical spectrum corresponding to a
particular peptide sequence. Accordingly, the success
of a spectrum analysis pipeline depends critically upon
this peptide-spectrum score function. We develop
peptide-spectrum score functions that compute the
maximum value of a submodular function under $m$
matroid constraints. We call this procedure a
submodular generalized matching SGM since it
generalizes bipartite matching. We use a greedy
algorithm to compute maximization, which can achieve a
solution whose objective is guaranteed to be at least $
\frac {1}{1 + m}$ of the true optimum. The advantage of
the SGM framework is that known long-range properties
of experimental spectra can be modeled by designing
suitable submodular functions and matroid constraints.
Experiments on four data sets from various organisms
and mass spectrometry platforms show that the SGM
approach leads to significantly improved performance
compared to several state-of-the-art methods.
Supplementary information, C++ source code, and data
sets can be found at
https://melodi-lab.github.io/SGM.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zou:2019:AML,
author = "Quan Zou and Qi Liu",
title = "Advanced Machine Learning Techniques for
Bioinformatics",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1182--1183",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2919039",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The papers in this special section focus on the
machine learning methods, and applications of these
methods to computational biology.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2019:HOC,
author = "Qinhu Zhang and Lin Zhu and De-Shuang Huang",
title = "High-Order Convolutional Neural Network Architecture
for Predicting {DNA}-Protein Binding Sites",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1184--1192",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2819660",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Although Deep learning algorithms have outperformed
conventional methods in predicting the sequence
specificities of DNA-protein binding, they lack to
consider the dependencies among nucleotides and the
diverse binding lengths for different transcription
factors TFs. To address the above two limitations
simultaneously, in this paper, we propose a high-order
convolutional neural network architecture HOCNN, which
employs a high-order encoding method to build
high-order dependencies among nucleotides, and a
multi-scale convolutional layer to capture the motif
features of different length. The experimental results
on real ChIP-seq datasets show that the proposed method
outperforms the state-of-the-art deep learning method
DeepBind in the motif discovery task. In addition, we
provide further insights about the importance of
introducing additional convolutional kernels and the
degeneration problem of importing high-order in the
motif discovery task.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2019:AIC,
author = "Min Li and Zhihui Fei and Min Zeng and Fang-Xiang Wu
and Yaohang Li and Yi Pan and Jianxin Wang",
title = "Automated {ICD-9} Coding via A Deep Learning
Approach",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1193--1202",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2817488",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "ICD-9 the Ninth Revision of International
Classification of Diseases is widely used to describe a
patient's diagnosis. Accurate automated ICD-9 coding is
important because manual coding is expensive,
time-consuming, and inefficient. Inspired by the recent
successes of deep learning, in this study, we present a
deep learning framework called DeepLabeler to
automatically assign ICD-9 codes. DeepLabeler combines
the convolutional neural network with the 'Document to
Vector' technique to extract and encode local and
global features. Our proposed DeepLabeler demonstrates
its effectiveness by achieving state-of-the-art
performance, i.e., 0.335 micro F-measure on MIMIC-II
dataset and 0.408 micro F-measure on MIMIC-III dataset.
It outperforms classical hierarchy-based SVM and
flat-SVM both on these two datasets by at least 14
percent. Furthermore, we analyze the deep neural
network structure to discover the vital elements in the
success of DeepLabeler. We find that the convolutional
neural network is the most effective component in our
network and the 'Document to Vector' technique is also
necessary for enhancing classification performance
since it extracts well-recognized global features.
Extensive experimental results demonstrate that the
great promise of deep learning techniques in the field
of text multi-label classification and automated
medical coding.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2019:PCP,
author = "Bin Liu and Shumin Li",
title = "{ProtDet-CCH}: Protein Remote Homology Detection by
Combining Long Short-Term Memory and Ranking Methods",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1203--1210",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2789880",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "As one of the most challenging tasks in sequence
analysis, protein remote homology detection has been
extensively studied. Methods based on discriminative
models and ranking approaches have achieved the
state-of-the-art performance, and these two kinds of
methods are complementary. In this study, three LSTM
models have been applied to construct the predictors
for protein remote homology detection, including ULSTM,
BLSTM, and CNN-BLSTM. They are able to automatically
extract the local and global sequence order
information. Combined with PSSMs, the CNN-BLSTM
achieved the best performance among the three
LSTM-based models. We named this method as
CNN-BLSTM-PSSM. Finally, a new method called
ProtDet-CCH was proposed by combining CNN-BLSTM-PSSM
and a ranking method HHblits. Tested on a widely used
SCOP benchmark dataset, ProtDet-CCH achieved an ROC
score of 0.998, and an ROC50 score of 0.982,
significantly outperforming other existing
state-of-the-art methods. Experimental results on two
updated SCOPe independent datasets showed that
ProtDet-CCH can achieve stable performance.
Furthermore, our method can provide useful insights for
studying the features and motifs of protein families
and superfamilies. It is anticipated that ProtDet-CCH
will become a very useful tool for protein remote
homology detection.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2019:CPS,
author = "Bingqiang Liu and Ling Han and Xiangrong Liu and
Jichang Wu and Qin Ma",
title = "Computational Prediction of Sigma-54 Promoters in
Bacterial Genomes by Integrating Motif Finding and
Machine Learning Strategies",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1211--1218",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2816032",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Sigma factor, as a unit of RNA polymerase holoenzyme,
is a critical factor in the process of gene
transcriptional regulation. It recognizes the specific
DNA sites and brings the core enzyme of RNA polymerase
to the upstream regions of target genes. Therefore, the
prediction of the promoters for a particular sigma
factor is essential for interpreting functional genomic
data and observation. This paper develops a new method
to predict sigma-54 promoters in bacterial genomes. The
new method organically integrates motif finding and
machine learning strategies to capture the intrinsic
features of sigma-54 promoters. The experiments on E.
coli benchmark test set show that our method has good
capability to distinguish sigma-54 promoters from
surrounding or randomly selected DNA sequences. The
applications of the other three bacterial genomes
indicate the potential robustness and applicative power
of our method on a large number of bacterial genomes.
The source code of our method can be freely downloaded
at https://github.com/maqin2001/PromotePredictor.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Liu:2019:TBP,
author = "Hongyu Liu and Qinmin Vivian Hu and Liang He",
title = "Term-Based Personalization for Feature Selection in
Clinical Handover Form Auto-Filling",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1219--1230",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2874237",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Feature learning and selection have been widely
applied in many research areas because of their good
performance and lower complexity. Traditional methods
usually treat all terms with same feature sets, such
that performance can be damaged when noisy information
is brought via wrong features for a given term. In this
paper, we propose a term-based personalization approach
to finding the best features for each term. First,
features are given as the input so that we focus on
selection strategies. Second, the importance of each
feature subset to a given term is evaluated by the
term-feature probabilistic relevance model. We present
a feature searching method to generate feature
candidate subsets for each term, since evaluating all
the possible feature subsets is computationally
intensive. Finally, we obtain the personalized feature
set for each term as a subset of all features.
Experiments have been conducted on the NICTA Synthetic
Nursing Handover dataset and the results show that our
approach is promising and effective.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Su:2019:DMD,
author = "Ran Su and Huichen Wu and Bo Xu and Xiaofeng Liu and
Leyi Wei",
title = "Developing a Multi-Dose Computational Model for
Drug-Induced Hepatotoxicity Prediction Based on
Toxicogenomics Data",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1231--1239",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2858756",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Drug-induced hepatotoxicity may cause acute and
chronic liver disease, leading to great concern for
patient safety. It is also one of the main reasons for
drug withdrawal from the market. Toxicogenomics data
has been widely used in hepatotoxicity prediction. In
our study, we proposed a multi-dose computational model
to predict the drug-induced hepatotoxicity based on
gene expression and toxicity data. The
dose/concentration information after drug treatment is
fully utilized in our study based on the dose-response
curve, thus a more informative representative of the
dose-response relationship is considered. We also
proposed a new feature selection method, named MEMO,
which is also one important aspect of our multi-dose
model in our study, to deal with the high-dimensional
toxicogenomics data. We validated the proposed model
using the TG-GATEs, which is a large database recording
toxicogenomics data from multiple views. The
experimental results show that the drug-induced
hepatotoxicity can be predicted with high accuracy and
efficiency using the proposed predictive model.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yu:2019:HPB,
author = "Liang Yu and Lin Gao",
title = "Human Pathway-Based Disease Network",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1240--1249",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2774802",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Constructing disease-disease similarity network is
important in elucidating the associations between the
origin and molecular mechanism of diseases, and in
researching disease function and medical research. In
this paper, we use a high-quality protein interaction
network and a collection of pathway databases to
construct a Human Pathway-based Disease Network HPDN to
explore the relationship between diseases and their
intrinsic interactions. We find that the similarity of
two diseases has a strong correlation with the number
of their shared functional pathways and the interaction
between their related gene sets. Comparing HPDN with
disease networks based on genes and symptoms
respectively, we find the three networks have high
overlap rates. Additionally, HPDN can predict new
disease-disease correlations, which are supported by
Comparative Toxicogenomics Database CTD benchmark and
large-scale biomedical literature database. The
comprehensive, high-quality relations between diseases
based on pathways can further be applied to study
important matters in systems medicine, for instance,
drug repurposing. Based on a dense subgraph in our
network, we find two drugs, prednisone and folic acid,
may have new indications, which will provide potential
directions for the treatments of complex diseases.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Imani:2019:CGR,
author = "Mahdi Imani and Ulisses M. Braga-Neto",
title = "Control of Gene Regulatory Networks Using {Bayesian}
Inverse Reinforcement Learning",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1250--1261",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2830357",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Control of gene regulatory networks GRNs to shift gene
expression from undesirable states to desirable ones
has received much attention in recent years. Most of
the existing methods assume that the cost of
intervention at each state and time point, referred to
as the immediate cost function, is fully known. In this
paper, we employ the Partially-Observed Boolean
Dynamical System POBDS signal model for a time sequence
of noisy expression measurement from a Boolean GRN and
develop a Bayesian Inverse Reinforcement Learning BIRL
approach to address the realistic case in which the
only available knowledge regarding the immediate cost
function is provided by the sequence of measurements
and interventions recorded in an experimental setting
by an expert. The Boolean Kalman Smoother BKS algorithm
is used for optimally mapping the available
gene-expression data into a sequence of Boolean states,
and then the BIRL method is efficiently combined with
the Q-learning algorithm for quantification of the
immediate cost function. The performance of the
proposed methodology is investigated by applying a
state-feedback controller to two GRN models: a melanoma
WNT5A Boolean network and a p53-MDM2 negative feedback
loop Boolean network, when the cost of the undesirable
states, and thus the identity of the undesirable genes,
is learned using the proposed methodology.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Guo:2019:GES,
author = "Feng-Biao Guo and Xinglai Ji and Jian Huang",
title = "Guest Editorial for Special Section on the {7th
National Conference on Bioinformatics and Systems
Biology of China}",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1262--1263",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2918969",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The eight papers in this special section were
presented at the 7th National Conference on
Bioinformatics and Systems Biology of China in 2016.
The conference is the most influential conference of
the Chinese scientific community of bioinformatics and
systems biology.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wei:2019:FPP,
author = "Leyi Wei and Pengwei Xing and Gaotao Shi and Zhiliang
Ji and Quan Zou",
title = "Fast Prediction of Protein Methylation Sites Using a
Sequence-Based Feature Selection Technique",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1264--1273",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2670558",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Protein methylation, an important post-translational
modification, plays crucial roles in many cellular
processes. The accurate prediction of protein
methylation sites is fundamentally important for
revealing the molecular mechanisms undergoing
methylation. In recent years, computational prediction
based on machine learning algorithms has emerged as a
powerful and robust approach for identifying
methylation sites, and much progress has been made in
predictive performance improvement. However, the
predictive performance of existing methods is not
satisfactory in terms of overall accuracy. Motivated by
this, we propose a novel random-forest-based predictor
called MePred-RF, integrating several discriminative
sequence-based feature descriptors and improving
feature representation capability using a powerful
feature selection technique. Importantly, unlike other
methods based on multiple, complex information inputs,
our proposed MePred-RF is based on sequence information
alone. Comparative studies on benchmark datasets via
vigorous jackknife tests indicate that our proposed
MePred-RF method remarkably outperforms other
state-of-the-art predictors, leading by a 4.5 percent
average in terms of overall accuracy. A user-friendly
webserver that implements the proposed method has been
established for researchers' convenience, and is now
freely available for public use through
http://server.malab.cn/MePred-RF. We anticipate our
research tool to be useful for the large-scale
prediction and analysis of protein methylation sites.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lin:2019:IBE,
author = "Yan Lin and Fa-Zhan Zhang and Kai Xue and Yi-Zhou Gao
and Feng-Biao Guo",
title = "Identifying Bacterial Essential Genes Based on a
Feature-Integrated Method",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1274--1279",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2669968",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Essential genes are those genes of an organism that
are considered to be crucial for its survival.
Identification of essential genes is therefore of great
significance to advance our understanding of the
principles of cellular life. We have developed a novel
computational method, which can effectively predict
bacterial essential genes by extracting and integrating
homologous features, protein domain feature, gene
intrinsic features, and network topological features.
By performing the principal component regression PCR
analysis for Escherichia coli MG1655, we established a
classification model with the average area under curve
AUC value of 0.992 in ten times 5-fold cross-validation
tests. Furthermore, when employing this new model to a
distantly related organism-Streptococcus pneumoniae
TIGR4, we still got a reliable AUC value of 0.788.
These results indicate that our feature-integrated
approach could have practical applications in
accurately investigating essential genes from broad
bacterial species, and also provide helpful guidelines
for the minimal cell.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Shi:2019:CED,
author = "Yan Shi and Bolun Zhang and Maolin Cai and Weiqing
Xu",
title = "Coupling Effect of Double Lungs on a {VCV} Ventilator
with Automatic Secretion Clearance Function",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1280--1287",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2670079",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "For patients with mechanical ventilation, secretions
in airway are harmful and sometimes even mortal, it's
of great significance to clear secretion timely and
efficiently. In this paper, a new secretion clearance
method for VCV volume-controlled ventilation ventilator
is put forward, and a secretion clearance system with a
VCV ventilator and double lungs is designed.
Furthermore, the mathematical model of the secretion
clearance system is built and verified via experimental
study. Finally, to illustrate the influence of key
parameters of respiratory system and secretion
clearance system on the secretion clearance
characteristics, coupling effects of two lungs on VCV
secretion clearance system are studied by an orthogonal
experiment, it can be obtained that rise of tidal
volume adds to efficiency of secretion clearance while
effect of area, compliance, and suction pressure on
efficiency of secretion clearance needs further study.
Rise of compliance improves bottom pressure of
secretion clearance while rise of area, tidal volume,
and suction pressure decreases bottom pressure of
secretion clearance. This paper can be referred to in
researches of secretion clearance for VCV.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2019:IFI,
author = "Ying Li and Ye He and Siyu Han and Yanchun Liang",
title = "Identification and Functional Inference for
Tumor-Associated Long Non-Coding {RNA}",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1288--1301",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2687442",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Gastric cancer is one of the top leading causes of
cancer mortality worldwide especially in China. In
recent years, some lncRNAs are discovered to be
dysregulated in many cancers. The study on long
non-coding RNAs lncRNAs relationship with cancers has
attracted increasing attention. The molecular mechanism
of gastric cancer remains largely unclear factors,
especially for lncRNAs. Experiments are feasible to
obtain related information, however, experimental
identification of cancer-related lncRNAs usually
possesses high time complexity and high cost. In this
paper, a computational method is proposed to determine
the relationship between lncRNA and gastric cancer by
reusing the exon-based array of gastric cancer. One
specific lncRNAs LINC00365 and its target
differentially expressed genes whose products are
predicted as blood, urine, or salvia-excretory are
identified to be candidates for a combined biomarker
for gastric cancer. Further biological function and
molecular mechanism of the gastric cancer related
lncRNAs and coding gene biomarkers are inferred in
terms of multi-source biological knowledge.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhang:2019:CMV,
author = "Jun-Ping Zhang and Yi Liu and Wei Sun and Xiao-Yang
Zhao and La Ta and Wei-Sheng Guo",
title = "Characteristics of Myosin {V} Processivity",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1302--1308",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2669311",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Myosin V is a processive doubled-headed biomolecular
motor involved in many intracellular organelle and
vesicle transport. The unidirectional movement is
coupled with the adenosine triphosphate ATP hydrolysis
and product release cycle. With the progress of
experimental techniques and the enhancement of
measuring directness, detailed knowledge of the
motility of myosin V has been obtained. Following the
ATPase cycle, the 4-state mechanochemical model of the
myosin V's processive movement is used. The transitions
between various states take place in a stochastic
manner. We can use the master equation to analyze and
calculate quantitatively. Meanwhile, the effect of the
reverse reaction is taken fully into account. We fit
the mean velocity, the mean dwell time, the mean run
length, and the ratio of forward/backward steps as a
function of ATP, ADP, and Pi concertration. The
theoretical curves are generally in line with the
experimental data. This work provides a new insight for
the characteristic of myosin V.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Feng:2019:PAP,
author = "Pengmian Feng and Zhenyi Wang and Xiaoyu Yu",
title = "Predicting Antimicrobial Peptides by Using Increment
of Diversity with Quadratic Discriminant Analysis
Method",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1309--1312",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2669302",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Antimicrobial peptides are crucial components of the
innate host defense system of most living organisms and
promising candidates for antimicrobial agents. Accurate
classification of antimicrobial peptides will be
helpful to the discovery of new therapeutic targets. In
this work, the Increment of Diversity with Quadratic
Discriminant analysis IDQD was presented to classify
antifungal and antibacterial peptides based on primary
sequence information. In the jackknife test, the
proposed IDQD model yields an accuracy of 86.02 percent
with the sensitivity of 74.31 percent and specificity
of 92.79 percent for identifying antimicrobial
peptides, which is superior to other state-of-the-art
methods. This result suggests that the proposed IDQD
model can be efficiently used to antimicrobial peptide
classification.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Chai:2019:HWT,
author = "Guoshi Chai and Min Yu and Lixu Jiang and Yaocong Duan
and Jian Huang",
title = "{HMMCAS}: a {Web} Tool for the Identification and
Domain Annotations of {CAS} Proteins",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1313--1315",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2665542",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The CRISPR-Cas clustered regularly interspaced short
palindromic repeats-CRISPR-associated proteins adaptive
immune systems are discovered in many bacteria and most
archaea. These systems are encoded by cas
CRISPR-associated operons that have an extremely
diverse architecture. The most crucial step in the
depiction of cas operons composition is the
identification of cas genes or Cas proteins. With the
continuous increase of the newly sequenced archaeal and
bacterial genomes, the recognition of new Cas proteins
is becoming possible, which not only provides
candidates for novel genome editing tools but also
helps to understand the prokaryotic immune system
better. Here, we describe HMMCAS, a web service for the
detection of CRISPR-associated structural and
functional domains in protein sequences. HMMCAS uses
hmmscan similarity search algorithm in HMMER3.1 to
provide a fast, interactive service based on a
comprehensive collection of hidden Markov models of Cas
protein family. It can accurately identify the Cas
proteins including those fusion proteins, for example
the Cas1-Cas4 fusion protein in Candidatus
Chloracidobacterium thermophilum B Cab. thermophilum B.
HMMCAS can also find putative cas operon and determine
which type it belongs to. HMMCAS is freely available at
http://i.uestc.edu.cn/hmmcas.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Lin:2019:ISP,
author = "Hao Lin and Zhi-Yong Liang and Hua Tang and Wei Chen",
title = "Identifying Sigma70 Promoters with Novel Pseudo
Nucleotide Composition",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1316--1321",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2666141",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Promoters are DNA regulatory elements located directly
upstream or at the 5' end of the transcription
initiation site TSS, which are in charge of gene
transcription initiation. With the completion of a
large number of microorganism genomics, it is urgent to
predict promoters accurately in bacteria by using the
computational method. In this work, a sequence-based
predictor named ``iPro70-PseZNC'' was designed for
identifying sigma70 promoters in prokaryote. In the
predictor, the samples of DNA sequences are formulated
by a novel pseudo nucleotide composition, called
PseZNC, into which the multi-window Z-curve composition
and six local DNA structural properties are
incorporated. In the 5-fold cross-validation, the area
under the curve of receiver operating characteristic of
0.909 was obtained on our benchmark dataset, indicating
that the proposed predictor is promising and will
provide an important guide in this area. Further
studies showed that the performance of PseZNC is better
than it of multi-window Z-curve composition. For the
sake of convenience for researchers, a user-friendly
online service was established and can be freely
accessible at
http://lin.uestc.edu.cn/server/iPro70-PseZNC. The
PseZNC approach can be also extended to other
DNA-related problems.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Ayday:2019:GIW,
author = "Erman Ayday and Muhammad Naveed and Haixu Tang",
title = "{GenoPri'17: International Workshop on Genome Privacy
and Security}",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1322--1323",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2891029",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The four papers in this special section were presented
at the 4th International Workshop on Genome Privacy and
Security GenoPri in 2017. This workshop aimed to bring
together a highly interdisciplinary community involved
in all aspects of genome privacy and security research.
This workshop built on its three predecessors,
GenoPri'14, GenoPri'15, and GenoPri'16 which were
collocated with the Privacy Enhancing Technologies
Symposium PETS, IEEE Symposium on Security and Privacy,
and American Medical Informatics Association Annual
Fall Symposium AMIA, respectively. Over the past
several decades, genome sequencing technologies have
evolved from slow and expensive systems that were
limited in access to a select few scientists and
forensics investigators to high-throughput, relatively
low-cost tools that are available to consumers.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Senf:2019:EES,
author = "Alexander Senf",
title = "End-to-End Security for Local and Remote Human Genetic
Data Applications at the {EGA}",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1324--1327",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2916810",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Sensitive genomic data should remain secure ---
whether on disk for storage, or analysis, or in
transport. However, secure storage, delivery, and usage
of genomic data is complicated by the size of files and
diversity of workflows. This paper presents solutions
developed by GA4GH and EGA to use custom-ized
encryption, encrypted file formats, toolchain
integration, and intelligent APIs to help solve this
problem.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Raisaro:2019:MES,
author = "Jean Louis Raisaro and Juan Ramon Troncoso-Pastoriza
and Mickael Misbach and Joao Sa Sousa and Sylvain
Pradervand and Edoardo Missiaglia and Olivier Michielin
and Bryan Ford and Jean-Pierre Hubaux",
title = "{MedCo}: Enabling Secure and Privacy-Preserving
Exploration of Distributed Clinical and Genomic Data",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1328--1341",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2854776",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The increasing number of health-data breaches is
creating a complicated environment for medical-data
sharing and, consequently, for medical progress.
Therefore, the development of new solutions that can
reassure clinical sites by enabling privacy-preserving
sharing of sensitive medical data in compliance with
stringent regulations e.g., HIPAA, GDPR is now more
urgent than ever. In this work, we introduce MedCo, the
first operational system that enables a group of
clinical sites to federate and collectively protect
their data in order to share them with external
investigators without worrying about security and
privacy concerns. MedCo uses a collective homomorphic
encryption to provide trust decentralization and
end-to-end confidentiality protection, and b
obfuscation techniques to achieve formal notions of
privacy, such as differential privacy. A critical
feature of MedCo is that it is fully integrated within
the i2b2 Informatics for Integrating Biology and the
Bedside framework, currently used in more than 300
hospitals worldwide. Therefore, it is easily adoptable
by clinical sites. We demonstrate MedCo's practicality
by testing it on data from The Cancer Genome Atlas in a
simulated network of three institutions. Its
performance is comparable to the ones of SHRINE
networked i2b2, which, in contrast, does not provide
any data protection guarantee.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Baker:2019:PPL,
author = "Dixie B. Baker and Bartha M. Knoppers and Mark
Phillips and David van Enckevort and Petra Kaufmann and
Hanns Lochmuller and Domenica Taruscio",
title = "Privacy-Preserving Linkage of Genomic and Clinical
Data Sets",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1342--1348",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2855125",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The capacity to link records associated with the same
individual across data sets is a key challenge for
data-driven research. The challenge is exacerbated by
the potential inclusion of both genomic and clinical
data in data sets that may span multiple legal
jurisdictions, and by the need to enable
re-identification in limited circumstances.
Privacy-Preserving Record Linkage PPRL methods address
these challenges. In 2016, the Interdisciplinary
Committee of the International Rare Diseases Research
Consortium IRDiRC launched a task team to explore
approaches to PPRL. The task team is a collaboration
with the Global Alliance for Genomics and Health GA4GH
Regulatory and Ethics and Data Security Work Streams,
and aims to prepare policy and technology standards to
enable highly reliable linking of records associated
with the same individual without disclosing their
identity except under conditions in which the use of
the data has led to information of importance to the
individual's safety or health, and applicable law
allows or requires the return of results. The PPRL Task
Force has examined the ethico-legal requirements,
constraints, and implications of PPRL, and has applied
this knowledge to the exploration of technology methods
and approaches to PPRL. This paper reports and
justifies the findings and recommendations thus far.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Weidman:2019:SIP,
author = "Jake Weidman and William Aurite and Jens Grossklags",
title = "On Sharing Intentions, and Personal and Interdependent
Privacy Considerations for Genetic Data: a Vignette
Study",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1349--1361",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2854785",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genetics and genetic data have been the subject of
recent scholarly work, with significant attention paid
towards understanding consent practices for the
acquisition and usage of genetic data as well as
genetic data security. Attitudes and perceptions
concerning the trustworthiness of governmental
institutions receiving test-taker data have been
explored, with varied findings, but no robust models or
deterministic relationships have been established that
account for these differences. These results also do
not explore in detail the perceptions regarding other
types of organizations e.g., private corporations.
Further, considerations of privacy interdependence
arising from blood relative relationships have been
absent from the conversation regarding the sharing of
genetic data. This paper reports the results from a
factorial vignette survey study in which we investigate
how variables of ethnicity, age, genetic markers, and
association of data with the individual's name affect
the likelihood of sharing data with different types of
organizations. We also investigate elements of personal
and interdependent privacy concerns. We document the
significant role these factors have in the decision to
share or not share genetic data. We support our
findings with a series of regression analyses.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mandoiu:2019:GEI,
author = "Ion I. Mandoiu and Pavel Skums and Alexander
Zelikovsky",
title = "{Guest Editors}' Introduction to the Special Section
on Bioinformatics Research and Applications",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1362--1363",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2894215",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The papers in this special section were presented at
the 12th International Symposium on Bioinformatics
Research and Application ISBRA, which was held at
Belarusian State University in Minsk, Belarus on June
5-8, 2016.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Luhmann:2019:SSP,
author = "Nina Luhmann and Manuel Lafond and Annelyse Thevenin
and Aida Ouangraoua and Roland Wittler and Cedric
Chauve",
title = "The {SCJ} Small Parsimony Problem for Weighted Gene
Adjacencies",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1364--1373",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2661761",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Reconstructing ancestral gene orders in a given
phylogeny is a classical problem in comparative
genomics. Most existing methods compare conserved
features in extant genomes in the phylogeny to define
potential ancestral gene adjacencies, and either try to
reconstruct all ancestral genomes under a global
evolutionary parsimony criterion, or, focusing on a
single ancestral genome, use a scaffolding approach to
select a subset of ancestral gene adjacencies,
generally aiming at reducing the fragmentation of the
reconstructed ancestral genome. In this paper, we
describe an exact algorithm for the Small Parsimony
Problem that combines both approaches. We consider that
gene adjacencies at internal nodes of the species
phylogeny are weighted, and we introduce an objective
function defined as a convex combination of these
weights and the evolutionary cost under the
Single-Cut-or-Join SCJ model. The weights of ancestral
gene adjacencies can, e.g., be obtained through the
recent availability of ancient DNA sequencing data,
which provide a direct hint at the genome structure of
the considered ancestor, or through probabilistic
analysis of gene adjacencies evolution. We show the
NP-hardness of our problem variant and propose a
Fixed-Parameter Tractable algorithm based on the
Sankoff-Rousseau dynamic programming algorithm that
also allows to sample co-optimal solutions. We apply
our approach to mammalian and bacterial data providing
different degrees of complexity. We show that including
adjacency weights in the objective has a significant
impact in reducing the fragmentation of the
reconstructed ancestral gene orders. An implementation
is available at http://github.com/nluhmann/PhySca.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Markin:2019:ELS,
author = "Alexey Markin and Oliver Eulenstein",
title = "Efficient Local Search for {Euclidean} Path-Difference
Median Trees",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1374--1385",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2763137",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Synthesizing large-scale phylogenetic trees is a
fundamental problem in evolutionary biology. Median
tree problems have evolved as a powerful tool to
reconstruct such trees. Such problems seek a median
tree for a given collection of input trees under some
problem-specific tree distance. There has been an
increased interest in the median tree problem for the
classical path-difference distance between trees. While
this problem is NP-hard, standard local search
heuristics have been described that are based on
solving a local search problem exactly. For a more
effective heuristic we devise a time efficient
algorithm for the local search problem that improves on
the best-known solution by a factor of $n$, where $n$
is the size of the input trees. Furthermore, we
introduce a novel hybrid version of the standard local
search that is exploiting our new algorithm for a more
refined heuristic search. Finally, we demonstrate the
performance of our hybrid heuristic in a comparative
study with other commonly used methods that synthesize
species trees using published empirical data sets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2019:CRP,
author = "Min Li and Peng Ni and Xiaopei Chen and Jianxin Wang
and Fang-Xiang Wu and Yi Pan",
title = "Construction of Refined Protein Interaction Network
for Predicting Essential Proteins",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1386--1397",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2665482",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Identification of essential proteins based on protein
interaction network PIN is a very important and hot
topic in the post genome era. Up to now, a number of
network-based essential protein discovery methods have
been proposed. Generally, a static protein interaction
network was constructed by using the protein-protein
interactions obtained from different experiments or
databases. Unfortunately, most of the network-based
essential protein discovery methods are sensitive to
the reliability of the constructed PIN. In this paper,
we propose a new method for constructing refined PIN by
using gene expression profiles and subcellular location
information. The basic idea behind refining the PIN is
that two proteins should have higher possibility to
physically interact with each other if they appear
together at the same subcellular location and are
active together at least at a time point in the cell
cycle. The original static PIN is denoted by S-PIN
while the final PIN refined by our method is denoted by
TS-PIN. To evaluate whether the constructed TS-PIN is
more suitable to be used in the identification of
essential proteins, 10 network-based essential protein
discovery methods DC, EC, SC, BC, CC, IC, LAC, NC, BN,
and DMNC are applied on it to identify essential
proteins. A comparison of TS-PIN and two other
networks: S-PIN and NF-APIN a noise-filtered active PIN
constructed by using gene expression data and S-PIN is
implemented on the prediction of essential proteins by
using these ten network-based methods. The comparison
results show that all of the 10 network-based methods
achieve better results when being applied on TS-PIN
than that being applied on S-PIN and NF-APIN.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sergeev:2019:GWA,
author = "Roman Sergeevich Sergeev and Ivan S. Kavaliou and
Uladzislau V. Sataneuski and Andrei Gabrielian and Alex
Rosenthal and Michael Tartakovsky and Alexander V.
Tuzikov",
title = "Genome-Wide Analysis of {MDR} and {XDR} Tuberculosis
from {Belarus}: Machine-Learning Approach",
journal = j-TCBB,
volume = "16",
number = "4",
pages = "1398--1408",
month = jul,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2720669",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:02 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Emergence of drug-resistant microorganisms has been
recognized as a serious threat to public health
worldwide. This problem is extensively discussed in the
context of tuberculosis treatment. Alterations in
pathogen genomes are among the main mechanisms by which
microorganisms exhibit drug resistance. Analysis of 144
M. tuberculosis strains of different phenotypes
including drug susceptible, MDR, and XDR isolated in
Belarus was fulfilled in this paper. A wide range of
machine learning methods that can discover SNPs related
to drug-resistance in the whole bacteria genomes was
investigated. Besides single-SNP testing approaches,
methods that allow detecting joint effects from
interacting SNPs were considered. We proposed a
framework for automated selection of the best
performing statistical model in terms of recall,
precision, and accuracy to identify drug
resistance-associated mutations. Analysis of
whole-genome sequences often leads to situations where
the number of treated features exceeds the number of
available observations. For this reason, special
attention is paid to fair evaluation of the model
prediction quality and minimizing the risk of
overfitting while estimating the underlying parameters.
Results of our experiments aimed at identifying
top-scoring resistance mutations to the major
first-line and second-line anti-TB drugs are
presented.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Shehu:2019:GEA,
author = "Amarda Shehu and Giuseppe Pozzi and Tamer Kahveci",
title = "Guest Editorial for the {ACM International Conference
on Bioinformatics, Computational Biology, and Health
Informatics}",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1409--1409",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2921083",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The six papers in this special section were presented
at the ACM Conference on Bioinformatics, Computational
Biology, and Health Informatics ACM BCB in 2017.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Bonizzoni:2019:DRI,
author = "Paola Bonizzoni and Simone Ciccolella and Gianluca
Della Vedova and Mauricio Soto",
title = "Does Relaxing the Infinite Sites Assumption Give
Better Tumor Phylogenies? {An} {ILP}-Based Comparative
Approach",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1410--1423",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2865729",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Most of the evolutionary history reconstruction
approaches are based on the infinite sites assumption,
which states that mutations appear once in the
evolutionary history. The Perfect Phylogeny model is
the result of the infinite sites assumption and has
been widely used to infer cancer evolution.
Nonetheless, recent results show that recurrent and
back mutations are present in the evolutionary history
of tumors, hence the Perfect Phylogeny model might be
too restrictive. We propose an approach that allows
losing previously acquired mutations and multiple
acquisitions of a character. Moreover, we provide an
ILP formulation for the evolutionary tree
reconstruction problem. Our formulation allows us to
tackle both the Incomplete Directed Phylogeny problem
and the Clonal Reconstruction problem when general
evolutionary models are considered. The latter problem
is fundamental in cancer genomics, the goal is to study
the evolutionary history of a tumor considering as
input data the fraction of cells having a certain
mutation in a set of cancer samples. For the Clonal
Reconstruction problem, an experimental analysis shows
the advantage of allowing mutation losses. Namely, by
analyzing real and simulated datasets, our ILP approach
provides a better interpretation of the evolutionary
history than a Perfect Phylogeny. The software is at
https://github.com/AlgoLab/gppf.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Cleary:2019:EFR,
author = "Alan Cleary and Thiruvarangan Ramaraj and Indika
Kahanda and Joann Mudge and Brendan Mumey",
title = "Exploring Frequented Regions in Pan-Genomic Graphs",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1424--1435",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2864564",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We consider the problem of identifying regions within
a pan-genome De Bruijn graph that are traversed by many
sequence paths. We define such regions and the subpaths
that traverse them as frequented regions FRs. In this
work, we formalize the FR problem and describe an
efficient algorithm for finding FRs. Subsequently, we
propose some applications of FRs based on
machine-learning and pan-genome graph simplification.
We demonstrate the effectiveness of these applications
using data sets for the organisms Staphylococcus aureus
bacterium and Saccharomyces cerevisiae yeast. We
corroborate the biological relevance of FRs such as
identifying introgressions in yeast that aid in alcohol
tolerance, and show that FRs are useful for
classification of yeast strains by industrial use and
visualizing pan-genomic space.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kwon:2019:EER,
author = "Sunyoung Kwon and Sungroh Yoon",
title = "End-to-End Representation Learning for
Chemical-Chemical Interaction Prediction",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1436--1447",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2864149",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Chemical-chemical interaction CCI plays a major role
in predicting candidate drugs, toxicities, therapeutic
effects, and biological functions. CCI is typically
inferred from a variety of information; however, CCI
has yet not been predicted using a learning-based
approach. In other drug analyses, deep learning has
been actively used in recent years. However, in most
cases, deep learning has been used only for
classification even though it has feature extraction
capabilities. Thus, in this paper, we propose an
end-to-end representation learning method for CCI,
named DeepCCI, which includes feature extraction and a
learning-based approach. Our proposed architecture is
based on the Siamese network. Hidden representations
are extracted from a simplified molecular input line
entry system SMILES, which is a string notation
representing the chemical structure using weight-shared
convolutional neural networks. Subsequently, L1
element-wise distances between the two extracted hidden
representations are measured. The performance of
DeepCCI is compared with those of 12 fingerprint-method
combinations. The proposed DeepCCI shows the best
performance in most of the evaluation metrics used. In
addition, DeepCCI was experimentally validated to
guarantee the commutative property. The automatically
extracted features can alleviate the efforts required
for manual feature engineering and improve prediction
performance.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Malik:2019:RCS,
author = "Laraib Malik and Rob Patro",
title = "Rich Chromatin Structure Prediction from {Hi-C} Data",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1448--1458",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2851200",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recent studies involving the 3-dimensional
conformation of chromatin have revealed the important
role it has to play in different processes within the
cell. These studies have also led to the discovery of
densely interacting segments of the chromosome, called
topologically associating domains. The accurate
identification of these domains from Hi-C interaction
data is an interesting and important computational
problem for which numerous methods have been proposed.
Unfortunately, most existing algorithms designed to
identify these domains assume that they are
non-overlapping whereas there is substantial evidence
to believe a nested structure exists. We present a
methodology to predict hierarchical chromatin domains
using chromatin conformation capture data. Our method
predicts domains at different resolutions, calculated
using intrinsic properties of the chromatin data, and
effectively clusters these to construct the hierarchy.
At each individual level, the domains are
non-overlapping in such a way that the intra-domain
interaction frequencies are maximized. We show that our
predicted structure is highly enriched for actively
transcribing housekeeping genes and various chromatin
markers, including CTCF, around the domain boundaries.
We also show that large-scale domains, at multiple
resolutions within our hierarchy, are conserved across
cell types and species. We also provide comparisons
against existing tools for extracting hierarchical
domains. Our software, Matryoshka, is written in C++11
and licensed under GPL v3; it is available at
https://github.com/COMBINE-lab/matryoshka.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Markin:2019:CMT,
author = "Alexey Markin and Oliver Eulenstein",
title = "Cophenetic Median Trees",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1459--1470",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2870173",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Median tree inference under path-difference metrics
has shown great promise for large-scale phylogeny
estimation. Similar to these metrics is the family of
cophenetic metrics that originates from a classic
dendrogram comparison method introduced more than 50
years ago. Despite the appeal of this family of
metrics, the problem of computing median trees under
cophenetic metrics has not been analyzed. Like other
standard median tree problems relevant in practice, as
we show here, this problem is also NP-hard. NP-hard
median tree problems have been successfully addressed
by local search heuristics that are solving thousands
of instances of a corresponding local neighborhood
search problem. For the local neighborhood search
problem under a cophenetic metric, the best known
na{\"\i}ve algorithm has a time complexity that is
typically prohibitive for effective heuristic searches.
Building on the pioneering work on path-difference
median trees, we develop efficient algorithms for
Manhattan and Euclidean cophenetic search problems that
improve on the na{\"\i}ve solution by a linear and a
quadratic factor, respectively. We demonstrate the
performance and effectiveness of the resulting
heuristic methods in a comparative study using
benchmark empirical datasets.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhu:2019:TSS,
author = "Lu Zhu and Ralf Hofestadt and Martin Ester",
title = "Tissue-Specific Subcellular Localization Prediction
Using Multi-Label {Markov} Random Fields",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1471--1482",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2897683",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The understanding of subcellular localization SCL of
proteins and proteome variation in the different
tissues and organs of the human body are two crucial
aspects for increasing our knowledge of the dynamic
rules of proteins, the cell biology, and the mechanism
of diseases. Although there have been tremendous
contributions to these two fields independently, the
lack of knowledge of the variation of spatial
distribution of proteins in the different tissues still
exists. Here, we proposed an approach that allows
predicting protein SCL on tissue specificity through
the use of tissue-specific functional associations and
physical protein-protein interactions PPIs. We applied
our previously developed Bayesian collective Markov
random fields BCMRFs on tissue-specific protein-protein
interaction network PPI network for nine types of
tissues focusing on eight high-level SCL. The evaluated
results demonstrate the strength of our approach in
predicting tissue-specific SCL. We identified 1,314
proteins that their SCL were previously proven cell
line dependent. We predicted 549 novel tissue-specific
localized candidate proteins while some of them were
validated via text-mining.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Le:2019:FPA,
author = "Thuc Duy Le and Tao Hoang and Jiuyong Li and Lin Liu
and Huawen Liu and Shu Hu",
title = "A Fast {PC} Algorithm for High Dimensional Causal
Discovery with Multi-Core {PCs}",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1483--1495",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2591526",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Discovering causal relationships from observational
data is a crucial problem and it has applications in
many research areas. The PC algorithm is the
state-of-the-art constraint based method for causal
discovery. However, runtime of the PC algorithm, in the
worst-case, is exponential to the number of nodes
variables, and thus it is inefficient when being
applied to high dimensional data, e.g., gene expression
datasets. On another note, the advancement of computer
hardware in the last decade has resulted in the
widespread availability of multi-core personal
computers. There is a significant motivation for
designing a parallelized PC algorithm that is suitable
for personal computers and does not require end users'
parallel computing knowledge beyond their competency in
using the PC algorithm. In this paper, we develop
parallel-PC, a fast and memory efficient PC algorithm
using the parallel computing technique. We apply our
method to a range of synthetic and real-world high
dimensional datasets. Experimental results on a dataset
from the DREAM 5 challenge show that the original PC
algorithm could not produce any results after running
more than 24 hours; meanwhile, our parallel-PC
algorithm managed to finish within around 12 hours with
a 4-core CPU computer, and less than six hours with a
8-core CPU computer. Furthermore, we integrate
parallel-PC into a causal inference method for
inferring miRNA-mRNA regulatory relationships. The
experimental results show that parallel-PC helps
improve both the efficiency and accuracy of the causal
inference algorithm.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Meysman:2019:MES,
author = "Pieter Meysman and Yvan Saeys and Ehsan Sabaghian and
Wout Bittremieux and Yves {Van de Peer} and Bart
Goethals and Kris Laukens",
title = "Mining the Enriched Subgraphs for Specific Vertices in
a Biological Graph",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1496--1507",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2576440",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In this paper, we present a subgroup discovery method
to find subgraphs in a graph that are associated with a
given set of vertices. The association between a
subgraph pattern and a set of vertices is defined by
its significant enrichment based on a
Bonferroni-corrected hypergeometric probability value.
This interestingness measure requires a dedicated
pruning procedure to limit the number of subgraph
matches that must be calculated. The presented mining
algorithm to find associated subgraph patterns in large
graphs is therefore designed to efficiently traverse
the search space. We demonstrate the operation of this
method by applying it on three biological graph data
sets and show that we can find associated subgraphs for
a biologically relevant set of vertices and that the
found subgraphs themselves are biologically
interesting.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jahanshad:2019:MSM,
author = "Neda Jahanshad and Joshua Faskowitz and Gennady
Roshchupkin and Derrek P. Hibar and Boris A. Gutman and
Nicholas J. Tustison and Hieab H. H. Adams and Wiro J.
Niessen and Meike W. Vernooij and M. Arfan Ikram and
Marcel P. Zwiers and Alejandro Arias Vasquez and
Barbara Franke and Jennifer L. Kroll and Benson Mwangi
and Jair C. Soares and Alex Ing and Sylvane Desrivieres
and Gunter Schumann and Narelle K. Hansell and Greig I.
de Zubicaray and Katie L. McMahon and Nicholas G.
Martin and Margaret J. Wright and Paul M. Thompson",
title = "Multi-Site Meta-Analysis of Morphometry",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1508--1514",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2914905",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Genome-wide association studies GWAS link full genome
data to a handful of traits. However, in neuroimaging
studies, there is an almost unlimited number of traits
that can be extracted for full image-wide big data
analyses. Large populations are needed to achieve the
necessary power to detect statistically significant
effects, emphasizing the need to pool data across
multiple studies. Neuroimaging consortia, e.g., ENIGMA
and CHARGE, are now analyzing MRI data from over 30,000
individuals. Distributed processing protocols extract
harmonized features at each site, and pool together
only the cohort statistics using meta analysis to avoid
data sharing. To date, such MRI projects have focused
on single measures such as hippocampal volume, yet
voxelwise analyses e.g., tensor-based morphometry; TBM
may help better localize statistical effects. This can
lead to $ 10^{13} $ tests for GWAS and become
underpowered. We developed an analytical framework for
multi-site TBM by performing multi-channel registration
to cohort-specific templates. Our results highlight the
reliability of the method and the added power over
alternative options while preserving single site
specificity and opening the doors for well-powered
image-wide genome-wide discoveries.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mirzaei:2019:PSP,
author = "Shokoufeh Mirzaei and Tomer Sidi and Chen Keasar and
Silvia Crivelli",
title = "Purely Structural Protein Scoring Functions Using
Support Vector Machine and Ensemble Learning",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1515--1523",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2602269",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The function of a protein is determined by its
structure, which creates a need for efficient methods
of protein structure determination to advance
scientific and medical research. Because current
experimental structure determination methods carry a
high price tag, computational predictions are highly
desirable. Given a protein sequence, computational
methods produce numerous 3D structures known as decoys.
Selection of the best quality decoys is both
challenging and essential as the end users can handle
only a few ones. Therefore, scoring functions are
central to decoy selection. They combine measurable
features into a single number indicator of decoy
quality. Unfortunately, current scoring functions do
not consistently select the best decoys. Machine
learning techniques offer great potential to improve
decoy scoring. This paper presents two machine-learning
based scoring functions to predict the quality of
proteins structures, i.e., the similarity between the
predicted structure and the experimental one without
knowing the latter. We use different metrics to compare
these scoring functions against three state-of-the-art
scores. This is a first attempt at comparing different
scoring functions using the same non-redundant dataset
for training and testing and the same features. The
results show that adding informative features may be
more significant than the method used.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Singh:2019:TSK,
author = "Ritambhara Singh and Jack Lanchantin and Gabriel
Robins and Yanjun Qi",
title = "Transfer String Kernel for Cross-Context
{DNA}--Protein Binding Prediction",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1524--1536",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2016.2609918",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Through sequence-based classification, this paper
tries to accurately predict the DNA binding sites of
transcription factors TFs in an unannotated cellular
context. Related methods in the literature fail to
perform such predictions accurately, since they do not
consider sample distribution shift of sequence segments
from an annotated source context to an unannotated
target context. We, therefore, propose a method called
``Transfer String Kernel'' TSK that achieves improved
prediction of transcription factor binding site TFBS
using knowledge transfer via cross-context sample
adaptation. TSK maps sequence segments to a
high-dimensional feature space using a discriminative
mismatch string kernel framework. In this
high-dimensional space, labeled examples of the source
context are re-weighted so that the revised sample
distribution matches the target context more closely.
We have experimentally verified TSK for TFBS
identifications on 14 different TFs under a
cross-organism setting. We find that TSK consistently
outperforms the state-of-the-art TFBS tools, especially
when working with TFs whose binding sequences are not
conserved across contexts. We also demonstrate the
generalizability of TSK by showing its cutting-edge
performance on a different set of cross-context tasks
for the MHC peptide binding predictions.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Saha:2019:DFM,
author = "Tanay Kumar Saha and Ataur Katebi and Wajdi Dhifli and
Mohammad {Al Hasan}",
title = "Discovery of Functional Motifs from the Interface
Region of Oligomeric Proteins Using Frequent Subgraph
Mining",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1537--1549",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2756879",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Modeling the interface region of a protein complex
paves the way for understanding its dynamics and
functionalities. Existing works model the interface
region of a complex by using different approaches, such
as, the residue composition at the interface region,
the geometry of the interface residues, or the
structural alignment of interface regions. These
approaches are useful for ranking a set of docked
conformation or for building scoring function for
protein-protein docking, but they do not provide a
generic and scalable technique for the extraction of
interface patterns leading to functional motif
discovery. In this work, we model the interface region
of a protein complex by graphs and extract interface
patterns of the given complex in the form of frequent
subgraphs. To achieve this, we develop a scalable
algorithm for frequent subgraph mining. We show that a
systematic review of the mined subgraphs provides an
effective method for the discovery of functional motifs
that exist along the interface region of a given
protein complex. In our experiments, we use three PDB
protein structure datasets. The first two datasets are
composed of PDB structures from different conformations
of two dimeric protein complexes: HIV-1 protease 329
structures, and triosephosphate isomerase TIM 86
structures. The third dataset is a collection of
different enzyme structures protein structures from the
six top-level enzyme classes, namely: Oxydoreductase,
Transferase, Hydrolase, Lyase, Isomerase, and Ligase.
We show that for the first two datasets, our method
captures the locking mechanism at the dimeric interface
by taking into account the spatial positioning of the
interfacial residues through graphs. Indeed, our
frequent subgraph mining based approach discovers the
patterns representing the dimerization lock which is
formed at the base of the structure in 323 of the 329
HIV-1 protease structures. Similarly, for 86 TIM
structures, our approach discovers the dimerization
lock formation in 50 structures. For the enzyme
structures, we show that we are able to capture the
functional motifs active sites that are specific to
each of the six top-level classes of enzymes through
frequent subgraphs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Frasca:2019:MPF,
author = "Marco Frasca and Nicolo Cesa Bianchi",
title = "Multitask Protein Function Prediction through Task
Dissimilarity",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1550--1560",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2684127",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Automated protein function prediction is a challenging
problem with distinctive features, such as the
hierarchical organization of protein functions and the
scarcity of annotated proteins for most biological
functions. We propose a multitask learning algorithm
addressing both issues. Unlike standard multitask
algorithms, which use task protein functions similarity
information as a bias to speed up learning, we show
that dissimilarity information enforces separation of
rare class labels from frequent class labels, and for
this reason is better suited for solving unbalanced
protein function prediction problems. We support our
claim by showing that a multitask extension of the
label propagation algorithm empirically works best when
the task relatedness information is represented using a
dissimilarity matrix as opposed to a similarity matrix.
Moreover, the experimental comparison carried out on
three model organism shows that our method has a more
stable performance in both ``protein-centric'' and
``function-centric'' evaluation settings.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Feret:2019:E,
author = "Jerome Feret and Heinz Koeppl",
title = "Editorial",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1561--1561",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2934374",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Presents the introductory editorial for this issue of
the publication.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Harmer:2019:BCC,
author = "Russ Harmer and Yves-Stan {Le Cornec} and Sebastien
Legare and Eugenia Oshurko",
title = "Bio-Curation for Cellular Signalling: The {KAMI
Project}",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1562--1573",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2906164",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The general question of what constitutes bio-curation
for rule-based modelling of cellular signalling is
posed. A general approach to the problem is presented,
based on rewriting in hierarchies of graphs, together
with a specific instantiation of the methodology that
addresses our particular bio-curation problem. The
current state of the ongoing development of the KAMI
bio-curation tool, based on this approach, is outlined
along with our plans for future development.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Biane:2019:CRB,
author = "Celia Biane and Franck Delaplace",
title = "Causal Reasoning on {Boolean} Control Networks Based
on Abduction: Theory and Application to Cancer Drug
Discovery",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1574--1585",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2889102",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Complex diseases such as Cancer or Alzheimer's are
caused by multiple molecular perturbations leading to
pathological cellular behavior. However, the
identification of disease-induced molecular
perturbations and subsequent development of efficient
therapies are challenged by the complexity of the
genotype-phenotype relationship. Accordingly, a key
issue is to develop frameworks relating molecular
perturbations and drug effects to their consequences on
cellular phenotypes. Such framework would aim at
identifying the sets of causal molecular factors
leading to phenotypic reprogramming. In this article,
we propose a theoretical framework, called Boolean
Control Networks, where disease-induced molecular
perturbations and drug actions are seen as topological
perturbations/actions on molecular networks leading to
cell phenotype reprogramming. We present a new method
using abductive reasoning principles inferring the
minimal causal topological actions leading to an
expected behavior at stable state. Then, we compare
different implementations of the algorithm and finally,
show a proof-of-concept of the approach on a model of
network regulating the proliferation/apoptosis switch
in breast cancer by automatically discovering driver
genes and their synthetic lethal drug target partner.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Abbas:2019:QRE,
author = "Houssam Abbas and Alena Rodionova and Konstantinos
Mamouras and Ezio Bartocci and Scott A. Smolka and Radu
Grosu",
title = "Quantitative Regular Expressions for Arrhythmia
Detection",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1586--1597",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2885274",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Implantable medical devices are safety-critical
systems whose incorrect operation can jeopardize a
patient's health, and whose algorithms must meet tight
platform constraints like memory consumption and
runtime. In particular, we consider here the case of
implantable cardioverter defibrillators, where peak
detection algorithms and various others discrimination
algorithms serve to distinguish fatal from non-fatal
arrhythmias in a cardiac signal. Motivated by the need
for powerful formal methods to reason about the
performance of arrhythmia detection algorithms, we show
how to specify all these algorithms using Quantitative
Regular Expressions QREs. QRE is a formal language to
express complex numerical queries over data streams,
with provable runtime and memory consumption
guarantees. We show that QREs are more suitable than
classical temporal logics to express in a concise and
easy way a range of peak detectors in both the time and
wavelet domains and various discriminators at the heart
of today's arrhythmia detection devices. The proposed
formalization also opens the way to formal analysis and
rigorous testing of these detectors' correctness and
performance, alleviating the regulatory burden on
device developers when modifying their algorithms. We
demonstrate the effectiveness of our approach by
executing QRE-based monitors on real patient data on
which they yield results on par with the results
reported in the medical literature.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Luck:2019:HMM,
author = "Alexander Luck and Pascal Giehr and Karl Nordstrom and
Jorn Walter and Verena Wolf",
title = "Hidden {Markov} Modelling Reveals Neighborhood
Dependence of {Dnmt3a} and 3b Activity",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1598--1609",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2910814",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "DNA methylation is an epigenetic mark whose important
role in development has been widely recognized. This
epigenetic modification results in heritable
information not encoded by the DNA sequence. The
underlying mechanisms controlling DNA methylation are
only partly understood. Several mechanistic models of
enzyme activities responsible for DNA methylation have
been proposed. Here, we extend existing Hidden Markov
Models HMMs for DNA methylation by describing the
occurrence of spatial methylation patterns over time
and propose several models with different neighborhood
dependences. Furthermore, we investigate correlations
between the neighborhood dependence and other genomic
information. We perform numerical analysis of the HMMs
applied to comprehensive hairpin and non-hairpin
bisulfite sequencing measurements and accurately
predict wild-type data. We find evidence that the
activities of Dnmt3a and Dnmt3b responsible for de novo
methylation depend on 5' left but not on 3' right
neighboring CpGs in a sequencing string.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Mandon:2019:ASR,
author = "Hugues Mandon and Cui Su and Jun Pang and Soumya Paul
and Stefan Haar and Loic Pauleve",
title = "Algorithms for the Sequential Reprogramming of
{Boolean} Networks",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1610--1619",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2914383",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Cellular reprogramming, a technique that opens huge
opportunities in modern and regenerative medicine,
heavily relies on identifying key genes to perturb.
Most of the existing computational methods for
controlling which attractor steady state the cell will
reach focus on finding mutations to apply to the
initial state. However, it has been shown, and is
proved in this article, that waiting between
perturbations so that the update dynamics of the system
prepares the ground, allows for new reprogramming
strategies. To identify such sequential perturbations,
we consider a qualitative model of regulatory networks,
and rely on Binary Decision Diagrams to model their
dynamics and the putative perturbations. Our method
establishes a set identification of sequential
perturbations, whether permanent mutations or only
temporary, to achieve the existential or inevitable
reachability of an arbitrary state of the system. We
apply an implementation for temporary perturbations on
models from the literature, illustrating that we are
able to derive sequential perturbations to achieve
trans-differentiation.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kapilevich:2019:CRC,
author = "Viacheslav Kapilevich and Shigeto Seno and Hideo
Matsuda and Yoichi Takenaka",
title = "Chromatin {$3$D} Reconstruction from Chromosomal
Contacts Using a Genetic Algorithm",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1620--1626",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2814995",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Recent epigenetics research has demonstrated that
chromatin conformation plays an important role in
various aspects of gene regulation. Chromosome
Conformation Capture 3C technology makes it possible to
analyze the spatial organization of chromatin in a
cell. Several algorithms for three-dimensional
reconstruction of chromatin structure from 3C
experimental data have been proposed. Compared to other
algorithms, ShRec3D, one of the most advanced
algorithms, can reconstruct a chromatin model in the
shortest time for high-resolution whole-genome
experimental data. However, ShRec3D employs a graph
shortest path algorithm, which introduces errors in the
resulting model. We propose an improved algorithm that
optimizes shortest path distances using a genetic
algorithm approach. The proposed algorithm and ShRec3D
were compared using in silico 3C experimental data.
Compared to ShRec3D, the proposed algorithm
demonstrated significant improvement relative to the
similarity between the algorithm's output and the
original model with a reasonable increase to
calculation time.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Jhee:2019:CSC,
author = "Jong Ho Jhee and Sunjoo Bang and Dong-gi Lee and
Hyunjung Shin",
title = "Comorbidity Scoring with Causal Disease Networks",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1627--1634",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2812886",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "In recent years, there has been numerous studies
constructing a disease network with diverse sources of
data. Many researchers attempted to extend the usage of
the disease network by employing machine learning
algorithms on various problems such as prediction of
comorbidity. The relations between diseases can further
be specified into causal relations. When causality is
laid on the edges in the network, prediction for
comorbid diseases can be more improved. However, not
many machine learning algorithms have been developed to
concern causality. In this study, we exploit a network
based machine learning algorithm that generates
comorbidity scores from a causal disease network. In
order to find comorbid diseases, semi-supervised
scoring for causal networks is proposed. It computes
scores of entire nodes in the network when a specific
node is labeled. Each score is calculated one at a time
and affects to the others along causal edges. The
algorithm iterates until it converges. We compared the
scoring results of the causal disease network and those
of simple association network. As a gold standard, we
referenced the values of relative risk from prevalence
database, HuDiNe. Scoring by the proposed method
provides clearer distinguishability between the
top-ranked diseases in the comorbidity list. This is a
benefit because it allows the choosing of the most
significant ones on an easier fashion. To present
typical use of the resulting list, comorbid diseases of
Huntington disease and pnuemonia are validated via
PubMed literature, respectively.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2019:CIC,
author = "Donghe Li and Wonji Kim and Longfei Wang and Kyong-Ah
Yoon and Boyoung Park and Charny Park and Sun-Young
Kong and Yongdeuk Hwang and Daehyun Baek and Eun Sook
Lee and Sungho Won",
title = "Comparison of {INDEL} Calling Tools with Simulation
Data and Real Short-Read Data",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1635--1644",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2854793",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Insertions and deletions INDELs comprise a significant
proportion of human genetic variation, and recent
papers have revealed that many human diseases may be
attributable to INDELs. With the development of
next-generation sequencing NGS technology, many
statistical/computational tools have been developed for
calling INDELs. However, there are differences among
those tools, and comparisons among them have been
limited. In order to better understand these inter-tool
differences, five popular and publicly available INDEL
calling tools-GATK HaplotypeCaller, Platypus, VarScan2,
Scalpel, and GotCloud-were evaluated using simulation
data, 1000 Genomes Project data, and family-based
sequencing data. The accuracy of INDEL calling by each
tool was mainly evaluated by concordance rates.
Family-based sequencing data, which consisted of 49
individuals from eight Korean families, were used to
calculate Mendelian error rates. Our comparison results
show that GATK HaplotypeCaller usually performs the
best and that joint calling with Platypus can lead to
additional improvements in accuracy. The result of this
study provides important information regarding future
directions for the variant detection and the algorithms
development.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Nishida:2019:EEP,
author = "Shimpei Nishida and Shun Sakuraba and Kiyoshi Asai and
Michiaki Hamada",
title = "Estimating Energy Parameters for {RNA} Secondary
Structure Predictions Using Both Experimental and
Computational Data",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1645--1655",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2813388",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Computational RNA secondary structure prediction
depends on a large number of nearest-neighbor
free-energy parameters, including 10 parameters for
Watson--Crick stacked base pairs that were estimated
from experimental measurements of the free energies of
90 RNA duplexes. These experimental data are provided
by time-consuming and cost-intensive experiments. In
contrast, various modified nucleotides in RNAs, which
would affect not only their structures but also
functions, have been found, and rapid determination of
energy parameters for a such modified nucleotides is
needed. To reduce the high cost of determining energy
parameters, we propose a novel method to estimate
energy parameters from both experimental and
computational data, where the computational data are
provided by a recently developed molecular dynamics
simulation protocol. We evaluate our method for
Watson--Crick stacked base pairs, and show that
parameters estimated from 10 experimental data items
and 10 computational data items can predict RNA
secondary structures with accuracy comparable to that
using conventional parameters. The results indicate
that the combination of experimental free-energy
measurements and molecular dynamics simulations is
capable of estimating the thermodynamic properties of
RNA secondary structures at lower cost.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rhee:2019:ILC,
author = "Je-Keun Rhee and Jinseon Yoo and Kyu Ryung Kim and
Jeeyoon Kim and Yong-Jae Lee and Byoung Chul Cho and
Tae-Min Kim",
title = "Identification of Local Clusters of Mutation Hotspots
in Cancer-Related Genes and Their Biological
Relevance",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1656--1662",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2813375",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Mutation hotspots are either solitary amino acid
residues or stretches of amino acids that show elevated
mutation frequency in cancer-related genes, but their
prevalence and biological relevance are not completely
understood. Here, we developed a Smith-Waterman
algorithm-based mutation hotspot discovery method,
MutClustSW, to identify mutation hotspots of either
single or clustered amino acid residues. We identified
181 missense mutation hotspots from COSMIC and TCGA
mutation databases. In addition to 77 single amino acid
residue hotspots 42.5 percent including well-known
mutation hotspots such as IDH1 p.R132 and BRAF p.V600,
we identified 104 mutation hotspots 57.5 percent as
clusters or stretches of multiple amino acids, and the
hotspots on MUC2, EPPK1, KMT2C, and TP53 were larger
than 50 amino acids. Twelve of 27 nonsense mutation
hotspots 44.4 percent were observed in four
cancer-related genes, TP53, ARID1A, CDKN2A, and PTEN,
suggesting that truncating mutations on some tumor
suppressor genes are not randomly distributed as
previously assumed. We also show that hotspot mutations
have higher mutation allele frequency than
non-hotspots, and the hotspot information can be used
to prioritize the cancer drivers. Together, the
proposed algorithm and the mutation hotspot information
can serve as valuable resources in the selection of
functional driver mutations and associated genes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Rampogu:2019:INS,
author = "Shailima Rampogu and Ayoung Baek and Rohit Bavi and
Minky Son and Guang Ping Cao and Raj Kumar and Chanin
Park and Amir Zeb and Rabia Mukthar Rana and Seok Ju
Park and Keun Woo Lee",
title = "Identification of Novel Scaffolds with Dual Role as
Antiepileptic and Anti-Breast Cancer",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1663--1674",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2855138",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Aromatase inhibitors with an $ \mathrm {IC}_{50} $
value ranging from 1.4 to 49.7 $ \mu $M are known to
act as antiepileptic drugs besides being potential
breast cancer inhibitors. The aim of the present study
is to identify novel antiepileptic aromatase inhibitors
with higher activity exploiting the ligand-based
pharmacophore approach utilizing the experimentally
known inhibitors. The resultant Hypo1 consists of four
features and was further validated by using three
different strategies. Hypo1 was allowed to screen
different databases to identify lead molecules and were
further subjected to Lipinski's Rule of Five and ADMET
to establish their drug-like properties. Consequently,
the obtained 68-screened molecules were subjected to
molecular docking by GOLD v5.2.2. Furthermore, the
compounds with the highest dock scores were assessed
for molecular interactions. Later, the MD simulation
was applied to evaluate the protein backbone
stabilities and binding energies adapting GROMACS
v5.0.6 and MM/PBSA which was followed by the density
functional theory DFT, to analyze their orbital
energies, and further the energy gap between them.
Eventually, the number of Hit molecules was culled to
three projecting Hit1, Hit2, and Hit3 as the potential
lead compounds based on their highest dock scores,
hydrogen bond interaction, lowest energy gap, and the
least binding energies and stable MD results.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Sudo:2019:SWM,
author = "Hiroki Sudo and Masanobu Jimbo and Koji Nuida and Kana
Shimizu",
title = "Secure Wavelet Matrix: Alphabet-Friendly
Privacy-Preserving String Search for Bioinformatics",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1675--1684",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2814039",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Biomedical data often includes personal information,
and the technology is demanded that enables the
searching of such sensitive data while protecting
privacy. We consider a case in which a server has a
text database and a user searches the database to find
substring matches. The user wants to conceal his/her
query and the server wants to conceal the database
except for the search results. The previous approach
for this problem is based on a linear-time algorithm in
terms of alphabet size $ \mathbf {| \Sigma |} $, and it
cannot search on the database of large alphabet such as
biomedical documents. We present a novel algorithm that
can search a string in logarithmic time of $ \mathbf {|
\Sigma |} $. In our algorithm, named secure wavelet
matrix sWM, we use an additively homomorphic encryption
to build an efficient data structure called a wavelet
matrix. In an experiment using a simulated string of
length 10,000 whose alphabet size ranges from 4 to
1024, the run time of the sWM was up to around two
orders of magnitude faster than that of the previous
method. sWM enables the searching of a private database
efficiently and thus it will facilitate utilizing
sensitive biomedical information.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Kim:2019:SDG,
author = "Man-Sun Kim and Dongsan Kim and Jeong-Rae Kim",
title = "Stage-Dependent Gene Expression Profiling in
Colorectal Cancer",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1685--1692",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2814043",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Temporal gene expression profiles have been widely
considered to uncover the mechanism of cancer
development and progression. Gene expression patterns,
however, have been analyzed for limited stages with
small samples, without proper data pre-processing, in
many cases. With those approaches, it is difficult to
unveil the mechanism of cancer development over time.
In this study, we analyzed gene expression profiles of
two independent colorectal cancer sample datasets, each
of which contained 556 and 566 samples, respectively.
To find specific gene expression changes according to
cancer stage, we applied the linear mixed-effect
regression model LMER that controls other clinical
variables. Based on this methodology, we found two
types of gene expression patterns: continuously
increasing and decreasing genes as cancer develops. We
found that continuously increasing genes are related to
the nervous and developmental system, whereas the
others are related to the cell cycle and metabolic
processes. We further analyzed connected sub-networks
related to the two types of genes. From these results,
we suggest that the gene expression profile analysis
can be used to understand underlying the mechanisms of
cancer development such as cancer growth and
metastasis. Furthermore, our approach can provide a
good guideline for advancing our understanding of
cancer developmental processes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Moon:2019:SIC,
author = "SeongRyeol Moon and Curt Balch and Sungjin Park and
Jinhyuk Lee and Jiyong Sung and Seungyoon Nam",
title = "Systematic Inspection of the Clinical Relevance of
{TP53} Missense Mutations in Gastric Cancer",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1693--1701",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2814049",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The ``guardian of the genome,'' TP53, is one of the
most frequently mutated genes of all cancers. Despite
the important biological roles of TP53, the clinical
relevance of TP53 mutations, in gastric cancer GC,
remains largely unknown. Here, we systematically
assessed clinical relevance, in terms of TP53 mutation
positions, finding substantial variability. Thus, we
hypothesized that the position of the TP53 mutation
might affect clinical outcomes in GC. We systematically
inspected missense mutations in TP53, from a TCGA The
Cancer Genome Atlas GC dataset in UCSC Xena repository.
Specifically, we examined five aspects of each
mutational position: 1 the whole gene body; 2 known
hot-spots; 3 the DNA-binding domain; 4 the secondary
structure of the domain; and 5 individual mutation
positions. We then analyzed the clinical outcomes for
each aspect. These results showed that, in terms of
secondary structure, patients with mutations in turn
regions showed poor prognosis, compared to those with
mutations in beta strand regions log rank $ {\text {p}}
= 0.043 $. Also, in terms of individual mutation
positions, patients having mutations at R248 showed
poorer survival than other patients having mutations at
different TP53 positions log rank $ p = 0.035 $.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Hao:2019:ASS,
author = "FanChang Hao and Melvin Zhang and Hon Wai Leong",
title = "A $2$-Approximation Scheme for Sorting Signed
Permutations by Reversals, Transpositions,
Transreversals, and Block-Interchanges",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1702--1711",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2719681",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "We consider the problem of sorting signed permutations
by reversals, transpositions, transreversals, and
block-interchanges and give a 2-approximation scheme,
called the GSB Genome Sorting by Bridges scheme. Our
result extends 2-approximation algorithm of He and Chen
[12] that allowed only reversals and
block-interchanges, and also the 1.5 approximation
algorithm of Hartman and Sharan [11] that allowed only
transreversals and transpositions. We prove this result
by introducing three bridge structures in the
breakpoint graph, namely, the L-bridge, T-bridge, and
X-bridge and show that they model ``proper'' reversals,
transpositions, transreversals, and block-interchanges,
respectively. We show that we can always find at least
one of these three bridges in any breakpoint graph,
thus giving an upper bound on the number of operations
needed. We prove a lower bound on the distance and use
it to show that GSB has a 2-approximation ratio. An $
{\text {On}}^3 $ algorithm called GSB-I that is based
on the GSB approximation scheme presented in this paper
has recently been published by Yu, Hao, and Leong in
[17]. We note that our 2-approximation scheme admits
many possible implementations by varying the order we
search for proper rearrangement operations.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Li:2019:DTP,
author = "Limin Li and Menglan Cai",
title = "Drug Target Prediction by Multi-View Low Rank
Embedding",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1712--1721",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2706267",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Drug repositioning has been a key problem in drug
development, and heterogeneous data sources are used to
predict drug-target interactions by different
approaches. However, most of studies focus on a single
representation of drugs or proteins. It has been shown
that integrating multi-view representations of drugs
and proteins can strengthen the prediction ability. For
example, a drug can be represented by its chemical
structure, or by its chemical response in different
cells. A protein can be represented by its sequence, or
by its gene expression values in different cells. The
docking of drugs and proteins based on their structure
can be considered as one view structural view, and the
chemical performance of them based on gene expression
and drug response can be considered as another view
chemical view. In this work, we first propose a
single-view approach of SLRE based on low rank
embedding for an arbitrary view, and then extend it to
a multi-view approach of MLRE, which could integrate
both views. Our experiments show that our methods
perform significantly better than baseline methods
including single-view methods and multi-view methods.
We finally report predicted drug-target interactions
for 30 FDA-approved drugs.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Xie:2019:EEM,
author = "Wen-Bin Xie and Hong Yan and Xing-Ming Zhao",
title = "{EmDL}: Extracting {miRNA}--Drug Interactions from
Literature",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1722--1728",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2723394",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "The microRNAs miRNAs, regulators of
post-transcriptional processes, have been found to
affect the efficacy of drugs by regulating the
biological processes in which the target proteins of
drugs may be involved. For example, some drugs develop
resistance when certain miRNAs are overexpressed.
Therefore, identifying miRNAs that affect drug effects
can help understand the mechanisms of drug actions and
design more efficient drugs. Although some
computational approaches have been developed to predict
miRNA-drug associations, such associations rarely
provide explicit information about which miRNAs and how
they affect drug efficacy. On the other hand, there are
rich information about which miRNAs affect the efficacy
of which drugs in the literature. In this paper, we
present a novel text mining approach, named as EmDL
Extracting miRNA-Drug interactions from Literature, to
extract the relationships of miRNAs affecting drug
efficacy from literature. Benchmarking on the
drug-miRNA interactions manually extracted from MEDLINE
and PubMed Central, EmDL outperforms traditional text
mining approaches as well as other popular methods for
predicting drug-miRNA associations. Specifically, EmDL
can effectively identify the sentences that describe
the relationships of miRNAs affecting drug effects. The
drug-miRNA interactome presented here can help
understand how miRNAs affect drug effects and provide
insights into the mechanisms of drug actions. In
addition, with the information about drug-miRNA
interactions, more effective drugs or combinatorial
strategies can be designed in the future. The data used
here can be accessed at http://mtd.comp-sysbio.org/.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2019:LSF,
author = "Yanbo Wang and Quan Liu and Shan Huang and Bo Yuan",
title = "Learning a Structural and Functional Representation
for Gene Expressions: To Systematically Dissect Complex
Cancer Phenotypes",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1729--1742",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2702161",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Cancer is a heterogeneous disease, thus one of the
central problems is how to dissect the resulting
complex phenotypes in terms of their biological
building blocks. Computationally, this is to represent
and interpret high dimensional observations through a
structural and conceptual abstraction into the most
influential determinants underlying the problem. The
working hypothesis of this report is to consider gene
interaction to be largely responsible for the
manifestation of complex cancer phenotypes, thus where
the representation is to be conceptualized. Here, we
report a representation learning strategy combined with
regularizations, in which gene expressions are
described in terms of a regularized product of
meta-genes and their expression levels. The meta-genes
are constrained by gene interactions thus representing
their original topological contexts. The expression
levels are supervised by their conditional dependencies
among the observations thus providing a
cluster-specific constraint. We obtain both of these
structural constraints using a node-based graphical
model. Our representation allows the selection of more
influential modules, thus implicating their possible
roles in neoplastic transformations. We validate our
representation strategy by its robust recognitions of
various cancer phenotypes comparing with various
classical methods. The modules discovered are either
shared or specify for different types or stages of
human cancers, all of which are consistent with
literature and biology.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Wang:2019:NCA,
author = "Yishu Wang and Huaying Fang and Dejie Yang and Hongyu
Zhao and Minghua Deng",
title = "Network Clustering Analysis Using Mixture
Exponential-Family Random Graph Models and Its
Application in Genetic Interaction Data",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1743--1752",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2743711",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Motivation: Epistatic miniarrary profile EMAP studies
have enabled the mapping of large-scale genetic
interaction networks and generated large amounts of
data in model organisms. It provides an incredible set
of molecular tools and advanced technologies that
should be efficiently understanding the relationship
between the genotypes and phenotypes of individuals.
However, the network information gained from EMAP
cannot be fully exploited using the traditional
statistical network models. Because the genetic network
is always heterogeneous, for example, the network
structure features for one subset of nodes are
different from those of the left nodes.
Exponential-family random graph models ERGMs are a
family of statistical models, which provide a
principled and flexible way to describe the structural
features e.g., the density, centrality, and
assortativity of an observed network. However, the
single ERGM is not enough to capture this heterogeneity
of networks. In this paper, we consider a mixture ERGM
MixtureEGRM networks, which model a network with
several communities, where each community is described
by a single EGRM. Results: EM algorithm is a classical
method to solve the mixture problem, however, it will
be very slow when the data size is huge in the numerous
applications. We adopt an efficient novel online graph
clustering algorithm to classify the graph nodes and
estimate the ERGM parameters for the MixtureERGM. In
comparison studies, the MixtureERGM outperforms the
role analysis for the network cluster in which the
mixture of exponential-family random graph model is
developed for many ego-network according to their
roles. One genetic interaction network of yeast and two
real social networks provided as supplemental
materials, which can be found on the Computer Society
Digital Library at
http://doi.ieeecomputersociety.org/10.1109/TCBB.2017.2743711
show the wide potential application of the
MixtureERGM.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Zhao:2019:PPI,
author = "Zhenni Zhao and Xinqi Gong",
title = "Protein--Protein Interaction Interface Residue Pair
Prediction Based on Deep Learning Architecture",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1753--1759",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2706682",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Motivation: Proteins usually fulfill their biological
functions by interacting with other proteins. Although
some methods have been developed to predict the binding
sites of a monomer protein, these are not sufficient
for prediction of the interaction between two monomer
proteins. The correct prediction of interface residue
pairs from two monomer proteins is still an open
question and has great significance for practical
experimental applications in the life sciences. We hope
to build a method for the prediction of interface
residue pairs that is suitable for those applications.
Results: Here, we developed a novel deep network
architecture called the multi-layered Long-Short Term
Memory networks LSTMs approach for the prediction of
protein interface residue pairs. First, we created
three new descriptions and used other six worked
characterizations to describe an amino acid, then we
employed these features to discriminate between
interface residue pairs and non-interface residue
pairs. Second, we used two thresholds to select residue
pairs that are more likely to be interface residue
pairs. Furthermore, this step increases the proportion
of interface residue pairs and reduces the influence of
imbalanced data. Third, we built deep network
architectures based on Long-Short Term Memory networks
algorithm to organize and refine the prediction of
interface residue pairs by employing features mentioned
above. We trained the deep networks on dimers in the
unbound state in the international Protein-protein
Docking Benchmark version 3.0. The updated data sets in
the versions 4.0 and 5.0 were used as the validation
set and test set respectively. For our best model, the
accuracy rate was over 62 percent when we chose the top
0.2 percent pairs of every dimer in the test set as
predictions, which will be very helpful for the
understanding of protein-protein interaction mechanisms
and for guidance in biological experiments.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Yang:2019:RHD,
author = "Xi Yang and Guoqiang Han and Hongmin Cai and Yan
Song",
title = "Recovering Hidden Diagonal Structures via Non-Negative
Matrix Factorization with Multiple Constraints",
journal = j-TCBB,
volume = "16",
number = "5",
pages = "1760--1772",
month = sep,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2690282",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Nov 29 16:39:03 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
abstract = "Revealing data with intrinsically diagonal block
structures is particularly useful for analyzing groups
of highly correlated variables. Earlier researches
based on non-negative matrix factorization NMF have
been shown to be effective in representing such data by
decomposing the observed data into two factors, where
one factor is considered to be the feature and the
other the expansion loading from a linear algebra
perspective. If the data are sampled from multiple
independent subspaces, the loading factor would possess
a diagonal structure under an ideal matrix
decomposition. However, the standard NMF method and its
variants have not been reported to exploit this type of
data via direct estimation. To address this issue, a
non-negative matrix factorization with multiple
constraints model is proposed in this paper. The
constraints include an sparsity norm on the feature
matrix and a total variational norm on each column of
the loading matrix. The proposed model is shown to be
capable of efficiently recovering diagonal block
structures hidden in observed samples. An efficient
numerical algorithm using the alternating direction
method of multipliers model is proposed for optimizing
the new model. Compared with several benchmark models,
the proposed method performs robustly and effectively
for simulated and real biological data.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J954",
}
@Article{Halder:2019:LII,
author = "Anup Kumar Halder and Piyali Chatterjee and Mita
Nasipuri and Dariusz Plewczynski and Subhadip Basu",
title = "{3gClust}: Human Protein Cluster Analysis",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1773--1784",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2840996",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2840996",
abstract = "We present a human protein cluster analysis by
combining: (1) $n$-gram based amino acid frequency
features, (2) optimal feature selection, (3)
hierarchical clustering, and (4) advanced partitioning
techniques. Our method qualitatively and \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Majumder:2019:CMD,
author = "Aurpan Majumder and Mrityunjay Sarkar and Prolay
Sharma",
title = "A Composite Mode Differential Gene Regulatory
Architecture based on Temporal Expression Profiles",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1785--1793",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2828418",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2828418",
abstract = "Exploring the complex interactive mechanism in a Gene
Regulatory Network (GRN) developed using transcriptome
data obtained from standard microarray and/or RNA-seq
experiments helps us to understand the triggering
factors in cancer research. The \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Feng:2019:DLA,
author = "Yujie Feng and Fan Yang and Xichuan Zhou and Yanli Guo
and Fang Tang and Fengbo Ren and Jishun Guo and
Shuiwang Ji",
title = "A Deep Learning Approach for Targeted
Contrast-Enhanced Ultrasound Based Prostate Cancer
Detection",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1794--1801",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2835444",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2835444",
abstract = "The important role of angiogenesis in cancer
development has driven many researchers to investigate
the prospects of noninvasive cancer diagnosis based on
the technology of contrast-enhanced ultrasound (CEUS)
imaging. This paper presents a deep learning \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Brankovic:2019:DFS,
author = "Aida Brankovic and Marjan Hosseini and Luigi Piroddi",
title = "A Distributed Feature Selection Algorithm Based on
Distance Correlation with an Application to
Microarrays",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1802--1815",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2833482",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2833482",
abstract = "DNA microarray datasets are characterized by a large
number of features with very few samples, which is a
typical cause of overfitting and poor generalization in
the classification task. Here, we introduce a novel
feature selection (FS) approach which \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2019:MLP,
author = "Li Zhang and Ho-Chun Wu and Cheuk-Hei Ho and
Shing-Chow Chan",
title = "A Multi-{Laplacian} Prior and Augmented {Lagrangian}
Approach to the Exploratory Analysis of Time-Varying
Gene and Transcriptional Regulatory Networks for Gene
Microarray Data",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1816--1829",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2828810",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2828810",
abstract = "This paper proposes a novel multi-Laplacian prior
(MLP) and augmented Lagrangian method (ALM) approach
for gene interactions and putative transcription
factors (TFs) identification from time-course gene
microarray data. It employs a non-linear time-.
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Malhotra:2019:PTE,
author = "Anvita Gupta Malhotra and Sudha Singh and Mohit Jha
and Khushhali M. Pandey",
title = "A Parametric Targetability Evaluation Approach for
Vitiligo Proteome Extracted through Integration of Gene
Ontologies and Protein Interaction Topologies",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1830--1842",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2835459",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2835459",
abstract = "Vitiligo is a well-known skin disorder with complex
etiology. Vitiligo pathogenesis is multifaceted with
many ramifications. A computational systemic path was
designed to first propose candidate disease proteins by
merging properties from protein \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Timonen:2019:PFM,
author = "Juho Timonen and Henrik Mannerstr{\"o}m and Harri
L{\"a}hdesm{\"a}ki and Jukka Intosalmi",
title = "A Probabilistic Framework for Molecular Network
Structure Inference by Means of Mechanistic Modeling",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1843--1854",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2825327",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2825327",
abstract = "Ordinary differential equations (ODEs) provide a
powerful formalism to model molecular networks
mechanistically. However, inferring the model
structure, given a set of time course measurements and
a large number of alternative molecular mechanisms, is
a \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ma:2019:IFP,
author = "Xiaoke Ma and Penggang Sun and Zhong-Yuan Zhang",
title = "An Integrative Framework for Protein Interaction
Network and Methylation Data to Discover Epigenetic
Modules",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1855--1866",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2831666",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2831666",
abstract = "DNA methylation is a critical epigenetic modification
that plays an important role in cancers. The available
algorithms fail to fully characterize epigenetic
modules. To address this issue, we first characterize
the epigenetic module as a group of well-. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Caudai:2019:LSC,
author = "Claudia Caudai and Emanuele Salerno and Monica
Zopp{\`e} and Ivan Merelli and Anna Tonazzini",
title = "{ChromStruct 4}: a {Python} Code to Estimate the
Chromatin Structure from {Hi-C} Data",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1867--1878",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2838669",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/python.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2838669",
abstract = "A method and a stand-alone Python code to estimate the
3D chromatin structure from chromosome conformation
capture data are presented. The method is based on a
multiresolution, modified-bead-chain chromatin model,
evolved through quaternion operators in a \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhou:2019:CCK,
author = "Huiwei Zhou and Yunlong Yang and Shixian Ning and
Zhuang Liu and Chengkun Lang and Yingyu Lin and Degen
Huang",
title = "Combining Context and Knowledge Representations for
Chemical-Disease Relation Extraction",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1879--1889",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2838661",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2838661",
abstract = "Automatically extracting the relationships between
chemicals and diseases is significantly important to
various areas of biomedical research and health care.
Biomedical experts have built many large-scale
knowledge bases (KBs) to advance the development
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Luo:2019:CDR,
author = "Huimin Luo and Jianxin Wang and Min Li and Junwei Luo
and Peng Ni and Kaijie Zhao and Fang-Xiang Wu and Yi
Pan",
title = "Computational Drug Repositioning with Random Walk on a
Heterogeneous Network",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1890--1900",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2832078",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2832078",
abstract = "Drug repositioning is an efficient and promising
strategy to identify new indications for existing
drugs, which can improve the productivity of
traditional drug discovery and development. Rapid
advances in high-throughput technologies have generated
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Stella:2019:DDB,
author = "Sabrina Stella and Roberto Chignola and Edoardo
Milotti",
title = "Dynamical Detection of Boundaries and Cavities in
Biophysical Cell-Based Simulations of Growing Tumor
Tissues",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1901--1911",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2827374",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2827374",
abstract = "Cell-based lattice-free simulations of the growth of
tumor tissues require the definition of geometrical and
topological relations among cells and the other basic
elements of the simulation (most notably the local and
the global environments). This is \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cai:2019:EAF,
author = "Xingyu Cai and Abdullah-Al Mamun and Sanguthevar
Rajasekaran",
title = "Efficient Algorithms for Finding the Closest $ \ell
1$-Mers in Biological Data",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1912--1921",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2843364",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2843364",
abstract = "With the advances in the next generation sequencing
technology, huge amounts of data have been and get
generated in biology. A bottleneck in dealing with such
datasets lies in developing effective algorithms for
extracting useful information from them. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Hu:2019:EDP,
author = "Lun Hu and Xiaohui Yuan and Xing Liu and Shengwu Xiong
and Xin Luo",
title = "Efficiently Detecting Protein Complexes from Protein
Interaction Networks via Alternating Direction Method
of Multipliers",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1922--1935",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2844256",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2844256",
abstract = "Protein complexes are crucial in improving our
understanding of the mechanisms employed by proteins.
Various computational algorithms have thus been
proposed to detect protein complexes from protein
interaction networks. However, given massive protein
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Dehghani:2019:EPA,
author = "Toktam Dehghani and Mahmoud Naghibzadeh and Javad
Sadri",
title = "Enhancement of Protein $ \beta $-Sheet Topology
Prediction Using Maximum Weight Disjoint Path Cover",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1936--1947",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2837753",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2837753",
abstract = "Predicting $ \beta $-sheet topology ($ \beta
$-topology) is one of the most critical intermediate
steps towards protein structure and function
prediction. The $ \beta $-topology prediction problem
is defined as the determination of the optimal
arrangement of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jiang:2019:FNN,
author = "Xue Jiang and Han Zhang and Zhao Zhang and Xiongwen
Quan",
title = "Flexible Non-Negative Matrix Factorization to Unravel
Disease-Related Genes",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1948--1957",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2823746",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2823746",
abstract = "Recently, non-negative matrix factorization (NMF) has
been shown to perform well in the analysis of omics
data. NMF assumes that the expression level of one gene
is a linear additive composition of metagenes. The
elements in metagene matrix represent the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Milano:2019:GNA,
author = "Marianna Milano and Pietro Hiram Guzzi and Mario
Cannataro",
title = "{GLAlign}: a Novel Algorithm for Local Network
Alignment",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1958--1969",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2830323",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2830323",
abstract = "Networks are successfully used as a modelling
framework in many application domains. For instance,
Protein-Protein Interaction Networks (PPINs) model the
set of interactions among proteins in a cell. A
critical application of network analysis is the
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Paul:2019:IDE,
author = "Amit Paul and Jaya Sil",
title = "Identification of Differentially Expressed Genes to
Establish New Biomarker for Cancer Prediction",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1970--1985",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2837095",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2837095",
abstract = "The goal of the human genome project is to integrate
genetic information into different clinical therapies.
To achieve this goal, different computational
algorithms are devised for identifying the biomarker
genes, cause of complex diseases. However, most
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Hao:2019:ICG,
author = "Xiaoke Hao and Xiaohui Yao and Shannon L. Risacher and
Andrew J. Saykin and Jintai Yu and Huifu Wang and Lan
Tan and Li Shen and Daoqiang Zhang",
title = "Identifying Candidate Genetic Associations with
{MRI}-Derived {AD}-Related {ROI} via Tree-Guided Sparse
Learning",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1986--1996",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2833487",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2833487",
abstract = "Imaging genetics has attracted significant interests
in recent studies. Traditional work has focused on
mass-univariate statistical approaches that identify
important single nucleotide polymorphisms (SNPs)
associated with quantitative traits (QTs) of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Fan:2019:ILS,
author = "Anjing Fan and Haitao Wang and Hua Xiang and Xiufen
Zou",
title = "Inferring Large-Scale Gene Regulatory Networks Using a
Randomized Algorithm Based on Singular Value
Decomposition",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "1997--2008",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2825446",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2825446",
abstract = "Reconstructing large-scale gene regulatory networks
(GRNs) is a challenging problem in the field of
computational biology. Various methods for inferring
GRNs have been developed, but they fail to accurately
infer GRNs with a large number of genes. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chaudhury:2019:MVS,
author = "Ayan Chaudhury and Christopher Ward and Ali Talasaz
and Alexander G. Ivanov and Mark Brophy and Bernard
Grodzinski and Norman P. A. H{\"u}ner and Rajnikant V.
Patel and John L. Barron",
title = "Machine Vision System for {$3$D} Plant Phenotyping",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "2009--2022",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2824814",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2824814",
abstract = "Machine vision for plant {\em phenotyping\/} is an
emerging research area for producing high throughput in
agriculture and crop science applications. Since 2D
based approaches have their inherent limitations, 3D
plant analysis is becoming state \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Nalbantoglu:2019:MAM,
author = "O. Ufuk Nalbantoglu and Khalid Sayood",
title = "{MIMOSA}: Algorithms for Microbial Profiling",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "2023--2034",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2830324",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2830324",
abstract = "A significant goal of the study of metagenomes
obtained from an environment is to find the microbial
diversity and the abundance of each organism in the
community. Phylotyping and binning methods which
address this problem generally operate using either
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Tsompanas:2019:MMF,
author = "Michail-Antisthenis Tsompanas and Andrew Adamatzky and
Ioannis Ieropoulos and Neil William Phillips and
Georgios Ch. Sirakoulis and John Greenman",
title = "Modelling Microbial Fuel Cells Using Lattice
{Boltzmann} Methods",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "2035--2045",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2831223",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2831223",
abstract = "An accurate modelling of bio-electrochemical processes
that govern Microbial Fuel Cells (MFCs) and mapping
their behavior according to several parameters will
enhance the development of MFC technology and enable
their successful implementation in well \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2019:PFI,
author = "Runtao Yang and Chengjin Zhang and Rui Gao and Lina
Zhang and Qing Song",
title = "Predicting {FAD} Interacting Residues with Feature
Selection and Comprehensive Sequence Descriptors",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "2046--2056",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2824332",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2824332",
abstract = "The function of a flavoprotein is determined to a
great extent by the binding sites on its surface that
interacts with flavin adenine dinucleotide (FAD).
Malfunction or dysregulation of FAD binding leads to a
series of diseases. Therefore, accurately \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{K:2019:PDL,
author = "MD Aksam V. K. and V. M. Chandrasekaran and
Sundaramurthy Pandurangan",
title = "Protein Domain Level Cancer Drug Targets in the
Network of {MAPK} Pathways",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "2057--2065",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2829507",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2829507",
abstract = "Proteins in the MAPK pathways considered as potential
drug targets for cancer treatment. Pathways along with
the cross-talks increase their scope to view them as a
network of MAPK pathways. Side effect causing targeted
domains act as a proxy for drug \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Biswas:2019:RIM,
author = "Ashis Kumer Biswas and Dong-Chul Kim and Mingon Kang
and Jean X. Gao",
title = "Robust Inductive Matrix Completion Strategy to Explore
Associations Between {LincRNAs} and Human Disease
Phenotypes",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "2066--2077",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2844816",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2844816",
abstract = "Over the past few years, it has been established that
a number of long intergenic non-coding RNAs (lincRNAs)
are linked to a wide variety of human diseases. The
relationship among many other lincRNAs still remains as
puzzle. Validation of such link \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wingfield:2019:RMM,
author = "Benjamin Wingfield and Sonya Coleman and TM McGinnity
and AJ Bjourson",
title = "Robust Microbial Markers for Non-Invasive Inflammatory
Bowel Disease Identification",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "2078--2088",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2831212",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2831212",
abstract = "Inflammatory Bowel Disease (IBD) is an umbrella term
for a group of inflammatory diseases of the
gastrointestinal tract, including Crohn's Disease and
ulcerative colitis. Changes to the intestinal
microbiome, the community of micro-organisms that
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sevakula:2019:TLM,
author = "Rahul K. Sevakula and Vikas Singh and Nishchal K.
Verma and Chandan Kumar and Yan Cui",
title = "Transfer Learning for Molecular Cancer Classification
Using Deep Neural Networks",
journal = j-TCBB,
volume = "16",
number = "6",
pages = "2089--2100",
month = nov,
year = "2019",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2822803",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:47 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2822803",
abstract = "The emergence of deep learning has impacted numerous
machine learning based applications and research. The
reason for its success lies in two main advantages: (1)
it provides the ability to learn very complex
non-linear relationships between features and (2).
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Martin-Vide:2020:ACB,
author = "Carlos Mart{\'\i}n-Vide and Miguel A.
Vega-Rodr{\'\i}guez",
title = "Algorithms for Computational Biology: Fifth Edition",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "1--1",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2949851",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2019.2949851",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Brito:2020:HRT,
author = "Klairton Lima Brito and Andre Rodrigues Oliveira and
Ulisses Dias and Zanoni Dias",
title = "Heuristics for the Reversal and Transposition Distance
Problem",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "2--13",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2945759",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2019.2945759",
abstract = "We present three heuristics --- {\em Sliding Window},
{\em Look Ahead}, and {Iterative Sliding Window} --- to
improve solutions for the Sorting Signed Permutations
by Reversals and Transpositions Problem. We investigate
the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{vanIersel:2020:PTA,
author = "Leo van Iersel and Remie Janssen and Mark Jones and
Yukihiro Murakami and Norbert Zeh",
title = "Polynomial-Time Algorithms for Phylogenetic Inference
Problems Involving Duplication and Reticulation",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "14--26",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2934957",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2019.2934957",
abstract = "A common problem in phylogenetics is to try to infer a
species phylogeny from gene trees. We consider
different variants of this problem. The first variant,
called {\sc Unrestricted Minimal Episodes Inference},
aims at inferring a species tree based on \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Miyawaki-Kuwakado:2020:PMC,
author = "Atsuko Miyawaki-Kuwakado and Soichiro Komori and
Fumihide Shiraishi",
title = "A Promising Method for Calculating True Steady-State
Metabolite Concentrations in Large-Scale Metabolic
Reaction Network Models",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "27--36",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2853724",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2853724",
abstract = "The calculation of steady-state metabolite
concentrations in metabolic reaction network models is
the first step in the sensitivity analysis of a
metabolic reaction system described by differential
equations. However, this calculation becomes very
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Maji:2020:SEA,
author = "Ranjan Kumar Maji and Sunirmal Khatua and Zhumur
Ghosh",
title = "A Supervised Ensemble Approach for Sensitive
{microRNA} Target Prediction",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "37--46",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2858252",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2858252",
abstract = "MicroRNAs, a class of small non-coding RNAs, regulate
important biological functions via post-transcriptional
regulation of messenger RNAs (mRNAs). Despite rapid
development in miRNA research, precise experimental
methods to determine miRNA target \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Kocak:2020:ABP,
author = "Mehmet Kocak and Khyobeni Mozhui",
title = "An Application of the {Bayesian} Periodicity Test to
Identify Diurnal Rhythm Genes in the Brain",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "47--55",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2859971",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2859971",
abstract = "Biological systems are extremely dynamic and many
aspects of cellular processes show rhythmic circadian
patterns. Extracting such information from large
expression data is challenging. In this work, we
present a modified application of the Empirical Bayes
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ahmed:2020:AEC,
author = "Syed Sazzad Ahmed and Swarup Roy and Jugal Kalita",
title = "Assessing the Effectiveness of Causality Inference
Methods for Gene Regulatory Networks",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "56--70",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2853728",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2853728",
abstract = "Causality inference is the use of computational
techniques to predict possible causal relationships for
a set of variables, thereby forming a directed network.
Causality inference in Gene Regulatory Networks (GRNs)
is an important, yet challenging task \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2020:CBM,
author = "Cheng-Hong Yang and Yu-Da Lin and Li-Yeh Chuang",
title = "Class Balanced Multifactor Dimensionality Reduction to
Detect Gene--Gene Interactions",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "71--81",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2858776",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2858776",
abstract = "Detecting gene--gene interactions in single-nucleotide
polymorphism data is vital for understanding disease
susceptibility. However, existing approaches may be
limited by the sample size in case--control studies.
Herein, we propose a balance \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{weiTan:2020:DLP,
author = "Jing wei Tan and Siow-Wee Chang and Sameem
Abdul-Kareem and Hwa Jen Yap and Kien-Thai Yong",
title = "Deep Learning for Plant Species Classification Using
Leaf Vein Morphometric",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "82--90",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2848653",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2848653",
abstract = "An automated plant species identification system could
help botanists and layman in identifying plant species
rapidly. Deep learning is robust for feature extraction
as it is superior in providing deeper information of
images. In this research, a new CNN-. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Feng:2020:DMP,
author = "Yangqin Feng and Lei Zhang and Juan Mo",
title = "Deep Manifold Preserving Autoencoder for Classifying
Breast Cancer Histopathological Images",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "91--101",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2858763",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2858763",
abstract = "Classifying breast cancer histopathological images
automatically is an important task in computer assisted
pathology analysis. However, extracting informative and
non-redundant features for histopathological image
classification is challenging due to the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Habibi:2020:DPC,
author = "Mahnaz Habibi and Pegah Khosravi",
title = "Disruption of Protein Complexes from Weighted Complex
Networks",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "102--109",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2859952",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2859952",
abstract = "Essential proteins are indispensable units for living
organisms. Removing those leads to disruption of
protein complexes and causing lethality. Recently,
theoretical methods have been presented to detect
essential proteins in protein interaction network.
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{He:2020:DSJ,
author = "Yicheng He and Junfeng Liu and Xia Ning",
title = "Drug Selection via Joint Push and Learning to Rank",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "110--123",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2848908",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2848908",
abstract = "Selecting the right drugs for the right patients is a
primary goal of precision medicine. In this article, we
consider the problem of cancer drug selection in a
learning-to-rank framework. We have formulated the
cancer drug selection problem as to \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhou:2020:EPD,
author = "Jiyun Zhou and Qin Lu and Ruifeng Xu and Lin Gui and
Hongpeng Wang",
title = "{EL\_LSTM}: Prediction of {DNA}-Binding Residue from
Protein Sequence by Combining Long Short-Term Memory
and Ensemble Learning",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "124--135",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2858806",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2858806",
abstract = "Most past works for DNA-binding residue prediction did
not consider the relationships between residues. In
this paper, we propose a novel approach for DNA-binding
residue prediction, referred to as EL_LSTM, which
includes two main components. The first \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Pan:2020:FBG,
author = "Tony Pan and Rahul Nihalani and Srinivas Aluru",
title = "Fast {de Bruijn} Graph Compaction in Distributed
Memory Environments",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "136--148",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2858797",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2858797",
abstract = "De Bruijn graph based genome assembly has gained
popularity as short read sequencers become ubiquitous.
A core assembly operation is the generation of unitigs,
which are sequences corresponding to chains in the
graph. Unitigs are used as building blocks \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Marczyk:2020:GXA,
author = "Michal Marczyk and Roman Jaksik and Andrzej Polanski
and Joanna Polanska",
title = "{GaMRed}-Adaptive Filtering of High-Throughput
Biological Data",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "149--157",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2858825",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2858825",
abstract = "Data filtering based on removing non-informative
features, with unchanged signal between compared
experimental conditions, can significantly increase
sensitivity of methods used to detect differentially
expressed genes or other molecular components
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Pons:2020:GLL,
author = "Joan Carles Pons and Celine Scornavacca and Gabriel
Cardona",
title = "Generation of Level-$k$ k {LGT} Networks",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "158--164",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2895344",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2019.2895344",
abstract = "Phylogenetic networks provide a mathematical model to
represent the evolution of a set of species where,
apart from speciation, reticulate evolutionary events
have to be taken into account. Among these events,
lateral gene transfers need special \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cai:2020:IMM,
author = "Jiulun Cai and Hongmin Cai and Jiazhou Chen and Xi
Yang",
title = "Identifying ``Many-to--Many'' Relationships between
Gene-Expression Data and Drug-Response Data via Sparse
Binary Matching",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "165--176",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2849708",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2849708",
abstract = "Identifying gene-drug patterns is a critical step in
pharmacology for unveiling disease mechanisms and drug
discovery. The availability of high-throughput
technologies accumulates massive large-scale
pharmacological and genomic data, and thus provides a
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liao:2020:ILI,
author = "Xingyu Liao and Min Li and Junwei Luo and You Zou and
Fang-Xiang Wu and Yi Pan and Feng Luo and Jianxin
Wang",
title = "Improving {\em de novo\/} Assembly Based on Read
Classification",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "177--188",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2861380",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2861380",
abstract = "Due to sequencing bias, sequencing error, and repeat
problems, the genome assemblies usually contain
misarrangements and gaps. When tackling these problems,
current assemblers commonly consider the read libraries
as a whole and adopt the same strategy to \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2020:LPL,
author = "Tianyi Zhang and Minghui Wang and Jianing Xi and Ao
Li",
title = "{LPGNMF}: Predicting Long Non-Coding {RNA} and Protein
Interaction Using Graph Regularized Nonnegative Matrix
Factorization",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "189--197",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2861009",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2861009",
abstract = "Long non-coding RNAs (lncRNA) play crucial roles in a
variety of biological processes and complex diseases.
Massive studies have indicated that lncRNAs interact
with related proteins to exert regulation of cellular
biological processes. Because it is time-. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Carvajal-Lopez:2020:MBQ,
author = "Patricia Carvajal-L{\'o}pez and Fernando D. {Von
Borstel} and Amada Torres and Gabriella Rustici and
Joaqu{\'\i}n Guti{\'e}rrez and Eduardo Romero-Vivas",
title = "Microarray-Based Quality Assessment as a Supporting
Criterion for {\em de novo\/} Transcriptome Assembly
Selection",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "198--206",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2860997",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2860997",
abstract = "RNA-Sequencing and {\em de novo\/} assembly have
enabled the analysis of species with non-available
reference transcriptomes, although intrinsic features
(biological and technical) induce errors in the
reconstruction. A strategy to resolve these \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Acharya:2020:MFG,
author = "Sudipta Acharya and Sriparna Saha and Prasanna
Pradhan",
title = "Multi-Factored Gene-Gene Proximity Measures Exploiting
Biological Knowledge Extracted from Gene Ontology:
Application in Gene Clustering",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "207--219",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2849362",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2849362",
abstract = "To describe the cellular functions of proteins and
genes, a potential dynamic vocabulary is Gene Ontology
(GO), which comprises of three sub-ontologies namely,
Biological-process, Cellular-component, and
Molecular-function. It has several applications in
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2020:MMT,
author = "Tao Li and Xiankai Zhang and Feng Luo and Fang-Xiang
Wu and Jianxin Wang",
title = "{MultiMotifMaker}: a Multi-Thread Tool for Identifying
{DNA} Methylation Motifs from {Pacbio} Reads",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "220--225",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2861399",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/multithreading.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2861399",
abstract = "The methylation of DNA is an important mechanism to
control biological processes. Recently, the Pacbio SMRT
technology provides a new way to identify base
methylation in the genome. MotifMaker is a tool
developed by Pacbio for discovering DNA methylation
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2020:NIM,
author = "Xiangtao Li and Shixiong Zhang and Ka-Chun Wong",
title = "Nature-Inspired Multiobjective Epistasis Elucidation
from Genome-Wide Association Studies",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "226--237",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2849759",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2849759",
abstract = "In recent years, the detection of epistatic
interactions of multiple genetic variants on the causes
of complex diseases brings a significant challenge in
genome-wide association studies (GWAS). However, most
of the existing methods still suffer from \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yu:2020:NGF,
author = "Guoxian Yu and Keyao Wang and Guangyuan Fu and Maozu
Guo and Jun Wang",
title = "{NMFGO}: Gene Function Prediction via Nonnegative
Matrix Factorization with Gene Ontology",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "238--249",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2861379",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2861379",
abstract = "Gene Ontology (GO) is a controlled vocabulary of terms
that describe molecule function, biological roles, and
cellular locations of gene products (i.e., proteins and
RNAs), it hierarchically organizes more than 43,000 GO
terms via the direct acyclic \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{pour:2020:OBF,
author = "Ali Foroughi pour and Lori A. Dalton",
title = "Optimal {Bayesian} Filtering for Biomarker Discovery:
Performance and Robustness",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "250--263",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2858814",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2858814",
abstract = "Optimal Bayesian feature filtering (OBF) is a fast and
memory-efficient algorithm that optimally identifies
markers with distributional differences between
treatment groups under Gaussian models. Here, we study
the performance and robustness of OBF for \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Mazrouee:2020:PMF,
author = "Sepideh Mazrouee and Wei Wang",
title = "{PolyCluster}: Minimum Fragment Disagreement
Clustering for Polyploid Phasing",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "264--277",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2858803",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2858803",
abstract = "Phasing is an emerging area in computational biology
with important applications in clinical decision making
and biomedical sciences. While machine learning
techniques have shown tremendous potential in many
biomedical applications, their utility in \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Pyne:2020:RRT,
author = "Saptarshi Pyne and Alok Ranjan Kumar and Ashish
Anand",
title = "Rapid Reconstruction of Time-Varying Gene Regulatory
Networks",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "278--291",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2861698",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2861698",
abstract = "Rapid advancements in high-throughput technologies
have resulted in genome-scale time series datasets.
Uncovering the temporal sequence of gene regulatory
events, in the form of time-varying gene regulatory
networks (GRNs), demands computationally fast,
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Delgado:2020:STR,
author = "Ram{\'o}n A. Delgado and Zhiyong Chen and Richard H.
Middleton",
title = "Stepwise {Tikhonov} Regularisation: Application to the
Prediction of {HIV-1} Drug Resistance",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "292--301",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2849369",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2849369",
abstract = "This paper focuses on constructing genotypic
predictors for antiretroviral drug susceptibility of
HIV. To this end, a method to recover the largest
elements of an unknown vector in a least squares
problem is developed. The proposed method introduces
two \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Alden:2020:UEE,
author = "Kieran Alden and Jason Cosgrove and Mark Coles and Jon
Timmis",
title = "Using Emulation to Engineer and Understand Simulations
of Biological Systems",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "302--315",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2843339",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2843339",
abstract = "Modeling and simulation techniques have demonstrated
success in studying biological systems. As the drive to
better capture biological complexity leads to more
sophisticated simulators, it becomes challenging to
perform statistical analyses that help \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ganor:2020:NGT,
author = "Dor Ganor and Ron Y. Pinter and Meirav Zehavi",
title = "A Note on {GRegNetSim}: a Tool for the Discrete
Simulation and Analysis of Genetic Regulatory
Networks",
journal = j-TCBB,
volume = "17",
number = "1",
pages = "316--320",
month = jan,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2878749",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2878749",
abstract = "Discrete simulations of genetic regulatory networks
have been used to study subsystems of yeast
successfully. Existing models underling these
simulations are based on specific transition functions,
which determine the node states in the network.
However, \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2020:BPD,
author = "Ziwei Chen and Xiangqi Bai and Liang Ma and Xiawei
Wang and Xiuqin Liu and Yuting Liu and Luonan Chen and
Lin Wan",
title = "A Branch Point on Differentiation Trajectory is the
Bifurcating Event Revealed by Dynamical Network
Biomarker Analysis of Single-Cell Data",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "366--375",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2847690",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2847690",
abstract = "The advance in single-cell profiling technologies and
the development in computational algorithms provide the
opportunity to reconstruct pseudo temporal trajectory
with branch point of cellular development. On the other
hand, theories such as dynamical \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2020:CCM,
author = "Lihua Zhang and Shihua Zhang",
title = "Comparison of Computational Methods for Imputing
Single-Cell {RNA}-Sequencing Data",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "376--389",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2848633",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2848633",
abstract = "Single-cell RNA-sequencing (scRNA-seq) is a recent
breakthrough technology, which paves the way for
measuring RNA levels at single cell resolution to study
precise biological functions. One of the main
challenges when analyzing scRNA-seq data is the
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2020:DDM,
author = "Feng Li and Lin Gao and Bingbo Wang",
title = "Detection of Driver Modules with Rarely Mutated Genes
in Cancers",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "390--401",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2846262",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2846262",
abstract = "Identifying driver modules or pathways is a key
challenge to interpret the molecular mechanisms and
pathogenesis underlying cancer. An increasing number of
studies suggest that rarely mutated genes are important
for the development of cancer. However, the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jiang:2020:DSE,
author = "Hao Jiang and Yushan Qiu and Wenpin Hou and Xiaoqing
Cheng and Man Yi Yim and Wai-Ki Ching",
title = "Drug Side-Effect Profiles Prediction: From Empirical
to Structural Risk Minimization",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "402--410",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2850884",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2850884",
abstract = "The identification of drug side-effects is considered
to be an important step in drug design, which could not
only shorten the time but also reduce the cost of drug
development. In this paper, we investigate the
relationship between the potential side-. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Feng:2020:EEL,
author = "Zhan-Ying Feng and Yong Wang",
title = "{ELF}: Extract Landmark Features By Optimizing
Topology Maintenance, Redundancy, and Specificity",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "411--421",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2846225",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2846225",
abstract = "Feature selection is the process of selecting a subset
of landmark features for model construction when there
are many features and a comparatively few samples. The
far-reaching development technologies such as
biological sequencing at single cell level \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xi:2020:HNM,
author = "Jianing Xi and Ao Li and Minghui Wang",
title = "{HetRCNA}: a Novel Method to Identify Recurrent Copy
Number Alternations from Heterogeneous Tumor Samples
Based on Matrix Decomposition Framework",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "422--434",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2846599",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2846599",
abstract = "A common strategy to discovering cancer associated
copy number aberrations (CNAs) from a cohort of cancer
samples is to detect recurrent CNAs (RCNAs). Although
the previous methods can successfully identify communal
RCNAs shared by nearly all tumor \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2020:MDN,
author = "Ye Liu and Michael K. Ng and Stephen Wu",
title = "Multi-Domain Networks Association for Biological Data
Using Block Signed Graph Clustering",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "435--448",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2848904",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2848904",
abstract = "Multi-domain biological network association and
clustering have attracted a lot of attention in
biological data integration and understanding, which
can provide a more global and accurate understanding of
biological phenomenon. In many problems, different
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Shi:2020:QDD,
author = "Jifan Shi and Juan Zhao and Xiaoping Liu and Luonan
Chen and Tiejun Li",
title = "Quantifying Direct Dependencies in Biological Networks
by Multiscale Association Analysis",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "449--458",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2846648",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2846648",
abstract = "Partial correlation (PC) or conditional mutual
information (CMI) is widely used in detecting direct
dependencies between the observed variables in
biological networks by eliminating indirect
correlations/associations, but it fails whenever there
are some \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Kapoor:2020:GMM,
author = "Rajan Kapoor and Aniruddha Datta and Chao Sima and
Jianping Hua and Rosana Lopes and Michael L. Bittner",
title = "A {Gaussian} Mixture-Model Exploiting Pathway
Knowledge for Dissecting Cancer Heterogeneity",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "459--468",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2869813",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2869813",
abstract = "In this work, we develop a systematic approach for
applying pathway knowledge to a multivariate Gaussian
mixture model for dissecting a heterogeneous cancer
tissue. The downstream transcription factors are
selected as observables from available partial
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lecca:2020:RBM,
author = "Paola Lecca and Angela Re",
title = "A Reaction-Based Model of the State Space of Chemical
Reaction Systems Enables Efficient Simulations",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "469--482",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2894699",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2019.2894699",
abstract = "The choice of the state space representation of a
system can turn into a prominent advantage or burden in
any endeavour to mathematically model dynamical systems
since it entails the analytical tractability of the
related modelling formalism and the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Riesen:2020:AGE,
author = "Kaspar Riesen and Miquel Ferrer and Horst Bunke",
title = "Approximate Graph Edit Distance in Quadratic Time",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "483--494",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2015.2478463",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2015.2478463",
abstract = "Graph edit distance is one of the most flexible and
general graph matching models available. The major
drawback of graph edit distance, however, is its
computational complexity that restricts its
applicability to graphs of rather small size. Recently,
the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lei:2020:AFS,
author = "Xiujuan Lei and Xiaoqin Yang and Fang-Xiang Wu",
title = "Artificial Fish Swarm Optimization Based Method to
Identify Essential Proteins",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "495--505",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2865567",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2865567",
abstract = "It is well known that essential proteins play an
extremely important role in controlling cellular
activities in living organisms. Identifying essential
proteins from protein protein interaction (PPI)
networks is conducive to the understanding of cellular
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sen:2020:AAS,
author = "Rishika Sen and Somnath Tagore and Rajat K. De",
title = "{ASAPP}: Architectural Similarity-Based Automated
Pathway Prediction System and Its Application in
Host-Pathogen Interactions",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "506--515",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2872527",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2872527",
abstract = "The significance of metabolic pathway prediction is to
envision the viable unknown transformations that can
occur provided the appropriate enzymes are present. It
can facilitate the prediction of the consequences of
host-pathogen interactions. In this \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2020:BDF,
author = "Haifen Chen and D. A. K. Maduranga and Piyushkumar A.
Mundra and Jie Zheng",
title = "{Bayesian} Data Fusion of Gene Expression and Histone
Modification Profiles for Inference of Gene Regulatory
Network",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "516--525",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2869590",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2869590",
abstract = "Accurately reconstructing gene regulatory networks
(GRNs) from high-throughput gene expression data has
been a major challenge in systems biology for decades.
Many approaches have been proposed to solve this
problem. However, there is still much room for
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yazdani:2020:BFP,
author = "Hossein Yazdani and Leo L. Cheng and David C.
Christiani and Azam Yazdani",
title = "Bounded Fuzzy Possibilistic Method Reveals Information
about Lung Cancer through Analysis of Metabolomics",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "526--535",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2869757",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2869757",
abstract = "Learning methods, such as conventional clustering and
classification, have been applied in diagnosing
diseases to categorize samples based on their features.
Going beyond clustering samples, membership degrees
represent to what degree each sample belongs \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2020:DNN,
author = "Yujie Li and Heng Huang and Hanbo Chen and Tianming
Liu",
title = "Deep Neural Networks for {{\em In Situ}} Hybridization
Grid Completion and Clustering",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "536--546",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2864262",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2864262",
abstract = "Transcriptome in brain plays a crucial role in
understanding the cortical organization and the
development of brain structure and function. Two
challenges, incomplete data and high dimensionality of
transcriptome, remain unsolved. Here, we present a
novel \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Arabameri:2020:DCC,
author = "Abazar Arabameri and Davud Asemani and Pegah
Teymourpour",
title = "Detection of Colorectal Carcinoma Based on Microbiota
Analysis Using Generalized Regression Neural Networks
and Nonlinear Feature Selection",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "547--557",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2870124",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2870124",
abstract = "To obtain a screening tool for colorectal cancer (CRC)
based on gut microbiota, we seek here to identify an
optimal classifier for CRC detection as well as a novel
nonlinear feature selection method for determining the
most discriminative microbial \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2020:DDF,
author = "Shengping Yang and Mitchell S. Wachtel and Jiangrong
Wu",
title = "{DFseq}: Distribution-Free Method to Detect
Differential Gene Expression for {RNA}-Sequencing
Data",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "558--565",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2866994",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2866994",
abstract = "Many current RNA-sequencing data analysis methods
compare expressions one gene at a time, taking little
consideration of the correlations among genes. In this
study, we propose a method to convert such an
one-dimensional comparison approach into a two-.
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chowdhury:2020:DEA,
author = "Hussain Ahmed Chowdhury and Dhruba Kumar Bhattacharyya
and Jugal Kumar Kalita",
title = "Differential Expression Analysis of {RNA}-seq Reads:
Overview, Taxonomy, and Tools",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "566--586",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2873010",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2873010",
abstract = "Analysis of RNA-sequence (RNA-seq) data is widely used
in transcriptomic studies and it has many applications.
We review RNA-seq data analysis from RNA-seq reads to
the results of differential expression analysis. In
addition, we perform a descriptive \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2020:DLS,
author = "Hang Wang and Jianing Xi and Minghui Wang and Ao Li",
title = "Dual-Layer Strengthened Collaborative Topic Regression
Modeling for Predicting Drug Sensitivity",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "587--598",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2864739",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2864739",
abstract = "An effective way to facilitate the development of
modern oncology precision medicine is the systematical
analysis of the known drug sensitivities that have
emerged in recent years. Meanwhile, the screening of
drug response in cancer cell lines provides an
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2020:EBE,
author = "Lishuang Li and Yang Liu and Meiyue Qin",
title = "Extracting Biomedical Events with Parallel
Multi-Pooling Convolutional Neural Networks",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "599--607",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2868078",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2868078",
abstract = "Biomedical event extraction is important for medical
research and disease prevention, which has attracted
much attention in recent years. Traditionally, most of
the state-of-the-art systems have been based on shallow
machine learning methods, which \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Saribudak:2020:GEH,
author = "Aydin Saribudak and Adarsha A. Subick and Na Hyun Kim
and Joshua A. Rutta and M. {\"U}mit Uyar",
title = "Gene Expressions, Hippocampal Volume Loss, and {MMSE}
Scores in Computation of Progression and Pharmacologic
Therapy Effects for {Alzheimer}'s Disease",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "608--622",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2870363",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2870363",
abstract = "We build personalized relevance parameterization
method (pr-e p-ad) based on artificial intelligence
(ai) techniques to compute Alzheimer's disease (ad)
progression for patients at the mild cognitive
impairment \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xiao:2020:ILM,
author = "Qiu Xiao and Jiawei Luo and Cheng Liang and Guanghui
Li and Jie Cai and Pingjian Ding and Ying Liu",
title = "Identifying {lncRNA} and {mRNA} Co-Expression Modules
from Matched Expression Data in Ovarian Cancer",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "623--634",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2864129",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2864129",
abstract = "Long non-coding RNAs (lncRNAs) have been shown to be
involved in multiple biological processes and play
critical roles in tumorigenesis. Numerous lncRNAs have
been discovered in diverse species, but the functions
of most lncRNAs still remain unclear. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Karimnezhad:2020:IPK,
author = "Ali Karimnezhad and David R. Bickel",
title = "Incorporating Prior Knowledge about Genetic Variants
into the Analysis of Genetic Association Data: an
Empirical {Bayes} Approach",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "635--646",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2865420",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2865420",
abstract = "In a genome-wide association study (GWAS), the
probability that a single nucleotide polymorphism (SNP)
is not associated with a disease is its local false
discovery rate (LFDR). The LFDR for each SNP is
relative to a reference class of SNPs. For example,
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2020:ISO,
author = "Rongrong Zhang and Ming Hu and Yu Zhu and Zhaohui Qin
and Ke Deng and Jun S. Liu",
title = "Inferring Spatial Organization of Individual
Topologically Associated Domains via Piecewise Helical
Model",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "647--656",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2865349",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2865349",
abstract = "The recently developed Hi-C technology enables a
genome-wide view of chromosome spatial organizations,
and has shed deep insights into genome structure and
genome function. However, multiple sources of
uncertainties make downstream data analysis and
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cao:2020:PEF,
author = "Zhen Cao and Shihua Zhang",
title = "Probe Efficient Feature Representation of Gapped
{$K$}-mer Frequency Vectors from Sequences Using Deep
Neural Networks",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "657--667",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2868071",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2868071",
abstract = "Gapped k-mers frequency vectors (gkm-fv) has been
presented for extracting sequence features. Coupled
with support vector machine (gkm-SVM), gkm-fvs have
been used to achieve effective sequence-based
predictions. However, the huge computation of a large
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Fergus:2020:UDL,
author = "Paul Fergus and Casimiro Curbelo Monta{\~n}ez and
Basma Abdulaimma and Paulo Lisboa and Carl Chalmers and
Beth Pineles",
title = "Utilizing Deep Learning and Genome Wide Association
Studies for Epistatic-Driven Preterm Birth
Classification in {African--American} Women",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "668--678",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2868667",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2868667",
abstract = "Genome-Wide Association Studies (GWAS) are used to
identify statistically significant genetic variants in
case-control studies. The main objective is to find
single nucleotide polymorphisms (SNPs) that influence a
particular phenotype (i.e., disease trait). \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2020:WSC,
author = "Qinhu Zhang and Lin Zhu and Wenzheng Bao and De-Shuang
Huang",
title = "Weakly-Supervised Convolutional Neural Network
Architecture for Predicting Protein--{DNA} Binding",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "679--689",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2864203",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2864203",
abstract = "Although convolutional neural networks (CNN) have
outperformed conventional methods in predicting the
sequence specificities of protein-DNA binding in recent
years, they do not take full advantage of the intrinsic
weakly-supervised information of DNA \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Mallik:2020:WWC,
author = "Saurav Mallik and Sanghamitra Bandyopadhyay",
title = "{WeCoMXP}: Weighted Connectivity Measure Integrating
Co-Methylation, Co-Expression and Protein-Protein
Interactions for Gene-Module Detection",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "690--703",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2868348",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2018.2868348",
abstract = "The identification of modules (groups of several
tightly interconnected genes) in gene interaction
network is an essential task for better understanding
of the architecture of the whole network. In this
article, we develop a novel weighted connectivity
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Fodeh:2020:CPC,
author = "Samah J. Fodeh and Taihua Li and Haya Jarad and Basmah
Safdar",
title = "Classification of Patients with Coronary Microvascular
Dysfunction",
journal = j-TCBB,
volume = "17",
number = "2",
pages = "704--711",
month = mar,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2914442",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Jun 10 07:29:49 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/abs/10.1109/TCBB.2019.2914442",
abstract = "While coronary microvascular dysfunction (CMD) is a
major cause of ischemia, it is very challenging to
diagnose due to lack of CMD-specific screening
measures. CMD has been identified as one of the five
priority areas of investigation in a 2014 National
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zheng:2020:GEI,
author = "Jie Zheng and Jinyan Li and Yun Zheng",
title = "Guest Editorial for the {29th International Conference
on Genome Informatics (GIW 2018)}",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "726--727",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2978606",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2978606",
abstract = "The six papers in this special section were presented
at the 29th International Conference on Genome
Informatics (GIW 2018) that was held at Kunming
University of Science and Technology, Kunming, China on
December 3-5, 2018.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liao:2020:ETA,
author = "Xingyu Liao and Min Li and You Zou and Fang-Xiang Wu
and Yi Pan and Jianxin Wang",
title = "An Efficient Trimming Algorithm based on Multi-Feature
Fusion Scoring Model for {NGS} Data",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "728--738",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2897558",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2897558",
abstract = "Next-generation sequencing (NGS) has enabled an
exponential growth rate of sequencing data. However,
several sequence artifacts, including error reads (base
calling errors and small insertions or deletions) and
poor quality reads, which can impose \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2020:GDI,
author = "Shunfang Wang and Zicheng Cao and Mingyuan Li and
Yaoting Yue",
title = "{G-DipC}: an Improved Feature Representation Method
for Short Sequences to Predict the Type of Cargo in
Cell-Penetrating Peptides",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "739--747",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2930993",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2930993",
abstract = "Cell-penetrating peptides (CPPs) are functional short
peptides with high carrying capacity. CPP sequences
with targeting functions for the highly efficient
delivery of drugs to target cells. In this paper, which
is focused on the prediction of the cargo \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2020:SSS,
author = "Yong Liu and Min Wu and Chenghao Liu and Xiao-Li Li
and Jie Zheng",
title = "{SL$^2$MF}: Predicting Synthetic Lethality in Human
Cancers via Logistic Matrix Factorization",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "748--757",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2909908",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2909908",
abstract = "Synthetic lethality (SL) is a promising concept for
novel discovery of anti-cancer drug targets. However,
wet-lab experiments for detecting SLs are faced with
various challenges, such as high cost, low consistency
across platforms, or cell lines. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Song:2020:EBM,
author = "Junrong Song and Wei Peng and Feng Wang",
title = "An Entropy-Based Method for Identifying Mutual
Exclusive Driver Genes in Cancer",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "758--768",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2897931",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2897931",
abstract = "Cancer in essence is a complex genomic alteration
disease which is caused by the somatic mutations during
the lifetime. According to previous researches, the
first step to overcome cancer is to identify driver
genes which can promote carcinogenesis. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Peng:2020:MRA,
author = "Jiajie Peng and Linjiao Zhu and Yadong Wang and Jin
Chen",
title = "Mining Relationships among Multiple Entities in
Biological Networks",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "769--776",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2904965",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2904965",
abstract = "Identifying topological relationships among multiple
entities in biological networks is critical towards the
understanding of the organizational principles of
network functionality. Theoretically, this problem can
be solved using minimum Steiner tree \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yao:2020:ADP,
author = "Heng Yao and Yunjia Shi and Jihong Guan and Shuigeng
Zhou",
title = "Accurately Detecting Protein Complexes by Graph
Embedding and Combining Functions with Interactions",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "777--787",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2897769",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2897769",
abstract = "Identifying protein complexes is helpful for
understanding cellular functions and designing drugs.
In the last decades, many computational methods have
been proposed based on detecting dense subgraphs or
subnetworks in Protein-Protein Interaction Networks
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ma:2020:CIH,
author = "Yuanyuan Ma and Xiaohua Hu and Tingting He and
Xingpeng Jiang",
title = "Clustering and Integrating of Heterogeneous Microbiome
Data by Joint Symmetric Nonnegative Matrix
Factorization with {Laplacian} Regularization",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "788--795",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2756628",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2017.2756628",
abstract = "Many datasets that exists in the real world are often
comprised of different representations or views which
provide complementary information to each other. To
integrate information from multiple views, data
integration approaches such as nonnegative \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Tan:2020:EEH,
author = "Renjie Tan and Jixuan Wang and Xiaoliang Wu and Liran
Juan and Tianjiao Zhang and Rui Ma and Qing Zhan and
Tao Wang and Shuilin Jin and Qinghua Jiang and Yadong
Wang",
title = "{ERDS-Exome}: a Hybrid Approach for Copy Number
Variant Detection from Whole-Exome Sequencing Data",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "796--803",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2758779",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2017.2758779",
abstract = "Copy number variants (CNVs) play important roles in
human disease and evolution. With the rapid development
of next-generation sequencing technologies, many tools
have been developed for inferring CNVs based on
whole-exome sequencing (WES) data. However, \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Peng:2020:HSC,
author = "Shaoliang Peng and Xiaoyu Zhang and Wenhe Su and Dong
Dong and Yutong Lu and Xiangke Liao and Kai Lu and
Canqun Yang and Jie Liu and Weiliang Zhu and Dongqing
Wei",
title = "High-Scalable Collaborated Parallel Framework for
Large-Scale Molecular Dynamic Simulation on {Tianhe-2}
Supercomputer",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "804--816",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2805709",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/super.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2805709",
abstract = "Molecular dynamics (MD) is a computer simulation
method of studying physical movements of atoms and
molecules that provide detailed microscopic sampling on
molecular scale. With the continuous efforts and
improvements, MD simulation gained popularity in
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2020:IPC,
author = "Min Li and Xiangmao Meng and Ruiqing Zheng and
Fang-Xiang Wu and Yaohang Li and Yi Pan and Jianxin
Wang",
title = "Identification of Protein Complexes by Using a Spatial
and Temporal Active Protein Interaction Network",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "817--827",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2749571",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2017.2749571",
abstract = "The rapid development of proteomics and
high-throughput technologies has produced a large
amount of Protein-Protein Interaction (PPI) data, which
makes it possible for considering dynamic properties of
protein interaction networks (PINs) instead of static
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Rahman:2020:PPM,
author = "Mohammad Arifur Rahman and Nathan LaPierre and Huzefa
Rangwala",
title = "Phenotype Prediction from Metagenomic Data Using
Clustering and Assembly with Multiple Instance Learning
{(CAMIL)}",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "828--840",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2758782",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2017.2758782",
abstract = "The recent advent of Metagenome Wide Association
Studies (MGWAS) provides insight into the role of
microbes on human health and disease. However, the
studies present several computational challenges. In
this paper, we demonstrate a novel, efficient, and
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2020:ILM,
author = "Lishuang Li and Yuxin Jiang",
title = "Integrating Language Model and Reading Control Gate in
{BLSTM-CRF} for Biomedical Named Entity Recognition",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "841--846",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2868346",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2868346",
abstract = "Biomedical named entity recognition (Bio-NER) is an
important preliminary step for many biomedical text
mining tasks. The current mainstream methods for NER
are based on the neural networks to avoid the complex
hand-designed features derived from various \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wu:2020:MME,
author = "Binbin Wu and Min Li and Xingyu Liao and Junwei Luo
and Fang-Xiang Wu and Yi Pan and Jianxin Wang",
title = "{MEC}: Misassembly Error Correction in Contigs based
on Distribution of Paired-End Reads and Statistics of
{GC-contents}",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "847--857",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2876855",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2876855",
abstract = "The de novo assembly tools aim at reconstructing
genomes from next-generation sequencing (NGS) data.
However, the assembly tools usually generate a large
amount of contigs containing many misassemblies, which
are caused by problems of repetitive regions,
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zheng:2020:IIC,
author = "Huiru Zheng and Haiying Wang and Richard J. Dewhurst
and Rainer Roehe",
title = "Improving the Inference of Co-Occurrence Networks in
the Bovine Rumen Microbiome",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "858--867",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2879342",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2879342",
abstract = "The importance of the composition and signature of
rumen microbial communities has gained increasing
attention. One of the key techniques was to infer
co-abundance networks through correlation analysis
based on relative abundances. While substantial
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zare:2020:PSC,
author = "Fatima Zare and Sardar Ansari and Kayvan Najarian and
Sheida Nabavi",
title = "Preprocessing Sequence Coverage Data for More Precise
Detection of Copy Number Variations",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "868--876",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2869738",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2869738",
abstract = "Copy number variation (CNV) is a type of
genomic/genetic variation that plays an important role
in phenotypic diversity, evolution, and disease
susceptibility. Next generation sequencing (NGS)
technologies have created an opportunity for more
accurate \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Luo:2020:GGF,
author = "Junwei Luo and Jianxin Wang and Juan Shang and Huimin
Luo and Min Li and Fang-Xiang Wu and Yi Pan",
title = "{GapReduce}: a Gap Filling Algorithm Based on
Partitioned Read Sets",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "877--886",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2789909",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2789909",
abstract = "With the advances in technologies of sequencing and
assembly, draft sequences of more and more genomes are
available. However, there commonly exist gaps in these
draft sequences which influence various downstream
analysis of biological studies. Gap \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Hu:2020:DRL,
author = "Haigen Hu and Qiu Guan and Shengyong Chen and Zhiwei
Ji and Yao Lin",
title = "Detection and Recognition for Life State of Cell
Cancer Using Two-Stage Cascade {CNNs}",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "887--898",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2780842",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2017.2780842",
abstract = "Cancer cell detection and its stages recognition of
life cycle are an important step to analyze cellular
dynamics in the automation of cell based-experiments.
In this work, a two-stage hierarchical method is
proposed to detect and recognize different life
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2020:BLE,
author = "Yuansheng Liu and Chaowang Lan and Michael Blumenstein
and Jinyan Li",
title = "Bi-Level Error Correction for {PacBio} Long Reads",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "899--905",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2017.2780832",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2017.2780832",
abstract = "The latest sequencing technologies such as the Pacific
Biosciences (PacBio) and Oxford Nanopore machines can
generate long reads at the length of thousands of
nucleic bases which is much longer than the reads at
the length of hundreds generated by \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ni:2020:CDS,
author = "Peng Ni and Jianxin Wang and Ping Zhong and Yaohang Li
and Fang-Xiang Wu and Yi Pan",
title = "Constructing Disease Similarity Networks Based on
Disease Module Theory",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "906--915",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2817624",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2817624",
abstract = "Quantifying the associations between diseases is now
playing an important role in modern biology and
medicine. Actually discovering associations between
diseases could help us gain deeper insights into
pathogenic mechanisms of complex diseases, thus could
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2020:FEC,
author = "Zhi-Zhong Chen and Youta Harada and Yuna Nakamura and
Lusheng Wang",
title = "Faster Exact Computation of {rSPR} Distance via Better
Approximation",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "916--929",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2878731",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2878731",
abstract = "Due to hybridization events in evolution, studying two
different genes of a set of species may yield two
related but different phylogenetic trees for the set of
species. In this case, we want to measure the
dissimilarity of the two trees. The rooted \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{He:2020:IAC,
author = "Zaobo He and Jiguo Yu and Ji Li and Qilong Han and
Guangchun Luo and Yingshu Li",
title = "Inference Attacks and Controls on Genotypes and
Phenotypes for Individual Genomic Data",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "930--937",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2810180",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2810180",
abstract = "The rapid growth of DNA-sequencing technologies
motivates more personalized and predictive
genetic-oriented services, which further attract
individuals to increasingly release their genome
information to learn about personalized medicines,
disease \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2020:INF,
author = "Jin Zhao and Haodi Feng and Daming Zhu and Chi Zhang
and Ying Xu",
title = "{IsoTree}: a New Framework for de novo Transcriptome
Assembly from {RNA-seq} Reads",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "938--948",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2808350",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2808350",
abstract = "High-throughput sequencing of mRNA has made the deep
and efficient probing of transcriptome more affordable.
However, the vast amounts of short RNA-seq reads make
de novo transcriptome assembly an algorithmic
challenge. In this work, we present IsoTree, a
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2020:EMM,
author = "Jingsong Zhang and Jianmei Guo and Ming Zhang and
Xiangtian Yu and Xiaoqing Yu and Weifeng Guo and Tao
Zeng and Luonan Chen",
title = "Efficient Mining Multi-Mers in a Variety of Biological
Sequences",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "949--958",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2828313",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2828313",
abstract = "Counting the occurrence frequency of each k-mer in a
biological sequence is a preliminary yet important step
in many bioinformatics applications. However, most
k-mer counting algorithms rely on a given k to produce
single-length k-mers, which is \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Boukari:2020:ACT,
author = "Fatima Boukari and Sokratis Makrogiannis",
title = "Automated Cell Tracking Using Motion Prediction-Based
Matching and Event Handling",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "959--971",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2875684",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2875684",
abstract = "Automated cell segmentation and tracking enables the
quantification of static and dynamic cell
characteristics and is significant for disease
diagnosis, treatment, drug development, and other
biomedical applications. This paper introduces a method
for \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2020:CHS,
author = "Lei Wang and Zhu-Hong You and De-Shuang Huang and
Fengfeng Zhou",
title = "Combining High Speed {ELM} Learning with a Deep
Convolutional Neural Network Feature Encoding for
Predicting {Protein-RNA} Interactions",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "972--980",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2874267",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2874267",
abstract = "Emerging evidence has shown that RNA plays a crucial
role in many cellular processes, and their biological
functions are primarily achieved by binding with a
variety of proteins. High-throughput biological
experiments provide a lot of valuable information
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Kang:2020:CGW,
author = "Qiwen Kang and Neil Moore and Christopher L. Schardl
and Ruriko Yoshida",
title = "{CURatio}: Genome-Wide Phylogenomic Analysis Method
Using Ratios of Total Branch Lengths",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "981--989",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2878564",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2878564",
abstract = "Evolutionary hypotheses provide important
underpinnings of biological and medical sciences, and
comprehensive, genome-wide understanding of
evolutionary relationships among organisms are needed
to test and refine such hypotheses. Theory and
empirical \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Som-In:2020:EPS,
author = "Sarawoot Som-In and Warangkhana Kimpan",
title = "Enhancing of Particle Swarm Optimization Based Method
for Multiple Motifs Detection in {DNA} Sequences
Collections",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "990--998",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2872978",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2872978",
abstract = "Genome sequence data consists of DNA sequences or
input sequences. Each one includes nucleotides with
chemical structures presented as characters: `A', `C',
'G', and `T', and groups of motif sequences, called
Transcription Factor Binding Sites (TFBSs), \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bakhteh:2020:IMS,
author = "Somayeh Bakhteh and Alireza Ghaffari-Hadigheh and
Nader Chaparzadeh",
title = "Identification of Minimum Set of Master Regulatory
Genes in Gene Regulatory Networks",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "999--1009",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2875692",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2875692",
abstract = "Identification of master regulatory genes is one of
the primary challenges in systems biology. The minimum
dominating set problem is a powerful paradigm in
analyzing such complex networks. In these models, genes
stand as nodes and their interactions are \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Vundavilli:2020:SDE,
author = "Haswanth Vundavilli and Aniruddha Datta and Chao Sima
and Jianping Hua and Rosana Lopes and Michael Bittner",
title = "In Silico Design and Experimental Validation of
Combination Therapy for Pancreatic Cancer",
journal = j-TCBB,
volume = "17",
number = "3",
pages = "1010--1018",
month = may,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2018.2872573",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:32 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2018.2872573",
abstract = "The number of deaths associated with Pancreatic Cancer
has been on the rise in the United States making it an
especially dreaded disease. The overall prognosis for
pancreatic cancer patients continues to be grim because
of the complexity of the disease at \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Huang:2020:GES,
author = "De-Shuang Huang and Vitoantonio Bevilacqua and Michael
Gromiha",
title = "Guest Editorial for Special Section on the {14th
International Conference on Intelligent Computing
(ICIC)}",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1474--1475",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2989800",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2989800",
abstract = "The papers in this special section were presented at
the Fourteenth International Conference on Intelligent
Computing (ICIC) held in Wuhan, China, on August 15-18,
2018.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lee:2020:CPP,
author = "Wook Lee and Kyungsook Han",
title = "Constructive Prediction of Potential {RNA} Aptamers
for a Protein Target",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1476--1482",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2951114",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2951114",
abstract = "Aptamers are short single-stranded nucleic acids that
bind to target molecules with high affinity and
selectivity. Aptamers are generally identified in vitro
by performing SELEX (systematic evolution of ligands by
exponential enrichment). Complementing \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Shen:2020:CNP,
author = "Zhen Shen and Su-Ping Deng and De-Shuang Huang",
title = "Capsule Network for Predicting {RNA}--Protein Binding
Preferences Using Hybrid Feature",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1483--1492",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2943465",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2943465",
abstract = "RNA-Protein binding is involved in many different
biological processes. With the progress of technology,
more and more data are available for research. Based on
these data, many prediction methods have been proposed
to predict RNA-Protein binding \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jia:2020:PIM,
author = "Huiqiang Jia and Haichao Wei and Daming Zhu and
Jingjing Ma and Hai Yang and Ruizhi Wang and Xianzhong
Feng",
title = "{PASA}: Identifying More Credible Structural Variants
of {Hedou12}",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1493--1503",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2934463",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2934463",
abstract = "Although plenty of structural variant detecting
approaches for human genomes can be looked up in the
literatures, little has been acknowledged on the
effectiveness of those structural variant softwares for
plant genomes. Moreover, it has been demonstrated
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2020:JIV,
author = "Jian Liu and Zhi Qu and Mo Yang and Jialiang Sun and
Shuhui Su and Lei Zhang",
title = "Jointly Integrating {VCF}-Based Variants and
{OWL}-Based Biomedical Ontologies in {MongoDB}",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1504--1515",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2951137",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2951137",
abstract = "The development of the next-generation sequencing
(NGS) technologies has led to massive amounts of VCF
(Variant Call Format) files, which have been the
standard formats developed with 1000 Genomes Project.
At the same time, with the widespread use of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Hu:2020:LMN,
author = "Pengwei Hu and Yu-An Huang and Keith C. C. Chan and
Zhu-Hong You",
title = "Learning Multimodal Networks From Heterogeneous Data
for Prediction of {lncRNA--miRNA} Interactions",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1516--1524",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2957094",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2957094",
abstract = "Long noncoding RNAs (lncRNAs) is an important class of
non-protein coding RNAs. They have recently been found
to potentially be able to act as a regulatory molecule
in some important biological processes. MicroRNAs
(miRNAs) have been confirmed to be \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lin:2020:ECH,
author = "Xiaoli Lin and Xiaolong Zhang and Xin Xu",
title = "Efficient Classification of Hot Spots and Hub Protein
Interfaces by Recursive Feature Elimination and
Gradient Boosting",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1525--1534",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2931717",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2931717",
abstract = "Proteins are not isolated biological molecules, which
have the specific three-dimensional structures and
interact with other proteins to perform functions. A
small number of residues (hot spots) in protein-protein
interactions (PPIs) play the vital role \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Hind:2020:NAD,
author = "Jade Hind and Paulo Lisboa and Abir J. Hussain and
Dhiya Al-Jumeily",
title = "A Novel Approach to Detecting Epistasis using Random
Sampling Regularisation",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1535--1545",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2948330",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2948330",
abstract = "Epistasis is a progressive approach that complements
the `common disease, common variant' hypothesis that
highlights the potential for connected networks of
genetic variants collaborating to produce a phenotypic
expression. Epistasis is commonly performed \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2020:UWE,
author = "Jianqiang Li and Xiaofeng Shi and Zhu-Hong You and
Hai-Cheng Yi and Zhuangzhuang Chen and Qiuzhen Lin and
Min Fang",
title = "Using Weighted Extreme Learning Machine Combined With
Scale-Invariant Feature Transform to Predict
Protein-Protein Interactions From Protein Evolutionary
Information",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1546--1554",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2965919",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2965919",
abstract = "Protein-Protein Interactions (PPIs) play an
irreplaceable role in biological activities of
organisms. Although many high-throughput methods are
used to identify PPIs from different kinds of
organisms, they have some shortcomings, such as high
cost and \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Shahdoust:2020:NBC,
author = "Maryam Shahdoust and Hossein Mahjub and Hamid Pezeshk
and Mehdi Sadeghi",
title = "A Network-Based Comparison Between Molecular Apocrine
Breast Cancer Tumor and Basal and Luminal Tumors by
Joint Graphical Lasso",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1555--1562",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2911074",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2911074",
abstract = "Joint graphical lasso (JGL) approach is a Gaussian
graphical model to estimate multiple graphical models
corresponding to distinct but related groups. Molecular
apocrine (MA) breast cancer tumor has similar
characteristics to luminal and basal subtypes.
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Guo:2020:NEE,
author = "Qian Guo and Tianhong Pan and Shan Chen and Xiaobo Zou
and Dorothy Yu Huang",
title = "A Novel Edge Effect Detection Method for Real-Time
Cellular Analyzer Using Functional Principal Component
Analysis",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1563--1572",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2903094",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2903094",
abstract = "Real-time cellular analyzer (RTCA) has been generally
applied to test the cytotoxicity of chemicals. However,
several factors impact the experimental quality. A
non-negligible factor is the abnormal time-dependent
cellular response curves (TCRCs) of the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sharma:2020:ESL,
author = "Nirmala Sharma and Harish Sharma and Ajay Sharma",
title = "An Effective Solution for Large Scale Single Machine
Total Weighted Tardiness Problem using Lunar Cycle
Inspired Artificial Bee Colony Algorithm",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1573--1581",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2897302",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2897302",
abstract = "Single machine total weighted tardiness problem
(SMTWTP) is one of the fundamental combinatorial
optimization problems. The problem consists of a set of
independent jobs with distinct processing times,
weights, and due dates to be scheduled on a single
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sarkar:2020:EAI,
author = "Aisharjya Sarkar and Yilmaz Atay and Alana Lorraine
Erickson and Ivan Arisi and Cesare Saltini and Tamer
Kahveci",
title = "An Efficient Algorithm for Identifying Mutated
Subnetworks Associated with Survival in Cancer",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1582--1594",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2911069",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2911069",
abstract = "Protein-protein interaction (PPI) network models
interconnections between protein-encoding genes. A
group of proteins that perform similar functions are
often connected to each other in the PPI network. The
corresponding genes form pathways or functional
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yan:2020:BPM,
author = "Cheng Yan and Guihua Duan and Fang-Xiang Wu and Yi Pan
and Jianxin Wang",
title = "{BRWMDA:Predicting} Microbe-Disease Associations Based
on Similarities and Bi-Random Walk on Disease and
Microbe Networks",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1595--1604",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2907626",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2907626",
abstract = "Many current studies have evidenced that microbes play
important roles in human diseases. Therefore,
discovering the associations between microbes and
diseases is beneficial to systematically understanding
the mechanisms of diseases, diagnosing, and \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Peng:2020:CNB,
author = "Chen Peng and Yang Zheng and De-Shuang Huang",
title = "Capsule Network Based Modeling of Multi-omics Data for
Discovery of Breast Cancer-Related Genes",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1605--1612",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2909905",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2909905",
abstract = "Breast cancer is one of the most common cancers all
over the world, which bring about more than 450,000
deaths each year. Although this malignancy has been
extensively studied by a large number of researchers,
its prognosis is still poor. Since \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yeganeh:2020:CDA,
author = "Pourya Naderi Yeganeh and M. Taghi Mostafavi",
title = "Causal Disturbance Analysis: a Novel Graph Centrality
Based Method for Pathway Enrichment Analysis",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1613--1624",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2907246",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2907246",
abstract = "Pathway enrichment analysis models (PEM) are the
premier methods for interpreting gene expression
profiles from high-throughput experiments. PEM often
use a priori background knowledge to infer the
underlying biological functions and mechanisms. A
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Filipovic:2020:CNM,
author = "Jir{\'\i} Filipovic and Ondrej V{\'a}vra and Jan
Plh{\'a}k and David Bedn{\'a}r and S{\'e}rgio M.
Marques and Jan Brezovsk{\'y} and Ludek Matyska and
Jir{\'\i} Damborsk{\'y}",
title = "{CaverDock}: a Novel Method for the Fast Analysis of
Ligand Transport",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1625--1638",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2907492",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2907492",
abstract = "Here we present a novel method for the analysis of
transport processes in proteins and its implementation
called CaverDock. Our method is based on a modified
molecular docking algorithm. It iteratively places the
ligand along the access tunnel in such a \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zeng:2020:DCF,
author = "Xiangxiang Zeng and Yinglai Lin and Yuying He and
Linyuan L{\"u} and Xiaoping Min and Alfonso
Rodr{\'\i}guez-Pat{\'o}n",
title = "Deep Collaborative Filtering for Prediction of Disease
Genes",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1639--1647",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2907536",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2907536",
abstract = "Accurate prioritization of potential disease genes is
a fundamental challenge in biomedical research. Various
algorithms have been developed to solve such problems.
Inductive Matrix Completion (IMC) is one of the most
reliable models for its well-. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ranjan:2020:DRF,
author = "Ashish Ranjan and Md Shah Fahad and David
Fern{\'a}ndez-Baca and Akshay Deepak and Sudhakar
Tripathi",
title = "Deep Robust Framework for Protein Function Prediction
Using Variable-Length Protein Sequences",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1648--1659",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2911609",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2911609",
abstract = "The order of amino acids in a protein sequence enables
the protein to acquire a conformation suitable for
performing functions, thereby motivating the need to
analyze these sequences for predicting functions.
Although machine learning based approaches are
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sharma:2020:ISE,
author = "Sunildatt Sharma and Sanjeev Narayan Sharma and Rajiv
Saxena",
title = "Identification of Short Exons Disunited by a Short
Intron in Eukaryotic {DNA} Regions",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1660--1670",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2900040",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2900040",
abstract = "Weak codon bias in short exons and separation by a
short intron induces difficulty in extracting period-3
component that marks the presence of exonic regions.
The annotation task of such short exons has been
addressed in the proposed model independent \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2020:IIE,
author = "Min Wang and Ting-Zhu Huang and Jian Fang and Vince D.
Calhoun and Yu-Ping Wang",
title = "Integration of Imaging (epi)Genomics Data for the
Study of Schizophrenia Using Group Sparse Joint
Nonnegative Matrix Factorization",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1671--1681",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2899568",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2899568",
abstract = "Schizophrenia (SZ) is a complex disease. Single
nucleotide polymorphism (SNP), brain activity measured
by functional magnetic resonance imaging (fMRI) and DNA
methylation are all important biomarkers that can be
used for the study of SZ. To our knowledge, \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2020:MNM,
author = "Lan Zhao and Hong Yan",
title = "{MCNF}: a Novel Method for Cancer Subtyping by
Integrating Multi-Omics and Clinical Data",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1682--1690",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2910515",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2910515",
abstract = "In the age of personalized medicine, there is a great
need to classify cancer (from the same organ site) into
homogeneous subtypes. Recent technology advancements in
genome-wide molecular profiling have made it possible
to profiling multiple molecular \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Pichene:2020:MVP,
author = "Matthieu Pichen{\'e} and Sucheendra K. Palaniappan and
Eric Fabre and Blaise Genest",
title = "Modeling Variability in Populations of Cells Using
Approximated Multivariate Distributions",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1691--1702",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2904276",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2904276",
abstract = "We are interested in studying the evolution of large
homogeneous populations of cells, where each cell is
assumed to be composed of a group of biological players
(species) whose dynamics is governed by a complex
biological pathway, identical for all \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2020:ODP,
author = "Yuan Zhang and Haihong Liu and Zhouhong Li and
Zhonghua Miao and Jin Zhou",
title = "Oscillatory Dynamics of {p53-Mdm2} Circuit in Response
to {DNA} Damage Caused by Ionizing Radiation",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1703--1713",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2899574",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2899574",
abstract = "Although the dynamical behavior of the p53-Mdm2 loop
has been extensively studied, the understanding of the
mechanism underlying the regulation of this pathway
still remains limited. Herein, we developed an
integrated model with five basic components and
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wu:2020:PNC,
author = "Sijia Wu and Xiaoming Wu and Jie Tian and Xiaobo Zhou
and Liyu Huang",
title = "{PredictFP2}: a New Computational Model to Predict
Fusion Peptide Domain in All Retroviruses",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1714--1720",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2898943",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2898943",
abstract = "Fusion peptide (FP) is a pivotal domain for the entry
of retrovirus into host cells to continue
self-replication. The crucial role indicates that FP is
a promising drug target for therapeutic intervention. A
FP model proposed in our previous work is \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Fu:2020:PDM,
author = "Laiyi Fu and Qinke Peng and Ling Chai",
title = "Predicting {DNA} Methylation States with Hybrid
Information Based Deep-Learning Model",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1721--1728",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2909237",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2909237",
abstract = "DNA methylation plays an important role in the
regulation of some biological processes. Up to now,
with the development of machine learning models, there
are several sequence-based deep learning models
designed to predict DNA methylation states, which
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Paul:2020:IRS,
author = "Sushmita Paul and Madhumita",
title = "{RFCM$^3$}: Computational Method for Identification of
{miRNA--mRNA} Regulatory Modules in Cervical Cancer",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1729--1740",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2910851",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2910851",
abstract = "Cervical cancer is a leading severe malignancy
throughout the world. Molecular processes and
biomarkers leading to tumor progression in cervical
cancer are either unknown or only partially understood.
An increasing number of studies have shown that
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Shen:2020:RPB,
author = "Zhen Shen and Su-Ping Deng and De-Shuang Huang",
title = "{RNA-Protein} Binding Sites Prediction via Multi Scale
Convolutional Gated Recurrent Unit Networks",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1741--1750",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2910513",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2910513",
abstract = "RNA-Protein binding plays important roles in the field
of gene expression. With the development of high
throughput sequencing, several conventional methods and
deep learning-based methods have been proposed to
predict the binding preference of RNA-protein
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Raza:2020:SPF,
author = "Saad Raza and Ghulam Abbas and Syed Sikander Azam",
title = "Screening Pipeline for Flavivirus Based Inhibitors for
{Zika} Virus {NS1}",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1751--1761",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2911081",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2911081",
abstract = "In-silico pipeline is applied for identifying and
designing novel inhibitors against ZIKV NS1 protein.
Comparative molecular docking studies are performed to
explore the binding of structurally diverse compounds
to ZIKV NS1 by AutoDock/Vina and GOLD. The \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Kawano:2020:SFB,
author = "Keisuke Kawano and Satoshi Koide and Chie Imamura",
title = "{Seq2seq} Fingerprint with Byte-Pair Encoding for
Predicting Changes in Protein Stability upon Single
Point Mutation",
journal = j-TCBB,
volume = "17",
number = "5",
pages = "1762--1772",
month = sep,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2908641",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:34 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2908641",
abstract = "The engineering of stable proteins is crucial for
various industrial purposes. Several machine learning
methods have been developed to predict changes in the
stability of proteins corresponding to single point
mutations. To improve the prediction accuracy,.
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yan:2020:GES,
author = "Da Yan and Xin Gao and Samah J. Fodeh and Jake Y.
Chen",
title = "Guest Editorial for Selected Papers from {BIOKDD 2018}
and {DMBIH 2018}",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "1832--1834",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3020443",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3020443",
abstract = "The papers in this special issue were presented at the
2018 17th International Workshop on Data Mining in
Bioinformatics (BIOKDD), held in conjunction with the
ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining. The Workshop was \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sokolovsky:2020:DLA,
author = "Michael Sokolovsky and Francisco Guerrero and Sarun
Paisarnsrisomsuk and Carolina Ruiz and Sergio A.
Alvarez",
title = "Deep Learning for Automated Feature Discovery and
Classification of Sleep Stages",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "1835--1845",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2912955",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2912955",
abstract = "Convolutional neural networks (CNN) have demonstrated
state-of-the-art classification results in image
categorization, but have received comparatively little
attention for classification of one-dimensional
physiological signals. We design a deep CNN \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{McDermott:2020:DLB,
author = "Matthew B. A. McDermott and Jennifer Wang and Wen-Ning
Zhao and Steven D. Sheridan and Peter Szolovits and
Isaac Kohane and Stephen J. Haggarty and Roy H.
Perlis",
title = "Deep Learning Benchmarks on L1000 Gene Expression
Data",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "1846--1857",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2910061",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2910061",
abstract = "Gene expression data can offer deep, physiological
insights beyond the static coding of the genome alone.
We believe that realizing this potential requires
specialized, high-capacity machine learning methods
capable of using underlying biological \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zheng:2020:DDA,
author = "Jingyi Zheng and Fushing Hsieh and Linqiang Ge",
title = "A Data-Driven Approach to Predict and Classify
Epileptic Seizures from Brain-Wide Calcium Imaging
Video Data",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "1858--1870",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2895077",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2895077",
abstract = "The prediction of epileptic seizures has been an
essential problem of epilepsy study. The calcium
imaging video data images the whole brain-wide neurons
activities with electrical discharge recorded by
calcium fluorescence intensity (CFI). In this paper,
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xu:2020:CCP,
author = "Hongming Xu and Sunho Park and Tae Hyun Hwang",
title = "Computerized Classification of Prostate Cancer
{Gleason} Scores from Whole Slide Images",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "1871--1882",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2941195",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2941195",
abstract = "Histological Gleason grading of tumor patterns is one
of the most powerful prognostic predictors in prostate
cancer. However, manual analysis and grading performed
by pathologists are typically subjective and
time-consuming. In this paper, we present an \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Pathak:2020:PSR,
author = "Shreyasi Pathak and Jorit van Rossen and Onno
Vijlbrief and Jeroen Geerdink and Christin Seifert and
Maurice van Keulen",
title = "Post-Structuring Radiology Reports of Breast Cancer
Patients for Clinical Quality Assurance",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "1883--1894",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2914678",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2914678",
abstract = "Hospitals often set protocols based on well defined
standards to maintain the quality of patient reports.
To ensure that the clinicians conform to the protocols,
quality assurance of these reports is needed. Patient
reports are currently written in free-. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Noriega-Atala:2020:EIS,
author = "Enrique Noriega-Atala and Paul D. Hein and Shraddha S.
Thumsi and Zechy Wong and Xia Wang and Sean M. Hendryx
and Clayton T. Morrison",
title = "Extracting Inter-Sentence Relations for Associating
Biological Context with Events in Biomedical Texts",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "1895--1906",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2904231",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2904231",
abstract = "We present an analysis of the problem of identifying
biological context and associating it with biochemical
events described in biomedical texts. This constitutes
a non-trivial, inter-sentential relation extraction
task. We focus on biological context as \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gibbs:2020:AVS,
author = "Jonathon A. Gibbs and Michael P. Pound and Andrew P.
French and Darren M. Wells and Erik H. Murchie and Tony
P. Pridmore",
title = "Active Vision and Surface Reconstruction for {$3$D}
Plant Shoot Modelling",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "1907--1917",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2896908",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2896908",
abstract = "Plant phenotyping is the quantitative description of a
plant&\#x0027;s physiological, biochemical, and
anatomical status which can be used in trait selection
and helps to provide mechanisms to link underlying
genetics with yield. Here, an active vision- \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jing:2020:AAE,
author = "Xiaoyang Jing and Qiwen Dong and Daocheng Hong and
Ruqian Lu",
title = "Amino Acid Encoding Methods for Protein Sequences: a
Comprehensive Review and Assessment",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "1918--1931",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2911677",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2911677",
abstract = "As the first step of machine-learning based protein
structure and function prediction, the amino acid
encoding play a fundamental role in the final success
of those methods. Different from the protein sequence
encoding, the amino acid encoding can be used
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Paul:2020:EAT,
author = "Soumya Paul and Cui Su and Jun Pang and Andrzej
Mizera",
title = "An Efficient Approach Towards the Source-Target
Control of {Boolean} Networks",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "1932--1945",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2915081",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2915081",
abstract = "We study the problem of computing a minimal subset of
nodes of a given asynchronous Boolean network that need
to be perturbed in a single-step to drive its dynamics
from an initial state to a target steady state (or {$<$
italic$>$ attractor$<$}/{italic$>$}), which we
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Qingge:2020:AOS,
author = "Letu Qingge and Killian Smith and Sean Jungst and
Baihui Wang and Qing Yang and Binhai Zhu",
title = "Approaching the One-Sided Exemplar Adjacency Number
Problem",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "1946--1954",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2913834",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2913834",
abstract = "The one-sided Exemplar Adjacency Number (EAN) is a
known problem for computing the exemplar similarity
between a generic linear genome {$<$ inline}-{formula$
> $$ <$ t e x} - math notation = ``LaTeX''{ $
>$}${\mathcal G}${$ <$ } / tex - math{ $ >$ }{ $ <$
}alternatives{ $ >$ }{ $ <$ }mml : math{ $ >$ }{ $ <$
}mml : mi mathvariant = ``script''{ $ >$ }. \ldots {}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Karim:2020:BNB,
author = "Mohammad Bozlul Karim and Ming Huang and Naoaki Ono
and Shigehiko Kanaya and Md. Altaf-Ul-Amin",
title = "{BiClusO}: a Novel Biclustering Approach and Its
Application to Species-{VOC} Relational Data",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "1955--1965",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2914901",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2914901",
abstract = "In this paper, we propose a novel biclustering
approach called BiClusO. Biclustering can be applied to
various types of bipartite data such as gene-condition
or gene-disease relations. For example, we applied
BiClusO to bipartite relations between species
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2020:BBP,
author = "Guang-Hui Liu and Bei-Wei Zhang and Gang Qian and Bin
Wang and Bo Mao and Isabelle Bichindaritz",
title = "Bioimage-Based Prediction of Protein Subcellular
Location in Human Tissue with Ensemble Features and
Deep Networks",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "1966--1980",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2917429",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2917429",
abstract = "Prediction of protein subcellular location has
currently become a hot topic because it has been proven
to be useful for understanding both the disease
mechanisms and novel drug design. With the rapid
development of automated microscopic imaging technology
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Paoletti:2020:DDR,
author = "Nicola Paoletti and Kin Sum Liu and Hongkai Chen and
Scott A. Smolka and Shan Lin",
title = "Data-Driven Robust Control for a Closed-Loop
Artificial Pancreas",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "1981--1993",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2912609",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2912609",
abstract = "We present a fully closed-loop design for an
artificial pancreas (AP) that regulates the delivery of
insulin for the control of Type I diabetes. Our AP
controller operates in a fully automated fashion,
without requiring any manual interaction with the
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2020:EMX,
author = "Jian Liu and Qiuru Liu and Lei Zhang and Shuhui Su and
Yongzhuang Liu",
title = "Enabling Massive {XML}-Based Biological Data
Management in {HBase}",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "1994--2004",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2915811",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2915811",
abstract = "Publishing biological data in XML formats is
attractive for organizations who would like to provide
their bioinformatics resources in an extensible and
machine-readable format. In the era of big data,
massive XML-based biological data management is
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Dutta:2020:EGC,
author = "Pratik Dutta and Sriparna Saha and Saraansh Chopra and
Varnika Miglani",
title = "Ensembling of Gene Clusters Utilizing Deep Learning
and Protein-Protein Interaction Information",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "2005--2016",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2918523",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2918523",
abstract = "Cluster ensemble techniques aim to combine the outputs
of multiple clustering algorithms to obtain a single
consensus partitioning. The current paper reports about
the development of a cluster ensemble based technique
combining the concepts of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Hu:2020:ICI,
author = "Lun Hu and Pengwei Hu and Xin Luo and Xiaohui Yuan and
Zhu-Hong You",
title = "Incorporating the Coevolving Information of Substrates
in Predicting {HIV-1} Protease Cleavage Sites",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "2017--2028",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2914208",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2914208",
abstract = "Human immunodeficiency virus 1 (HIV-1) protease (PR)
plays a crucial role in the maturation of the virus.
The study of substrate specificity of HIV-1 PR as a new
endeavor strives to increase our ability to understand
how HIV-1 PR recognizes its various \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yu:2020:LBE,
author = "Xinyi Yu and Wenge Rong and Jingshuang Liu and Deyu
Zhou and Yuanxin Ouyang and Zhang Xiong",
title = "{LSTM}-Based End-to-End Framework for Biomedical Event
Extraction",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "2029--2039",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2916346",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2916346",
abstract = "Biomedical event extraction plays an important role in
the extraction of biological information from
large-scale scientific publications. However, most
state-of-the-art systems separate this task into
several steps, which leads to cascading errors. In
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Kazemi:2020:MSA,
author = "Ehsan Kazemi and Matthias Grossglauser",
title = "{MPGM}: Scalable and Accurate Multiple Network
Alignment",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "2040--2052",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2914050",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2914050",
abstract = "Protein-protein interaction (PPI) network alignment is
a canonical operation to transfer biological knowledge
among species. The alignment of PPI-networks has many
applications, such as the prediction of protein
function, detection of conserved network \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2020:PEP,
author = "Wei Zhang and Jia Xu and Xiufen Zou",
title = "Predicting Essential Proteins by Integrating Network
Topology, Subcellular Localization Information, Gene
Expression Profile and {GO} Annotation Data",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "2053--2061",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2916038",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2916038",
abstract = "Essential proteins are indispensable for maintaining
normal cellular functions. Identification of essential
proteins from Protein-protein interaction (PPI)
networks has become a hot topic in recent years.
Traditionally biological experimental based \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{He:2020:PCI,
author = "Zengyou He and Can Zhao and Hao Liang and Bo Xu and
Quan Zou",
title = "Protein Complexes Identification with Family-Wise
Error Rate Control",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "2062--2073",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2912602",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2912602",
abstract = "The detection of protein complexes from
protein-protein interaction network is a fundamental
issue in bioinformatics and systems biology. To solve
this problem, numerous methods have been proposed from
different angles in the past decades. However, the
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Shrestha:2020:SMD,
author = "Midusha Shrestha and Truong X. Tran and Bidhan
Bhattarai and Marc L. Pusey and Ramazan S. Aygun",
title = "Schema Matching and Data Integration with Consistent
Naming on Protein Crystallization Screens",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "2074--2085",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2913368",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2913368",
abstract = "The data representation as well as naming conventions
used in commercial screen files by different companies
make the automated analysis of crystallization
experiments difficult and time-consuming. In order to
reduce the human effort required to deal with
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Carroll:2020:SSA,
author = "Thomas C. Carroll and Jude-Thaddeus Ojiaku and
Prudence W. H. Wong",
title = "Semiglobal Sequence Alignment with Gaps Using {GPU}",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "2086--2097",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2914105",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2914105",
abstract = "In this paper, we consider the pair-wise semiglobal
sequence alignment problem with gaps, which is
motivated by the {$<$ italic$>$
re}-{sequencing$<$}/{italic$>$} problem that requires
to assemble short reads sequences into a genome
sequence by referring to a reference \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Azuma:2020:SBA,
author = "Shun-Ichi Azuma and Toshimitsu Kure and Toshiharu
Sugie",
title = "Structural Bistability Analysis of Flower-Shaped and
Chain-Shaped {Boolean} Networks",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "2098--2106",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2917196",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2917196",
abstract = "{$<$ italic$>$Bistability$<$}/{italic$>$}, i.e., the
existence of just two stable equilibria, is known to
play an important role in biological systems, e.g.,
cellular differentiation and apoptosis. In this paper,
we consider the bistability but as a {$<$ italic$>$
structural} \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Rhodes:2020:TMT,
author = "John A. Rhodes",
title = "Topological Metrizations of Trees, and New Quartet
Methods of Tree Inference",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "2107--2118",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2917204",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2917204",
abstract = "Topological phylogenetic trees can be assigned edge
weights in several natural ways, highlighting different
aspects of the tree. Here, the rooted triple and
quartet metrizations are introduced, and applied to
formulate novel methods of inferring large \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2020:TSD,
author = "Gui-Jun Zhang and Xiao-Qi Wang and Lai-Fa Ma and
Liu-Jing Wang and Jun Hu and Xiao-Gen Zhou",
title = "Two-Stage Distance Feature-based Optimization
Algorithm for {{\em De novo\/}} Protein Structure
Prediction",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "2119--2130",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2917452",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2917452",
abstract = "De novo protein structure prediction can be treated as
a conformational space optimization problem under the
guidance of an energy function. However, it is a
challenge of how to design an accurate energy function
which ensures low-energy conformations \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ogunleye:2020:XMC,
author = "Adeola Ogunleye and Qing-Guo Wang",
title = "{XGBoost} Model for Chronic Kidney Disease Diagnosis",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "2131--2140",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2911071",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2911071",
abstract = "Chronic Kidney Disease (CKD) is a menace that is
affecting 10 percent of the world population and 15
percent of the South African population. The early and
cheap diagnosis of this disease with accuracy and
reliability will save 20,000 lives in South \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Manica:2020:FAA,
author = "Matteo Manica and Raphael Polig and Mitra Purandare
and Roland Mathis and Christoph Hagleitner and
Mar{\'\i}a Rodr{\'\i}guez Mart{\'\i}nez",
title = "{FPGA} Accelerated Analysis of {Boolean} Gene
Regulatory Networks",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "2141--2147",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2936836",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2936836",
abstract = "Boolean models are a powerful abstraction for
qualitative modeling of gene regulatory networks. With
the recent availability of advanced high-throughput
technologies, Boolean models have increasingly grown in
size and complexity, posing a challenge for \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xiao:2020:CDW,
author = "Ming Xiao and Xiangyu Yang and Jun Yu and Le Zhang",
title = "{CGIDLA}: Developing the {Web} Server for {CpG Island}
Related Density and {LAUPs} (Lineage-Associated
Underrepresented Permutations) Study",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "2148--2154",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2935971",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2935971",
abstract = "It is well known that CpG island plays an important
role in gene methylation. Since CpG island is closely
related to human genetic characteristics such as
TATA-box, tissue expression specificity, and LAUPs
(Lineage-associated Underrepresented Permutations).
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Petti:2020:CSD,
author = "Manuela Petti and Daniele Bizzarri and Antonella
Verrienti and Rosa Falcone and Lorenzo Farina",
title = "Connectivity Significance for Disease Gene
Prioritization in an Expanding Universe",
journal = j-TCBB,
volume = "17",
number = "6",
pages = "2155--2161",
month = nov,
year = "2020",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2938512",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Feb 23 08:57:36 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2938512",
abstract = "A fundamental topic in network medicine is disease
genes prioritization. The underlying hypothesis is that
disease genes are organized as modules confined within
the interactome. Here, we propose a novel algorithm
called DiaBLE \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Martin-Vide:2021:ACBa,
author = "Carlos Mart{\'\i}n-Vide and Miguel A.
Vega-Rodr{\'\i}guez",
title = "{{\booktitle{Algorithms for Computational Biology}}}:
Sixth Edition",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "1--1",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3023866",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3023866",
abstract = "The papers in this special section were presented at
the Sixth International Conference on Algorithms for
Computational Biology, AlCoB 2019, that was held in
Berkeley on May 28--30, 2019.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Le:2021:UCS,
author = "Thien Le and Aaron Sy and Erin K. Molloy and Qiuyi
Zhang and Satish Rao and Tandy Warnow",
title = "Using Constrained-{INC} for Large-Scale Gene Tree and
Species Tree Estimation",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "2--15",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2990867",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2990867",
abstract = "Incremental tree building (INC) is a new phylogeny
estimation method that has been proven to be absolute
fast converging under standard sequence evolution
models. A variant of INC, called Constrained-INC, is
designed for use in divide-and-conquer \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Maharaj:2021:CNE,
author = "Sridevi Maharaj and Taotao Qian and Zarin Ohiba and
Wayne Hayes",
title = "Common Neighbors Extension of the Sticky Model for
{PPI} Networks Evaluated by Global and Local Graphlet
Similarity",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "16--26",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3017374",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3017374",
abstract = "The structure of protein-protein interaction (PPI)
networks has been studied for over a decade. Many
theoretical models have been proposed to model PPI
network structure, but continuing noise and
incompleteness in these networks make conclusions about
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xiao:2021:EIA,
author = "Peng Xiao and Xingyu Cai and Sanguthevar Rajasekaran",
title = "{EMS3}: an Improved Algorithm for Finding
Edit-Distance Based Motifs",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "27--37",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3024222",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3024222",
abstract = "Discovering patterns in biological sequences is a
crucial step to extract useful information from them.
Motifs can be viewed as patterns that occur exactly or
with minor changes across some or all of the biological
sequences. Motif search has numerous \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2021:GET,
author = "Liu Liu and Reza Zare and Shuihua Wang",
title = "Guest Editorial: Transfer Learning Methods Used in
Medical Imaging and Health Informatics",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "38--39",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3020460",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3020460",
abstract = "The eight papers in this special section focus on
novel theories and methods using transfer learning
proposed for medical imaging and health information
processes.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jiang:2021:NNT,
author = "Yizhang Jiang and Xiaoqing Gu and Dongrui Wu and
Wenlong Hang and Jing Xue and Shi Qiu and Chin-Teng
Lin",
title = "A Novel Negative-Transfer-Resistant Fuzzy Clustering
Model With a Shared Cross-Domain Transfer Latent Space
and its Application to Brain {CT} Image Segmentation",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "40--52",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2963873",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2963873",
abstract = "Traditional clustering algorithms for medical image
segmentation can only achieve satisfactory clustering
performance under relatively ideal conditions, in which
there is adequate data from the same distribution, and
the data is rarely disturbed by noise \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xia:2021:CDC,
author = "Kaijian Xia and TongGuang Ni and Hongsheng Yin and Bo
Chen",
title = "Cross-Domain Classification Model With Knowledge
Utilization Maximization for Recognition of Epileptic
{EEG} Signals",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "53--61",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2973978",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2973978",
abstract = "Conventional classification models for epileptic EEG
signal recognition need sufficient labeled samples as
training dataset. In addition, when training and
testing EEG signal samples are collected from different
distributions, for example, due to \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Huang:2021:DSN,
author = "Chenxi Huang and Yisha Lan and Gaowei Xu and Xiaojun
Zhai and Jipeng Wu and Fan Lin and Nianyin Zeng and
Qingqi Hong and E. Y. K. Ng and Yonghong Peng and Fei
Chen and Guokai Zhang",
title = "A Deep Segmentation Network of Multi-Scale Feature
Fusion Based on Attention Mechanism for {IVOCT} Lumen
Contour",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "62--69",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2973971",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2973971",
abstract = "Recently, coronary heart disease has attracted more
and more attention, where segmentation and analysis for
vascular lumen contour are helpful for treatment. And
intravascular optical coherence tomography (IVOCT)
images are used to display lumen shapes in \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Qian:2021:TUM,
author = "Pengjiang Qian and Jiamin Zheng and Qiankun Zheng and
Yuan Liu and Tingyu Wang and Rose {Al Helo} and Atallah
Baydoun and Norbert Avril and Rodney J. Ellis and Harry
Friel and Melanie S. Traughber and Ajit Devaraj and
Bryan Traughber and Raymond F. Muzic",
title = "Transforming {UTE-mDixon MR} Abdomen--Pelvis Images
Into {CT} by Jointly Leveraging Prior Knowledge and
Partial Supervision",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "70--82",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2979841",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2979841",
abstract = "Computed tomography (CT) provides information for
diagnosis, PET attenuation correction (AC), and
radiation treatment planning (RTP). Disadvantages of CT
include poor soft tissue contrast and exposure to
ionizing radiation. While MRI can overcome these
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Singh:2021:IBC,
author = "Rishav Singh and Tanveer Ahmed and Abhinav Kumar and
Amit Kumar Singh and Anil Kumar Pandey and Sanjay Kumar
Singh",
title = "Imbalanced Breast Cancer Classification Using Transfer
Learning",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "83--93",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2980831",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2980831",
abstract = "Accurate breast cancer detection using automated
algorithms remains a problem within the literature.
Although a plethora of work has tried to address this
issue, an exact solution is yet to be found. This
problem is further exacerbated by the fact that
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yu:2021:RSB,
author = "Xiang Yu and Cheng Kang and David S. Guttery and
Seifedine Kadry and Yang Chen and Yu-Dong Zhang",
title = "{ResNet--SCDA--50} for Breast Abnormality
Classification",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "94--102",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2986544",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2986544",
abstract = "Aim: Breast cancer is the most common cancer in women
and the second most common cancer worldwide. With the
rapid advancement of deep learning, the early stages of
breast cancer development can be accurately detected by
radiologists with the help of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2021:TLB,
author = "Jintai Chen and Haochao Ying and Xuechen Liu and
Jingjing Gu and Ruiwei Feng and Tingting Chen and
Honghao Gao and Jian Wu",
title = "A Transfer Learning Based Super-Resolution Microscopy
for Biopsy Slice Images: The Joint Methods
Perspective",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "103--113",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2991173",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2991173",
abstract = "Higher-resolution biopsy slice images reveal many
details, which are widely used in medical practice.
However, taking high-resolution slice images is more
costly than taking low-resolution ones. In this paper,
we propose a joint framework containing a \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jin:2021:LTD,
author = "Yong Jin and Zhenjiang Qian and Shengrong Gong and
Weiyong Yang",
title = "Learning Transferable Driven and Drone Assisted
Sustainable and Robust Regional Disease Surveillance
for Smart Healthcare",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "114--125",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3017041",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3017041",
abstract = "Smart healthcare has been applied in many fields such
as disease surveillance and telemedicine, etc. However,
there are some challenges for device deployment, data
collection and guarantee of stainability in regional
disease surveillance. First, it is \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lim:2021:NWI,
author = "Hansaim Lim and Lei Xie",
title = "A New Weighted Imputed Neighborhood-Regularized
Tri-Factorization One-Class Collaborative Filtering
Algorithm: Application to Target Gene Prediction of
Transcription Factors",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "126--137",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2968442",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2968442",
abstract = "Identifying target genes of transcription factors
(TFs) is crucial to understand transcriptional
regulation. However, our understanding of genome-wide
TF targeting profile is limited due to the cost of
large-scale experiments and intrinsic complexity of
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Allen:2021:UFP,
author = "Daniel R. Allen and Sharma V. Thankachan and Bojian
Xu",
title = "An Ultra-Fast and Parallelizable Algorithm for Finding
$k$-Mismatch Shortest Unique Substrings",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "138--148",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2968531",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/string-matching.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2968531",
abstract = "This paper revisits the k-mismatch shortest unique
substring finding problem and demonstrates that a
technique recently presented in the context of solving
the k-mismatch average common substring problem can be
adapted and combined with parts of the existing
solution, resulting in a new algorithm which has
expected time complexity of $ O(n \log^k n) $, while
maintaining a practical space complexity at $ O(k n) $,
where $n$ is the string length. When $ k > 0$, which is
the hard case, our new proposal significantly improves
the anycase $ O(n^2)$ time complexity of the prior best
method for $k$-mismatch shortest unique substring
finding. Experimental study shows that our new
algorithm is practical to implement and demonstrates
significant improvements in processing time compared to
the prior best solution's implementation when $k$ is
small relative ton. For example, our method processes a
200 KB sample DNA sequence with $ k = 1$ in just 0.18
seconds compared to 174.37 seconds with the prior best
solution. Further, it is observed that significant
portions of the adapted technique can be executed in
parallel, using two different simple concurrency
models, resulting in further significant practical
performance improvement. As an example, when using 8
cores, the parallel implementations both achieved
processing times that are less than 1/4 of the serial
implementation's time cost, when processing a 10 MB
sample DNA sequence with $ k = 2$. In an age where
instances with thousands of gigabytes of RAM are
readily available for use through Cloud infrastructure
providers, it is likely that the trade-off of
additional memory usage for significantly improved
processing times will be desirable and needed by many
users. For example, the best prior solution may spend
years to finish a DNA sample of 200MB for any $ k > 0$,
while this new proposal, using 24 cores, can finish
processing a sample of this size with $ k = 1$ in
206.376 seconds with a peak memory usage of 46 GB,
which is both easily available and affordable on Cloud.
It is expected that this new efficient and practical
algorithm for $k$-mismatch shortest unique substring
finding will prove useful to those using the measure on
long sequences in fields such as computational biology.
We also give a theoretical bound that the $k$-mismatch
shortest unique substring finding problem can be solved
using $ O(n \log^k n)$ time and $ O(n)$ space,
asymptotically much better than the one we implemented,
serving as a new discovery of interest.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Tabaszewski:2021:CAS,
author = "P. Tabaszewski and P. G{\'o}recki and A. Markin and T.
Anderson and O. Eulenstein",
title = "Consensus of All Solutions for Intractable
Phylogenetic Tree Inference",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "149--161",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2947051",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2947051",
abstract = "Solving median tree problems is a classic approach for
inferring species trees from a collection of discordant
gene trees. Median tree problems are typically NP-hard
and dealt with by local search heuristics.
Unfortunately, such heuristics generally lack
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Pattabiraman:2021:PHM,
author = "Srilakshmi Pattabiraman and Tandy Warnow",
title = "Profile Hidden {Markov} Models Are Not Identifiable",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "162--172",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2933821",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2933821",
abstract = "Profile Hidden Markov Models (HMMs) are graphical
models that can be used to produce finite length
sequences from a distribution. In fact, although they
were only introduced for bioinformatics 25 years ago
(by Haussler et al., Hawaii International \ldots{}).",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Guan:2021:MGS,
author = "Jiaqi Guan and Runzhe Li and Sheng Yu and Xuegong
Zhang",
title = "A Method for Generating Synthetic Electronic Medical
Record Text",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "173--182",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2948985",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2948985",
abstract = "Machine learning (ML) and Natural Language Processing
(NLP) have achieved remarkable success in many fields
and have brought new opportunities and high expectation
in the analyses of medical data, of which the most
common type is the massive free-text \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Qin:2021:OIR,
author = "Ruiqi Qin and Lei Duan and Huiru Zheng and Jesse
Li-Ling and Kaiwen Song and Yidan Zhang",
title = "An Ontology-Independent Representation Learning for
Similar Disease Detection Based on Multi-Layer
Similarity Network",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "183--193",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2941475",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2941475",
abstract = "To identify similar diseases has significant
implications for revealing the etiology and
pathogenesis of diseases and further research in the
domain of biomedicine. Currently, most methods for the
measurement of disease similarity utilize either
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Long:2021:FDE,
author = "Wei Long and Tiange Li and Yang Yang and Hong-Bin
Shen",
title = "{FlyIT}: \bioname{Drosophila} Embryogenesis Image
Annotation based on Image Tiling and Convolutional
Neural Networks",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "194--204",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2935723",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2935723",
abstract = "With the rise of image-based transcriptomics, spatial
gene expression data has become increasingly important
for understanding gene regulations from the tissue
level down to the cell level. Especially, the gene
expression images of Drosophila embryos \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Dai:2021:GAG,
author = "Suyang Dai and Yuxia Ding and Zihan Zhang and Wenxuan
Zuo and Xiaodi Huang and Shanfeng Zhu",
title = "{GrantExtractor}: Accurate Grant Support Information
Extraction from Biomedical Fulltext Based on
{Bi-LSTM-CRF}",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "205--215",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2939128",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2939128",
abstract = "Grant support (GS) in the MEDLINE database refers to
funding agencies and contract numbers. It is important
for funding organizations to track their funding
outcomes from the GS information. As such, how to
accurately and automatically extract funding \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2021:IMO,
author = "Bo Yang and Yupei Zhang and Shanmin Pang and Xuequn
Shang and Xueqing Zhao and Minghui Han",
title = "Integrating Multi-Omic Data With Deep Subspace Fusion
Clustering for Cancer Subtype Prediction",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "216--226",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2951413",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2951413",
abstract = "One type of cancer usually consists of several
subtypes with distinct clinical implications, thus the
cancer subtype prediction is an important task in
disease diagnosis and therapy. Utilizing one type of
data from molecular layers in biological system to
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Du:2021:MTS,
author = "Lei Du and Kefei Liu and Xiaohui Yao and Shannon L.
Risacher and Junwei Han and Andrew J. Saykin and Lei
Guo and Li Shen",
title = "Multi-Task Sparse Canonical Correlation Analysis with
Application to Multi-Modal Brain Imaging Genetics",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "227--239",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2947428",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2947428",
abstract = "Brain imaging genetics studies the genetic basis of
brain structures and functionalities via integrating
genotypic data such as single nucleotide polymorphisms
(SNPs) and imaging quantitative traits (QTs). In this
area, both multi-task learning (MTL) and \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gao:2021:PAM,
author = "Jianliang Gao and Ling Tian and Tengfei Lv and Jianxin
Wang and Bo Song and Xiaohua Hu",
title = "{Protein2Vec}: Aligning Multiple {PPI} Networks with
Representation Learning",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "240--249",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2937771",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2937771",
abstract = "Research of Protein-Protein Interaction (PPI) Network
Alignment is playing an important role in understanding
the crucial underlying biological knowledge such as
functionally homologous proteins and conserved
evolutionary pathways across different \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chalk:2021:CRI,
author = "Cameron Chalk and Niels Kornerup and Wyatt Reeves and
David Soloveichik",
title = "Composable Rate-Independent Computation in Continuous
Chemical Reaction Networks",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "250--260",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2952836",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2952836",
abstract = "Biological regulatory networks depend upon chemical
interactions to process information. Engineering such
molecular computing systems is a major challenge for
synthetic biology and related fields. The chemical
reaction network (CRN) model idealizes \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Dunn:2021:FAN,
author = "Sara-Jane Dunn and Hillel Kugler and Boyan Yordanov",
title = "Formal Analysis of Network Motifs Links Structure to
Function in Biological Programs",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "261--271",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2948157",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2948157",
abstract = "A recurring set of small sub-networks have been
identified as the building blocks of biological
networks across diverse organisms. These network motifs
are associated with certain dynamic behaviors and
define key modules that are important for \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bokes:2021:MBF,
author = "Pavol Bokes and Michal Hojcka and Abhyudai Singh",
title = "{MicroRNA} Based Feedforward Control of Intrinsic Gene
Expression Noise",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "272--282",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2938502",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2938502",
abstract = "Intrinsic noise, which arises in gene expression at
low copy numbers, can be controlled by diverse
regulatory motifs, including feedforward loops. Here,
we study an example of a feedforward control system
based on the interaction between an mRNA molecule
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Breik:2021:PSI,
author = "Keenan Breik and Cameron Chalk and David Doty and
David Haley and David Soloveichik",
title = "Programming Substrate-Independent Kinetic Barriers
With Thermodynamic Binding Networks",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "283--295",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2959310",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2959310",
abstract = "Engineering molecular systems that exhibit complex
behavior requires the design of kinetic barriers. For
example, an effective catalytic pathway must have a
large barrier when the catalyst is absent. While
programming such energy barriers seems to require
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zeng:2021:DLF,
author = "Min Zeng and Min Li and Zhihui Fei and Fang-Xiang Wu
and Yaohang Li and Yi Pan and Jianxin Wang",
title = "A Deep Learning Framework for Identifying Essential
Proteins by Integrating Multiple Types of Biological
Information",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "296--305",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2897679",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2897679",
abstract = "Computational methods including centrality and machine
learning-based methods have been proposed to identify
essential proteins for understanding the minimum
requirements of the survival and evolution of a cell.
In centrality methods, researchers are \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Mu:2021:CML,
author = "Quanhua Mu and Jiguang Wang",
title = "{CNAPE}: a Machine Learning Method for Copy Number
Alteration Prediction from Gene Expression",
journal = j-TCBB,
volume = "18",
number = "1",
pages = "306--311",
month = jan,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2944827",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Jun 15 14:32:53 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2944827",
abstract = "Detection of DNA copy number alteration in cancer
cells is critical to understanding cancer initiation
and progression. Widely used methods, such as DNA
arrays and genomic DNA sequencing, are relatively
expensive and require DNA samples at a microgram
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Kitrungrotsakul:2021:CCB,
author = "Titinunt Kitrungrotsakul and Xian-Hau Han and Yutaro
Iwamoto and Satoko Takemoto and Hideo Yokota and Sari
Ipponjima and Tomomi Nemoto and Wei Xiong and Yen-Wei
Chen",
title = "A Cascade of {2.5D CNN} and Bidirectional {CLSTM}
Network for Mitotic Cell Detection in {$4$D} Microscopy
Image",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "396--404",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2919015",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2919015",
abstract = "Mitosis detection is one of the challenging steps in
biomedical imaging research, which can be used to
observe the cell behavior. Most of the already existing
methods that are applied in detecting mitosis usually
contain many nonmitotic events (normal \ldots{})",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:FLN,
author = "Wen Zhang and Zhishuai Li and Wenzheng Guo and Weitai
Yang and Feng Huang",
title = "A Fast Linear Neighborhood Similarity-Based Network
Link Inference Method to Predict {MicroRNA}--Disease
Associations",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "405--415",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2931546",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2931546",
abstract = "Increasing evidences revealed that microRNAs (miRNAs)
play critical roles in important biological processes.
The identification of disease-related miRNAs is
critical to understand the molecular mechanisms of
human diseases. Most existing computational \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2021:FAC,
author = "Biing-Feng Wang and Krister M. Swenson",
title = "A Faster Algorithm for Computing the Kernel of Maximum
Agreement Subtrees",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "416--430",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2922955",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2922955",
abstract = "The maximum agreement subtree method determines the
consensus of a collection of phylogenetic trees by
identifying maximum cardinality subsets of leaves for
which all input trees agree. The trees induced by these
maximum cardinality subsets are maximum \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xu:2021:GRB,
author = "Yunpei Xu and Hong-Dong Li and Yi Pan and Feng Luo and
Fang-Xiang Wu and Jianxin Wang",
title = "A Gene Rank Based Approach for Single Cell Similarity
Assessment and Clustering",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "431--442",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2931582",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2931582",
abstract = "Single-cell RNA sequencing (scRNA-seq) technology
provides quantitative gene expression profiles at
single-cell resolution. As a result, researchers have
established new ways to explore cell population
heterogeneity and genetic variability of cells. One
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Araghi:2021:HSA,
author = "Sahar Araghi and Thanh Nguyen",
title = "A Hybrid Supervised Approach to Human Population
Identification Using Genomics Data",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "443--454",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2919501",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2919501",
abstract = "Single nucleotide polymorphisms (SNPs) are one type of
genetic variations and each SNP represents a difference
in a single DNA building block, namely a nucleotide.
Previous research demonstrated that SNPs can be used to
identify the correct source \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chou:2021:NBB,
author = "Hsin-Hung Chou and Ching-Tien Hsu and Li-Hsuan Chen
and Yue-Cheng Lin and Sun-Yuan Hsieh",
title = "A Novel Branch-and-Bound Algorithm for the Protein
Folding Problem in the {$3$D HP} Model",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "455--462",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2934102",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2934102",
abstract = "The protein folding problem (PFP) is an important
issue in bioinformatics and biochemical physics. One of
the most widely studied models of protein folding is
the hydrophobic-polar (HP) model introduced by Dill.
The PFP in the three-dimensional (3D) \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Luo:2021:NDR,
author = "Huimin Luo and Jianxin Wang and Cheng Yan and Min Li
and Fang-Xiang Wu and Yi Pan",
title = "A Novel Drug Repositioning Approach Based on
Collaborative Metric Learning",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "463--471",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2926453",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2926453",
abstract = "Computational drug repositioning, which is an
efficient approach to find potential indications for
drugs, has been used to increase the efficiency of drug
development. The drug repositioning problem essentially
is a top-K recommendation task that \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sadeghi:2021:ARC,
author = "Seyedeh Shaghayegh Sadeghi and Mohammad Reza
Keyvanpour",
title = "An Analytical Review of Computational Drug
Repurposing",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "472--488",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2933825",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2933825",
abstract = "Drug repurposing is a vital function in pharmaceutical
fields and has gained popularity in recent years in
both the pharmaceutical industry and research
community. It refers to the process of discovering new
uses and indications for existing or failed \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Banerjee:2021:EPG,
author = "Anupam Banerjee and Kuntal Pal and Pralay Mitra",
title = "An Evolutionary Profile Guided Greedy Parallel
Replica-Exchange {Monte Carlo} Search Algorithm for
Rapid Convergence in Protein Design",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "489--499",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2928809",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2928809",
abstract = "Protein design, also known as the inverse protein
folding problem, is the identification of a protein
sequence that folds into a target protein structure.
Protein design is proved as an NP-hard problem. While
researchers are working on designing \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gu:2021:IMT,
author = "Wanrong Gu and Ziye Zhang and Xianfen Xie and Yichen
He",
title = "An Improved Muti-Task Learning Algorithm for Analyzing
Cancer Survival Data",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "500--511",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2920770",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2920770",
abstract = "Survival analysis is a popular branch of statistics.
At present, many algorithms (like traditional
multi-tasking learning model) cannot be applied well in
practice because of censored data. Although using some
model (like parametric regression model) can \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chow:2021:PAA,
author = "Kevin Chow and Aisharjya Sarkar and Rasha Elhesha and
Pietro Cinaglia and Ahmet Ay and Tamer Kahveci",
title = "\pkg{ANCA}: Alignment-Based Network Construction
Algorithm",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "512--524",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2923620",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2923620",
abstract = "Dynamic biological networks model changes in the
network topology over time. However, often the
topologies of these networks are not available at
specific time points. Existing algorithms for studying
dynamic networks often ignore this problem and focus
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gonzalez:2021:BIL,
author = "Miguel Gonz{\'a}lez and Cristina Guti{\'e}rrez and
Rodrigo Mart{\'\i}nez",
title = "{Bayesian} Inference in {Y}-Linked Two-Sex Branching
Processes with Mutations: {ABC} Approach",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "525--538",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2921308",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2921308",
abstract = "A Y-linked two-sex branching process with mutations
and blind choice of males is a suitable model for
analyzing the evolution of the number of carriers of a
Y-linked allele and its mutations. Such a model
considers a two-sex monogamous population in which
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yuan:2021:PCI,
author = "Xiguo Yuan and Jiaao Yu and Jianing Xi and Liying Yang
and Junliang Shang and Zhe Li and Junbo Duan",
title = "\pkg{CNV\_IFTV}: an Isolation Forest and Total
Variation-Based Detection of {CNVs} from Short-Read
Sequencing Data",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "539--549",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2920889",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2920889",
abstract = "Accurate detection of copy number variations (CNVs)
from short-read sequencing data is challenging due to
the uneven distribution of reads and the unbalanced
amplitudes of gains and losses. The direct use of read
depths to measure CNVs tends to limit \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Biswas:2021:CNM,
author = "Sourav Biswas and Sumanta Ray and Sanghamitra
Bandyopadhyay",
title = "Colored Network {Motif} Analysis by Dynamic
Programming Approach: an Application in Host Pathogen
Interaction Network",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "550--561",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2923173",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2923173",
abstract = "Network motifs are subgraphs of a network which are
found with significantly higher frequency than that
expected in similar random networks. Motifs are small
building blocks of a network and they have emerged as a
way to uncover topological properties of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Kimmel:2021:DCR,
author = "Jacob C. Kimmel and Andrew S. Brack and Wallace F.
Marshall",
title = "Deep Convolutional and Recurrent Neural Networks for
Cell Motility Discrimination and Prediction",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "562--574",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2919307",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2919307",
abstract = "Cells in culture display diverse motility behaviors
that may reflect differences in cell state and
function, providing motivation to discriminate between
different motility behaviors. Current methods to do so
rely upon manual feature engineering. However,
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2021:DDL,
author = "Min Li and Yake Wang and Ruiqing Zheng and Xinghua Shi
and Yaohang Li and Fang-Xiang Wu and Jianxin Wang",
title = "{DeepDSC}: a Deep Learning Method to Predict Drug
Sensitivity of Cancer Cell Lines",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "575--582",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2919581",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2919581",
abstract = "High-throughput screening technologies have provided a
large amount of drug sensitivity data for a panel of
cancer cell lines and hundreds of compounds.
Computational approaches to analyzing these data can
benefit anticancer therapeutics by identifying
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:IRP,
author = "Jingrong Zhang and Zihao Wang and Zhiyong Liu and Fa
Zhang",
title = "Improve the Resolution and Parallel Performance of the
Three-Dimensional Refine Algorithm in {RELION} Using
{CUDA} and {MPI}",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "583--595",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2929171",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2929171",
abstract = "In cryo-electron microscopy, RELION is a powerful tool
for high-resolution reconstruction. Due to the
complicated imaging procedure and the heterogeneity of
particles, some of the selected particle images offer
more disturbing information than others. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Awais:2021:PIP,
author = "Muhammad Awais and Waqar Hussain and Yaser Daanial
Khan and Nouman Rasool and Sher Afzal Khan and Kuo-Chen
Chou",
title = "\pkg{iPhosH-PseAAC}: Identify Phosphohistidine Sites
in Proteins by Blending Statistical Moments and
Position Relative Features According to the {Chou}'s
5-Step Rule and General Pseudo Amino Acid Composition",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "596--610",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2919025",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2919025",
abstract = "Protein phosphorylation is one of the key mechanism in
prokaryotes and eukaryotes and is responsible for
various biological functions such as protein
degradation, intracellular localization, the multitude
of cellular processes, molecular association,
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yan:2021:PMP,
author = "Cheng Yan and Guihua Duan and Fang-Xiang Wu and Yi Pan
and Jianxin Wang",
title = "\pkg{MCHMDA}: Predicting Microbe-Disease Associations
Based on Similarities and Low-Rank Matrix Completion",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "611--620",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2926716",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2926716",
abstract = "With the development of high-through sequencing
technology and microbiology, many studies have
evidenced that microbes are associated with human
diseases, such as obesity, liver cancer, and so on.
Therefore, identifying the association between microbes
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Peng:2021:PMM,
author = "Cheng Peng and Xinyu Wu and Wen Yuan and Xinran Zhang
and Yu Zhang and Ying Li",
title = "\pkg{MGRFE}: Multilayer Recursive Feature Elimination
Based on an Embedded Genetic Algorithm for Cancer
Classification",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "621--632",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2921961",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2921961",
abstract = "Microarray gene expression data have become a topic of
great interest for cancer classification and for
further research in the field of bioinformatics.
Nonetheless, due to the &\#x201C;large {$<$
inline}-{formula$ > $$ <$ t e x} - math notation =
``LaTeX''{ $ >$}$ p${$ <$ } / tex - {m a t h $ > $$<$}.
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gondeau:2021:OWN,
author = "Alexandre Gondeau and Zahia Aouabed and Mohamed Hijri
and Pedro R. Peres-Neto and Vladimir Makarenkov",
title = "Object Weighting: a New Clustering Approach to Deal
with Outliers and Cluster Overlap in Computational
Biology",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "633--643",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2921577",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2921577",
abstract = "Considerable efforts have been made over the last
decades to improve the robustness of clustering
algorithms against noise features and outliers, known
to be important sources of error in clustering.
Outliers dominate the sum-of-the-squares calculations
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Karbalayghareh:2021:OBT,
author = "Alireza Karbalayghareh and Xiaoning Qian and Edward R.
Dougherty",
title = "Optimal {Bayesian} Transfer Learning for Count Data",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "644--655",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2920981",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2920981",
abstract = "There is often a limited amount of omics data to
design predictive models in biomedicine. Knowing that
these omics data come from underlying processes that
may share common pathways and disease mechanisms, it
may be beneficial for designing a more \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lopez-Lopera:2021:PIG,
author = "Andr{\'e}s F. L{\'o}pez-Lopera and Nicolas Durrande
and Mauricio A. {\'A}lvarez",
title = "Physically-Inspired {Gaussian} Process Models for
Post-Transcriptional Regulation in
\bioname{Drosophila}",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "656--666",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2918774",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2918774",
abstract = "The regulatory process of Drosophila is thoroughly
studied for understanding a great variety of biological
principles. While pattern-forming gene networks are
analyzed in the transcription step,
post-transcriptional events (e.g., translation, protein
\ldots{})",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:PVT,
author = "Qinhu Zhang and Zhen Shen and De-Shuang Huang",
title = "Predicting {\em in-vitro\/} Transcription Factor
Binding Sites Using {DNA} Sequence $+$ Shape",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "667--676",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2947461",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2947461",
abstract = "Discovery of transcription factor binding sites
(TFBSs) is essential for understanding the underlying
binding mechanisms and cellular functions. Recently,
Convolutional neural network (CNN) has succeeded in
predicting TFBSs from the primary DNA sequences.
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Qiu:2021:PAR,
author = "Jingxuan Qiu and Tianyi Qiu and Qingli Dong and Dongpo
Xu and Xiang Wang and Qi Zhang and Jing Pan and Qing
Liu",
title = "Predicting the Antigenic Relationship of
Foot-and-Mouth Disease Virus for Vaccine Selection
Through a Computational Model",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "677--685",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2923396",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2923396",
abstract = "Foot-and-mouth disease virus (FMDV) is an
antigenic-variable RNA virus that is responsible for
the recurrence of foot-and-mouth disease in livestock
and can be prevented and controlled using a vaccine
with broad-spectrum protection. Current anti-genicity
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:PRB,
author = "Chen Zhang and Yanrui Ding",
title = "Probing the Relation Between Community Evolution in
Dynamic Residue Interaction Networks and Xylanase
Thermostability",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "686--696",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2922906",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2922906",
abstract = "Residue-residue interactions are the basis of protein
thermostability. The molecular conformations of {$<$
italic$>$Streptomyces} {lividans$<$}/{italic$>$}
xylanase (xyna_strli) and {$<$ italic$>$Thermoascus}
{aurantiacus$<$}/{italic$>$} xylanase (xyna_theau) at
300K, 325K and 350K \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:PSP,
author = "Gui-Jun Zhang and Teng-Yu Xie and Xiao-Gen Zhou and
Liu-Jing Wang and Jun Hu",
title = "Protein Structure Prediction Using Population-Based
Algorithm Guided by Information Entropy",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "697--707",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2921958",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2921958",
abstract = "Ab initio protein structure prediction is one of the
most challenging problems in computational biology.
Multistage algorithms are widely used in ab initio
protein structure prediction. The different
computational costs of a multistage algorithm for
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Biswas:2021:RPC,
author = "Saikat Biswas and Pabitra Mitra and Krothapalli
Sreenivasa Rao",
title = "Relation Prediction of Co-Morbid Diseases Using
Knowledge Graph Completion",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "708--717",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2927310",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2927310",
abstract = "Co-morbid disease condition refers to the simultaneous
presence of one or more diseases along with the primary
disease. A patient suffering from co-morbid diseases
possess more mortality risk than with a disease alone.
So, it is necessary to predict co-. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Song:2021:SDS,
author = "Xiaona Song and Mi Wang and Shuai Song and Choon Ki
Ahn",
title = "Sampled-Data State Estimation of Reaction Diffusion
Genetic Regulatory Networks via Space-Dividing
Approaches",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "718--730",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2919532",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2919532",
abstract = "A novel state estimator is designed for genetic
regulatory networks with reaction-diffusion terms in
this study. First, the diffusion space (where mRNA and
protein exist) is divided into several parts and only a
point, a line, or a plane, etc., is \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Guo:2021:SNG,
author = "Guimu Guo and Hongzhi Chen and Da Yan and James Cheng
and Jake Y. Chen and Zechen Chong",
title = "Scalable {De Novo} Genome Assembly Using a Pregel-Like
Graph-Parallel System",
journal = j-TCBB,
volume = "18",
number = "2",
pages = "731--744",
month = mar,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2920912",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:16 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2920912",
abstract = "De novo genome assembly is the process of stitching
short DNA sequences to generate longer DNA sequences,
without using any reference sequence for alignment. It
enables high-throughput genome sequencing and thus
accelerates the discovery of new genomes. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yan:2021:GES,
author = "Da Yan and Sharma Thankachan and Jake Y. Chen",
title = "Guest Editorial for Selected Papers From {BIOKDD
2019}",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "809--810",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3067071",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3067071",
abstract = "The papers in this special section were presented at
the 18th International Workshop on Data Mining in
Bioinformatics (BIOKDD), held in conjunction with the
ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining that was held on August
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Raghu:2021:PIT,
author = "Vineet K. Raghu and Xiaoyu Ge and Arun Balajiee and
Daniel J. Shirer and Isha Das and Panayiotis V. Benos
and Panos K. Chrysanthis",
title = "A Pipeline for Integrated Theory and Data-Driven
Modeling of Biomedical Data",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "811--822",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3019237",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3019237",
abstract = "Genome sequencing technologies have the potential to
transform clinical decision making and biomedical
research by enabling high-throughput measurements of
the genome at a granular level. However, to truly
understand mechanisms of disease and predict the
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jiang:2021:BKG,
author = "Tianwen Jiang and Qingkai Zeng and Tong Zhao and Bing
Qin and Ting Liu and Nitesh V. Chawla and Meng Jiang",
title = "Biomedical Knowledge Graphs Construction From
Conditional Statements",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "823--835",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2979959",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2979959",
abstract = "Conditions play an essential role in biomedical
statements. However, existing biomedical knowledge
graphs (BioKGs) only focus on factual knowledge,
organized as a flat relational network of biomedical
concepts. These BioKGs ignore the conditions of the
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sreedharan:2021:RPE,
author = "Jithin K. Sreedharan and Krzysztof Turowski and
Wojciech Szpankowski",
title = "Revisiting Parameter Estimation in Biological
Networks: Influence of Symmetries",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "836--849",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2980260",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2980260",
abstract = "Graph models often give us a deeper understanding of
real-world networks. In the case of biological networks
they help in predicting the evolution and history of
biomolecule interactions, provided we map properly real
networks into the corresponding graph \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yan:2021:GEM,
author = "Ke Yan and Zhiwei Ji and Qun Jin and Qing-Guo Wang",
title = "Guest Editorial: Machine Learning for {AI}-Enhanced
Healthcare and Medical Services: New Development and
Promising Solution",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "850--851",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3050935",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3050935",
abstract = "The papers in this special section focus on machine
learning for artificial intelligent-enhances healthcare
and medical services. These services are always among
the top concerns for humans, especially under the
special situation of COVID-19 pandemic, \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lian:2021:GLE,
author = "Sheng Lian and Lei Li and Guiren Lian and Xiao Xiao
and Zhiming Luo and Shaozi Li",
title = "A Global and Local Enhanced Residual {U-Net} for
Accurate Retinal Vessel Segmentation",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "852--862",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2917188",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2917188",
abstract = "Retinal vessel segmentation is a critical procedure
towards the accurate visualization, diagnosis, early
treatment, and surgery planning of ocular diseases.
Recent deep learning-based approaches have achieved
impressive performance in retinal vessel \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lu:2021:HEA,
author = "Huijuan Lu and Huiyun Gao and Minchao Ye and Xiuhui
Wang",
title = "A Hybrid Ensemble Algorithm Combining {AdaBoost} and
Genetic Algorithm for Cancer Classification with Gene
Expression Data",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "863--870",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2952102",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2952102",
abstract = "The diversity of base classifiers and integration of
multiple classifiers are two key issues in the field of
ensemble learning. This paper puts forward a hybrid
ensemble algorithm combining AdaBoost and genetic
algorithm(GA) for cancer classification with \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yan:2021:MIP,
author = "Xiaodan Yan and Baojiang Cui and Yang Xu and Peilin
Shi and Ziqi Wang",
title = "A Method of Information Protection for Collaborative
Deep Learning under {GAN} Model Attack",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "871--881",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2940583",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2940583",
abstract = "Deep learning is widely used in the medical field
owing to its high accuracy in medical image
classification and biological applications. However,
under collaborative deep learning, there is a serious
risk of information leakage based on the deep
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:USC,
author = "Qingchen Zhang and Changchuan Bai and Laurence T. Yang
and Zhikui Chen and Peng Li and Hang Yu",
title = "A Unified Smart {Chinese} Medicine Framework for
Healthcare and Medical Services",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "882--890",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2914447",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2914447",
abstract = "Smart Chinese medicine has emerged to contribute to
the evolution of healthcare and medical services by
applying machine learning together with advanced
computing techniques like cloud computing to
computer-aided diagnosis and treatment in the health
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2021:KPD,
author = "Dehua Chen and Meihua Huang and Weimin Li",
title = "Knowledge-Powered Deep Breast Tumor Classification
With Multiple Medical Reports",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "891--901",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2955484",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2955484",
abstract = "Breast tumor classification with multiple medical
reports such as B-ultrasound, Mammography (X-ray) and
Nuclear Magnetic Resonance Imaging (MRI) is crucial to
the intelligent cancer diagnosis system. Unlike the
other domain texts, the medical reports have \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2021:CCB,
author = "Yiyuan Chen and Yufeng Wang and Liang Cao and Qun
Jin",
title = "{CCFS}: a Confidence-Based Cost-Effective Feature
Selection Scheme for Healthcare Data Classification",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "902--911",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2903804",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2903804",
abstract = "Feature selection (FS) is one of the fundamental data
processing techniques in various machine learning
algorithms, especially for classification of healthcare
data. However, it is a challenging issue due to the
large search space. Binary Particle Swarm \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhou:2021:CRB,
author = "Xiaokang Zhou and Yue Li and Wei Liang",
title = "{CNN-RNN} Based Intelligent Recommendation for Online
Medical Pre-Diagnosis Support",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "912--921",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2994780",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2994780",
abstract = "The rapidly developed Health 2.0 technology has
provided people with more opportunities to conduct
online medical consultation than ever before.
Understanding contexts within different online medical
communications and activities becomes a significant
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yan:2021:DED,
author = "Lu Yan and Weihong Huang and Liming Wang and Song Feng
and Yonghong Peng and Jie Peng",
title = "Data-Enabled Digestive Medicine: a New Big Data
Analytics Platform",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "922--931",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2951555",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2951555",
abstract = "This paper presents a big data analytics platform for
clinical research and practice in the Gastroenterology
Department of Xiangya Hospital at Central South
University in China. This platform features a
comprehensive and systematic support of big data in
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Souza:2021:DCI,
author = "Mauricio Guevara Souza and Edgar E. Vallejo and Karol
Estrada",
title = "Detecting Clustered Independent Rare Variant
Associations Using Genetic Algorithms",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "932--939",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2930505",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2930505",
abstract = "The availability of an increasing collection of
sequencing data provides the opportunity to study
genetic variation with an unprecedented level of
detail. There is much interest in uncovering the role
of rare variants and their contribution to disease.
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhou:2021:DUD,
author = "Yongjin Zhou and Weijian Huang and Pei Dong and Yong
Xia and Shanshan Wang",
title = "{D-UNet}: a Dimension-Fusion {U} Shape Network for
Chronic Stroke Lesion Segmentation",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "940--950",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2939522",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2939522",
abstract = "Assessing the location and extent of lesions caused by
chronic stroke is critical for medical diagnosis,
surgical planning, and prognosis. In recent years, with
the rapid development of 2D and 3D convolutional neural
networks (CNN), the encoder-decoder \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2021:EDP,
author = "Yaqi Wang and Lingling Sun and Qun Jin",
title = "Enhanced Diagnosis of Pneumothorax with an Improved
Real-Time Augmentation for Imbalanced Chest {X}-rays
Data Based on {DCNN}",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "951--962",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2911947",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2911947",
abstract = "Pneumothorax is a common pulmonary disease that can
lead to dyspnea and can be life-threatening. X-ray
examination is the main means to diagnose this disease.
Computer-aided diagnosis of pneumothorax on chest
X-ray, as a prerequisite for a timely cure, \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2021:HGR,
author = "Xiuhui Wang and Shiling Feng and Wei Qi Yan",
title = "Human Gait Recognition Based on Self-Adaptive Hidden
{Markov} Model",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "963--972",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2951146",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2951146",
abstract = "Human gait recognition has numerous challenges due to
view angle changing, human dressing, bag carrying, and
pedestrian walking speed, etc. In order to increase
gait recognition accuracy under these circumstances, in
this paper we propose a method for \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Pal:2021:IDR,
author = "Jayanta Kumar Pal and Shubhra Sankar Ray and Sankar K.
Pal",
title = "Identifying Drug Resistant {miRNAs} Using Entropy
Based Ranking",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "973--984",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2933205",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2933205",
abstract = "MicroRNAs play an important role in controlling drug
sensitivity and resistance in cancer. Identification of
responsible miRNAs for drug resistance can enhance the
effectiveness of treatment. A new set theoretic entropy
measure (SPEM) is defined to \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2021:IDP,
author = "Bing Wang and Changqing Mei and Yuanyuan Wang and
Yuming Zhou and Mu-Tian Cheng and Chun-Hou Zheng and
Lei Wang and Jun Zhang and Peng Chen and Yan Xiong",
title = "Imbalance Data Processing Strategy for Protein
Interaction Sites Prediction",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "985--994",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2953908",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2953908",
abstract = "Protein-protein interactions play essential roles in
various biological progresses. Identifying protein
interaction sites can facilitate researchers to
understand life activities and therefore will be
helpful for drug design. However, the number of
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jiang:2021:MBR,
author = "Xiran Jiang and Jiaxin Li and Yangyang Kan and Tao Yu
and Shijie Chang and Xianzheng Sha and Hairong Zheng
and Yahong Luo and Shanshan Wang",
title = "{MRI} Based Radiomics Approach With Deep Learning for
Prediction of Vessel Invasion in Early-Stage Cervical
Cancer",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "995--1002",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2963867",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2963867",
abstract = "This article aims to build deep learning-based
radiomic methods in differentiating vessel invasion
from non-vessel invasion in cervical cancer with
multi-parametric MRI data. A set of 1,070 dynamic T1
contrast-enhanced (DCE-T1) and 986 T2 weighted imaging
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2021:MVM,
author = "Cheng Li and Jingxu Xu and Qiegen Liu and Yongjin Zhou
and Lisha Mou and Zuhui Pu and Yong Xia and Hairong
Zheng and Shanshan Wang",
title = "Multi-View Mammographic Density Classification by
Dilated and Attention-Guided Residual Learning",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "1003--1013",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2970713",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2970713",
abstract = "Breast density is widely adopted to reflect the
likelihood of early breast cancer development. Existing
methods of mammographic density classification either
require steps of manual operations or achieve only
moderate classification accuracy due to the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cakmak:2021:PMA,
author = "Ali Cakmak and M. Hasan Celik",
title = "Personalized Metabolic Analysis of Diseases",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "1014--1025",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3008196",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3008196",
abstract = "The metabolic wiring of patient cells is altered
drastically in many diseases, including cancer.
Understanding the nature of such changes may pave the
way for new therapeutic opportunities as well as the
development of personalized treatment strategies
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2021:PPG,
author = "Wenyan Wang and Yuming Zhou and Mu-Tian Cheng and Yan
Wang and Chun-Hou Zheng and Yan Xiong and Peng Chen and
Zhiwei Ji and Bing Wang",
title = "Potential Pathogenic Genes Prioritization Based on
Protein Domain Interaction Network Analysis",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "1026--1034",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2983894",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2983894",
abstract = "Pathogenicity-related studies are of great importance
in understanding the pathogenesis of complex diseases
and improving the level of clinical medicine. This work
proposed a bioinformatics scheme to analyze
cancer-related gene mutations, and try to \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bouasker:2021:PAB,
author = "Souad Bouasker and Wissem Inoubli and Sadok {Ben
Yahia} and Gayo Diallo",
title = "Pregnancy Associated Breast Cancer Gene Expressions:
New Insights on Their Regulation Based on Rare
Correlated Patterns",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "1035--1048",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3015236",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3015236",
abstract = "Breast-cancer (BC) is the most common invasive cancer
in women, with considerable death. Given that, BC is
classified as a hormone-dependent cancer, when it
collides with pregnancy, different questions may arise
for which there are still no convincing \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2021:NAP,
author = "Jiechen Li and Haochen Zhao and Zhanwei Xuan and
Jingwen Yu and Xiang Feng and Bo Liao and Lei Wang",
title = "A Novel Approach for Potential Human {LncRNA}-Disease
Association Prediction Based on Local Random Walk",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "1049--1059",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2934958",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2934958",
abstract = "In recent years, lncRNAs (long non-coding RNAs) have
been proved to be closely related to many diseases that
are seriously harmful to human health. Although
researches on clarifying the relationships between
lncRNAs and diseases are developing rapidly, \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2021:DLG,
author = "Min Liu and Yalan Liu and Weili Qian and Yaonan Wang",
title = "{DeepSeed} Local Graph Matching for Densely Packed
Cells Tracking",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "1060--1069",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2936851",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2936851",
abstract = "The tracking of densely packed plant cells across
microscopy image sequences is very challenging, because
their appearance change greatly over time. A local
graph matching algorithm was proposed to track such
cells by exploiting the tight spatial topology
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:DDM,
author = "Zhen Zhang and Junwei Luo and Juan Shang and Min Li
and Fang-Xiang Wu and Yi Pan and Jianxin Wang",
title = "Deletion Detection Method Using the Distribution of
Insert Size and a Precise Alignment Strategy",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "1070--1081",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2934407",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2934407",
abstract = "Homozygous and heterozygous deletions commonly exist
in the human genome. For current structural variation
detection tools, it is significant to determine whether
a deletion is homozygous or heterozygous. However, the
problems of sequencing errors, micro-. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yu:2021:DBF,
author = "Tingting Yu and Jianxing Liu and Qingshuang Zeng and
Ligang Wu",
title = "Dissipativity-Based Filtering for Switched Genetic
Regulatory Networks with Stochastic Disturbances and
Time-Varying Delays",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "1082--1092",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2936351",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2936351",
abstract = "This paper deals with the problem of
dissipativity-based filtering for switched genetic
regulatory networks (GRNs) with stochastic perturbation
and time-varying delays. By choosing an appropriate
piecewise Lyapunov function and using the average dwell
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{An:2021:HRP,
author = "Ying An and Nengjun Huang and Xianlai Chen and
Fangxiang Wu and Jianxin Wang",
title = "High-Risk Prediction of Cardiovascular Diseases via
Attention-Based Deep Neural Networks",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "1093--1105",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2935059",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2935059",
abstract = "High-risk prediction of cardiovascular disease is of
great significance and impendency in medical fields
with the increasing phenomenon of sub-health these
years. Most existing pathological methods for the
prognosis prediction are either costly or prone
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2021:IIA,
author = "Qingfeng Chen and Dehuan Lai and Wei Lan and Ximin Wu
and Baoshan Chen and Jin Liu and Yi-Ping Phoebe Chen
and Jianxin Wang",
title = "{ILDMSF}: Inferring Associations Between Long
Non-Coding {RNA} and Disease Based on Multi-Similarity
Fusion",
journal = j-TCBB,
volume = "18",
number = "3",
pages = "1106--1112",
month = may # "\slash " # jun,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2936476",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:52 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2936476",
abstract = "The dysregulation and mutation of long non-coding RNAs
(lncRNAs) have been proved to result in a variety of
human diseases. Identifying potential disease-related
lncRNAs may benefit disease diagnosis, treatment and
prognosis. A number of methods have been \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wei:2021:ECG,
author = "Dong-Qing Wei and Aman Chandra Kaushik and Gurudeeban
Selvaraj and Yi Pan",
title = "Editorial: Computational Genomics and Molecular
Medicine for Emerging {COVID-19}",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1227--1229",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3088319",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3088319",
abstract = "The papers in this special section focus on
computational genomics and molecular medicine for
emerging COVID-19. In 2020, World Health Organization
announced Coronavirus disease (COVID)-19 is a pandemic
disease, which is devastated the socio-economic life
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cui:2021:IAI,
author = "Chunmei Cui and Chuanbo Huang and Wanlu Zhou and
Xiangwen Ji and Fenghong Zhang and Liang Wang and Yuan
Zhou and Qinghua Cui",
title = "\gene{AGTR2}, One Possible Novel Key Gene for the
Entry of {SARS-CoV-2} Into Human Cells",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1230--1233",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3009099",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3009099",
abstract = "Recently, it was confirmed that {$<$
italic$>$ACE2$<$}/{italic$>$} is the receptor of
SARS-CoV-2, the pathogen causing the recent outbreak of
severe pneumonia around the world. It is confused that
{$<$ italic$>$ACE2$<$}/{italic$>$} is widely expressed
across a variety of organs \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Pathak:2021:DBC,
author = "Yadunath Pathak and Piyush Kumar Shukla and K. V.
Arya",
title = "Deep Bidirectional Classification Model for {COVID-19}
Disease Infected Patients",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1234--1241",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3009859",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3009859",
abstract = "In December of 2019, a novel coronavirus (COVID-19)
appeared in Wuhan city, China and has been reported in
many countries with millions of people infected within
only four months. Chest computed Tomography (CT) has
proven to be a useful supplement to \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lv:2021:DPR,
author = "Jinxiong Lv and Shikui Tu and Lei Xu",
title = "Detection of Phenotype-Related Mutations of {COVID-19}
via the Whole Genomic Data",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1242--1249",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3049836",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3049836",
abstract = "The coronavirus disease 2019 (COVID-19) epidemic
continues to spread rapidly around the world and nearly
20 millions people are infected. This paper utilises
both single-locus analysis and joint-SNPs analysis for
detection of significant single nucleotide \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xiao:2021:DWS,
author = "Ming Xiao and Guangdi Liu and Jianghang Xie and Zichun
Dai and Zihao Wei and Ziyao Ren and Jun Yu and Le
Zhang",
title = "{2019nCoVAS}: Developing the Web Service for Epidemic
Transmission Prediction, Genome Analysis, and
Psychological Stress Assessment for {2019-nCoV}",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1250--1261",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3049617",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3049617",
abstract = "Since the COVID-19 epidemic is still expanding around
the world and poses a serious threat to human life and
health, it is necessary for us to carry out epidemic
transmission prediction, whole genome sequence
analysis, and public psychological stress \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Selvaraj:2021:HTS,
author = "Chandrabose Selvaraj and Dhurvas Chandrasekaran Dinesh
and Umesh Panwar and Evzen Boura and Sanjeev Kumar
Singh",
title = "High-Throughput Screening and Quantum Mechanics for
Identifying Potent Inhibitors Against {Mac1} Domain of
{SARS-CoV-2} {Nsp3}",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1262--1270",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3037136",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3037136",
abstract = "SARS-CoV-2 encodes the Mac1 domain within the large
nonstructural protein 3 (Nsp3), which has an
ADP-ribosylhydrolase activity conserved in other
coronaviruses. The enzymatic activity of Mac1 makes it
an essential virulence factor for the pathogenicity of
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chakrabarty:2021:NBA,
author = "Broto Chakrabarty and Dibyajyoti Das and
Gopalakrishnan Bulusu and Arijit Roy",
title = "Network-Based Analysis of Fatal Comorbidities of
{COVID-19} and Potential Therapeutics",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1271--1280",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3075299",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3075299",
abstract = "COVID-19 is a highly contagious disease caused by the
severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2). The case-fatality rate is significantly
higher in older patients and those with diabetes,
cancer or cardiovascular disorders. The human
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jokinen:2021:DBS,
author = "Elmeri M. Jokinen and Krishnasamy Gopinath and Sami T.
Kurkinen and Olli T. Pentik{\"a}inen",
title = "Detection of Binding Sites on {SARS-CoV-2} Spike
Protein Receptor-Binding Domain by Molecular Dynamics
Simulations in Mixed Solvents",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1281--1289",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3076259",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3076259",
abstract = "The novel SARS-CoV-2 uses ACE2 (Angiotensin-Converting
Enzyme 2) receptor as an entry point. Insights on S
protein receptor-binding domain (RBD) interaction with
ACE2 receptor and drug repurposing has accelerated drug
discovery for the novel SARS-CoV-2 \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhou:2021:LDS,
author = "Deshan Zhou and Shaoliang Peng and Dong-Qing Wei and
Wu Zhong and Yutao Dou and Xiaolan Xie",
title = "{LUNAR}: Drug Screening for Novel Coronavirus Based on
Representation Learning Graph Convolutional Network",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1290--1298",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3085972",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3085972",
abstract = "An outbreak of COVID-19 that began in late 2019 was
caused by a novel coronavirus(SARS-CoV-2). It has
become a global pandemic. As of June 9, 2020, it has
infected nearly 7 million people and killed more than
400,000, but there is no specific drug. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Kaushik:2021:CAC,
author = "Aman Chandra Kaushik and Aamir Mehmood and Gurudeeban
Selvaraj and Xiaofeng Dai and Yi Pan and Dong-Qing
Wei",
title = "{CoronaPep}: an Anti-Coronavirus Peptide Generation
Tool",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1299--1304",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3064630",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3064630",
abstract = "The novel coronavirus (COVID-19) infections have
adopted the shape of a global pandemic now, demanding
an urgent vaccine design. The current work reports
contriving an anti-coronavirus peptide scanner tool to
discern anti-coronavirus targets in the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yu:2021:KEM,
author = "Zhimiao Yu and Jiarui Lu and Yuan Jin and Yang Yang",
title = "{KenDTI}: an Ensemble Model for Predicting Drug-Target
Interaction by Integrating Multi-Source Information",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1305--1314",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3074401",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3074401",
abstract = "The identification of drug-target interactions (DTIs)
is an essential step in the process of drug discovery.
As experimental validation suffers from high cost and
low success rate, various computational models have
been exploited to infer potential DTIs. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Hu:2021:CNN,
author = "ShanShan Hu and DeNan Xia and Benyue Su and Peng Chen
and Bing Wang and Jinyan Li",
title = "A Convolutional Neural Network System to Discriminate
Drug-Target Interactions",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1315--1324",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2940187",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2940187",
abstract = "Biological targets are most commonly proteins such as
enzymes, ion channels, and receptors. They are anything
within a living organism to bind with some other
entities (like an endogenous ligand or a drug),
resulting in change in their behaviors or \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2021:FFB,
author = "Enze Liu and Jin Li and Garrett H. Kinnebrew and
Pengyue Zhang and Yan Zhang and Lijun Cheng and Lang
Li",
title = "A Fast and Furious {Bayesian} Network and Its
Application of Identifying Colon Cancer to Liver
Metastasis Gene Regulatory Networks",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1325--1335",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2944826",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2944826",
abstract = "Bayesian networks is a powerful method for identifying
causal relationships among variables. However, as the
network size increases, the time complexity of
searching the optimal structure grows exponentially. We
proposed a novel search algorithm --- Fast \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Carrillo-Cabada:2021:GEM,
author = "Hector Carrillo-Cabada and Jeremy Benson and Asghar M.
Razavi and Brianna Mulligan and Michel A. Cuendet and
Harel Weinstein and Michela Taufer and Trilce Estrada",
title = "A Graphic Encoding Method for Quantitative
Classification of Protein Structure and Representation
of Conformational Changes",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1336--1349",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2945291",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2945291",
abstract = "In order to successfully predict a proteins function
throughout its trajectory, in addition to uncovering
changes in its conformational state, it is necessary to
employ techniques that maintain its 3D information
while performing at scale. We extend a \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:LGC,
author = "Aiying Zhang and Jian Fang and Wenxing Hu and Vince D.
Calhoun and Yu-Ping Wang",
title = "A Latent {Gaussian} Copula Model for Mixed Data
Analysis in Brain Imaging Genetics",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1350--1360",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2950904",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2950904",
abstract = "Recent advances in imaging genetics make it possible
to combine different types of data including medical
images like functional magnetic resonance imaging
(fMRI) and genetic data like single nucleotide
polymorphisms (SNPs) for comprehensive diagnosis of
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Angadi:2021:NWC,
author = "Ulavappa B. Angadi and Krishna Kumar Chaturvedi and
Sudhir Srivastava and Anil Rai",
title = "A Novel Way of Comparing Protein {$3$D} Structure
Using Graph Partitioning Approach",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1361--1368",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2938948",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2938948",
abstract = "Alignment and comparison of protein 3D structures is
an important and fundamental task in structural biology
to study evolutionary, functional and structural
relatedness among proteins. Since two decades, the
research on protein structure alignment has \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2021:AMA,
author = "Liguang Wang and Yujia Wang and Yi Fu and Yunge Gao
and Jiawei Du and Chen Yang and Jianxiao Liu",
title = "{AFSBN}: a Method of Artificial Fish Swarm Optimizing
{Bayesian} Network for Epistasis Detection",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1369--1383",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2949780",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2949780",
abstract = "How to mine the interaction between SNPs (namely
epistasis) efficiently and accurately must be
considered when to tackle the complexity of underlying
biological mechanisms. In order to overcome the defect
of low learning efficiency and local optimal, this
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2021:CCD,
author = "Ziying Yang and Guoxian Yu and Maozu Guo and Jiantao
Yu and Xiangliang Zhang and Jun Wang",
title = "{CDPath}: Cooperative Driver Pathways Discovery Using
Integer Linear Programming and {Markov} Clustering",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1384--1395",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2945029",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2945029",
abstract = "Discovering driver pathways is an essential task to
understand the pathogenesis of cancer and to design
precise treatments for cancer patients. Increasing
evidences have been indicating that multiple pathways
often function cooperatively in \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2021:CDD,
author = "Bing Nan Li and Xinle Wang and Rong Wang and Teng Zhou
and Rongke Gao and Edward J. Ciaccio and Peter H.
Green",
title = "Celiac Disease Detection From Videocapsule Endoscopy
Images Using Strip Principal Component Analysis",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1396--1404",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2953701",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2953701",
abstract = "The purpose of this study was to implement principal
component analysis (PCA) on videocapsule endoscopy (VE)
images to develop a new computerized tool for celiac
disease recognition. Three PCA algorithms were
implemented for feature extraction and sparse
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ge:2021:CMB,
author = "Yan Ge and Philipp Rosendahl and Claudio Dur{\'a}n and
Nicole T{\"o}pfner and Sara Ciucci and Jochen Guck and
Carlo Vittorio Cannistraci",
title = "Cell Mechanics Based Computational Classification of
Red Blood Cells Via Machine Intelligence Applied to
Morpho-Rheological Markers",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1405--1415",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2945762",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2945762",
abstract = "Despite fluorescent cell-labelling being widely
employed in biomedical studies, some of its drawbacks
are inevitable, with unsuitable fluorescent probes or
probes inducing a functional change being the main
limitations. Consequently, the demand for and
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:CES,
author = "Wenju Zhang and Zhewei Liang and Xin Chen and Lei Xin
and Baozhen Shan and Zhigang Luo and Ming Li",
title = "{ChimST}: an Efficient Spectral Library Search Tool
for Peptide Identification from Chimeric Spectra in
Data-Dependent Acquisition",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1416--1425",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2945954",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2945954",
abstract = "Accurate and sensitive identification of peptides from
MS/MS spectra is a very challenging problem in
computational shotgun proteomics. To tackle this
problem, spectral library search has been one of the
competitive solutions. However, most existing
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Maheshwari:2021:CVA,
author = "Sidharth Maheshwari and Venkateshwarlu Y. Gudur and
Rishad Shafik and Ian Wilson and Alex Yakovlev and Amit
Acharyya",
title = "{CORAL}: Verification-Aware {OpenCL} Based Read Mapper
for Heterogeneous Systems",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1426--1438",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2943856",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2943856",
abstract = "Genomics has the potential to transform medicine from
reactive to a personalized, predictive, preventive, and
participatory (P4) form. Being a Big Data application
with continuously increasing rate of data production,
the computational costs of genomics \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2021:CSP,
author = "Yingwen Zhao and Jun Wang and Maozu Guo and Xiangliang
Zhang and Guoxian Yu",
title = "Cross-Species Protein Function Prediction with
Asynchronous-Random Walk",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1439--1450",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2943342",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2943342",
abstract = "Protein function prediction is a fundamental task in
the post-genomic era. Available functional annotations
of proteins are incomplete and the annotations of two
homologous species are complementary to each other.
However, how to effectively leverage {$<$}. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:DNG,
author = "Jun Zhang and Qingcai Chen and Bin Liu",
title = "{DeepDRBP-2L}: a New Genome Annotation Predictor for
Identifying {DNA}-Binding Proteins and {RNA}-Binding
Proteins Using Convolutional Neural Network and Long
Short-Term Memory",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1451--1463",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2952338",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2952338",
abstract = "DNA-binding proteins (DBPs) and RNA-binding proteins
(RBPs) are two kinds of crucial proteins, which are
associated with various cellule activities and some
important diseases. Accurate identification of DBPs and
RBPs facilitate both theoretical research \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Peng:2021:DPS,
author = "Shaoliang Peng and Yaning Yang and Wei Liu and Fei Li
and Xiangke Liao",
title = "Discriminant Projection Shared Dictionary Learning for
Classification of Tumors Using Gene Expression Data",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1464--1473",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2950209",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2950209",
abstract = "With a variety of tumor subtypes, personalized
treatments need to identify the subtype of a tumor as
accurately as possible. The development of DNA
microarrays provides an opportunity to predict tumor
classification. One strategy is to use gene expression
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ozden:2021:DDR,
author = "Furkan Ozden and Metin Can Siper and Necmi Acarsoy and
Tugrulcan Elmas and Bryan Marty and Xinjian Qi and A.
Ercument Cicek",
title = "{DORMAN}: {Database of Reconstructed MetAbolic
Networks}",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1474--1480",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2944905",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2944905",
abstract = "Genome-scale reconstructed metabolic networks have
provided an organism specific understanding of cellular
processes and their relations to phenotype. As they are
deemed essential to study metabolism, the number of
organisms with reconstructed metabolic \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Hu:2021:DDS,
author = "Yue Hu and Jin-Xing Liu and Ying-Lian Gao and Junliang
Shang",
title = "{DSTPCA}: Double-Sparse Constrained Tensor Principal
Component Analysis Method for Feature Selection",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1481--1491",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2943459",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2943459",
abstract = "The identification of differentially expressed genes
plays an increasingly important role biologically.
Therefore, the feature selection approach has attracted
much attention in the field of bioinformatics. The most
popular method of principal component \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liao:2021:ESF,
author = "Xingyu Liao and Min Li and Junwei Luo and You Zou and
Fang-Xiang Wu and Yi-Pan and Feng Luo and Jianxin
Wang",
title = "{EPGA-SC}: a Framework for {\em de novo\/} Assembly of
Single-Cell Sequencing Reads",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1492--1503",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2945761",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2945761",
abstract = "Assembling genomes from single-cell sequencing data is
essential for single-cell studies. However, single-cell
assemblies are challenging due to (i) the highly
non-uniform read coverage and (ii) the elevated levels
of sequencing errors and chimeric reads. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Javadi:2021:FSM,
author = "Afsaneh Javadi and Faezeh Keighobadi and Vahab
Nekoukar and Marzieh Ebrahimi",
title = "Finite-Set Model Predictive Control of Melanoma Cancer
Treatment Using Signaling Pathway Inhibitor of Cancer
Stem Cell",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1504--1511",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2940658",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2940658",
abstract = "Drug delivery is one of the most important issues in
the treatment of cancer and surviving the patient.
Recently, with a combination of mathematical models of
the tumor growth and control theory, optimal drug
delivery can be planned, individually. The \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2021:GSH,
author = "Chao Wang and Lei Gong and Shiming Lei and Haijie Fang
and Xi Li and Aili Wang and Xuehai Zhou",
title = "{GenSeq+}: a Scalable High-Performance Accelerator for
Genome Sequencing",
journal = j-TCBB,
volume = "18",
number = "4",
pages = "1512--1523",
month = jul # "\slash " # aug,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2947059",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:54 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2947059",
abstract = "Genome sequencing is one of the most challenging
problems in computational biology and bioinformatics.
As a traditional algorithm, the string match meets a
challenge with the development of the massive volume of
data because of gene sequencing. Surveys \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xia:2021:GEA,
author = "Kaijian Xia and Yizhang Jiang and Yudong Zhang and Wen
Si",
title = "Guest Editorial: Advanced Machine-Learning Methods for
Brain-Machine Interfacing or Brain-Computer
Interfacing",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1643--1644",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3078145",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3078145",
abstract = "The seven papers in this special section focus on
advanced machine learning methods for brain machine
interfacing. Particular emphasis is on novel theories
and methods using transfer learning and deep learning
proposed for Brain-Machine Interfacing (BMI) \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gu:2021:EBB,
author = "Xiaotong Gu and Zehong Cao and Alireza Jolfaei and
Peng Xu and Dongrui Wu and Tzyy-Ping Jung and Chin-Teng
Lin",
title = "{EEG}-Based Brain-Computer Interfaces {(BCIs)}: a
Survey of Recent Studies on Signal Sensing Technologies
and Computational Intelligence Approaches and Their
Applications",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1645--1666",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3052811",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3052811",
abstract = "Brain-Computer interfaces (BCIs) enhance the
capability of human brain activities to interact with
the environment. Recent advancements in technology and
machine learning algorithms have increased interest in
electroencephalographic (EEG)-based BCI \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:ESR,
author = "Yuanpeng Zhang and Ziyuan Zhou and Wenjie Pan and
Heming Bai and Wei Liu and Li Wang and Chuang Lin",
title = "Epilepsy Signal Recognition Using Online Transfer
{TSK} Fuzzy Classifier Underlying Classification Error
and Joint Distribution Consensus Regularization",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1667--1678",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3002562",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3002562",
abstract = "In this study, an online transfer TSK fuzzy classifier
O-T-TSK-FC is proposed for recognizing epilepsy
signals. Compared with most of the existing transfer
learning models, O-T-TSK-FC enjoys its merits from the
following three aspects: (1) Since different \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gu:2021:HDS,
author = "Xiaoqing Gu and Cong Zhang and TongGuang Ni",
title = "A Hierarchical Discriminative Sparse Representation
Classifier for {EEG} Signal Detection",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1679--1687",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3006699",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3006699",
abstract = "Classification of electroencephalogram (EEG) signal
data plays a vital role in epilepsy detection. Recently
sparse representation-based classification (SRC)
methods have achieved the good performance in EEG
signal automatic detection, by which the EEG \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lv:2021:AML,
author = "Zhihan Lv and Liang Qiao and Qingjun Wang and
Francesco Piccialli",
title = "Advanced Machine-Learning Methods for Brain-Computer
Interfacing",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1688--1698",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3010014",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3010014",
abstract = "The brain-computer interface (BCI) connects the brain
and the external world through an information
transmission channel by interpreting the physiological
information of the brain during thinking activities.
The effective classification of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lin:2021:MSA,
author = "Bo Lin and Shuiguang Deng and Honghao Gao and Jianwei
Yin",
title = "A Multi-Scale Activity Transition Network for Data
Translation in {EEG} Signals Decoding",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1699--1709",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3024228",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3024228",
abstract = "Electroencephalogram (EEG) is a non-invasive
collection method for brain signals. It has broad
prospects in brain-computer interface (BCI)
applications. Recent advances have shown the
effectiveness of the widely used convolutional neural
network (CNN) in \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2021:SIE,
author = "Shuaiqi Liu and Xu Wang and Ling Zhao and Jie Zhao and
Qi Xin and Shui-Hua Wang",
title = "Subject-Independent Emotion Recognition of {EEG}
Signals Based on Dynamic Empirical Convolutional Neural
Network",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1710--1721",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3018137",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3018137",
abstract = "Affective computing is one of the key technologies to
achieve advanced brain-machine interfacing. It is
increasingly concerning research orientation in the
field of artificial intelligence. Emotion recognition
is closely related to affective computing. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Huang:2021:PHI,
author = "Chenxi Huang and Yutian Xiao and Gaowei Xu",
title = "Predicting Human Intention-Behavior Through {EEG}
Signal Analysis Using Multi-Scale {CNN}",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1722--1729",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3039834",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3039834",
abstract = "At present, the application of Electroencephalogram
(EEG) signal classification to human intention-behavior
prediction has become a hot topic in the brain computer
interface (BCI) research field. In recent studies, the
introduction of convolutional neural \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Huang:2021:GES,
author = "De-Shuang Huang and Vitoantonio Bevilacqua and M.
Michael Gromiha",
title = "Guest Editorial for Special Section on the {15th
International Conference on Intelligent Computing
(ICIC)}",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1730--1732",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3065722",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3065722",
abstract = "The papers in this special section were presented at
the Fifteenth International Conference on Intelligent
Computing (ICIC) held on August 3-6, 2019, in Nanchang,
Jiangxi Province, China.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zheng:2021:MIL,
author = "Kai Zheng and Zhu-Hong You and Lei Wang and Yi-Ran Li
and Ji-Ren Zhou and Hai-Tao Zeng",
title = "{MISSIM}: an Incremental Learning-Based Model With
Applications to the Prediction of {miRNA}-Disease
Association",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1733--1742",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3013837",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3013837",
abstract = "In the past few years, the prediction models have
shown remarkable performance in most biological
correlation prediction tasks. These tasks traditionally
use a fixed dataset, and the model, once trained, is
deployed as is. These models often encounter \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:PTD,
author = "Qinhu Zhang and Dailun Wang and Kyungsook Han and
De-Shuang Huang",
title = "Predicting {TF-DNA} Binding Motifs from {ChIP}-seq
Datasets Using the Bag-Based Classifier Combined With a
Multi-Fold Learning Scheme",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1743--1751",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3025007",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3025007",
abstract = "The rapid development of high-throughput sequencing
technology provides unique opportunities for studying
of transcription factor binding sites, but also brings
new computational challenges. Recently, a series of
discriminative motif discovery (DMD) \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wu:2021:EPE,
author = "Hongjie Wu and Huajing Ling and Lei Gao and Qiming Fu
and Weizhong Lu and Yijie Ding and Min Jiang and Haiou
Li",
title = "Empirical Potential Energy Function Toward ab Initio
Folding {G} Protein-Coupled Receptors",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1752--1762",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3008014",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3008014",
abstract = "Approximately 40&\#x2013;50 percent of all drugs
targets are G protein-coupled receptors (GPCRs).
Three-dimensional structure of GPCRs is important to
probe their biophysical and biochemical functions and
their pharmaceutical applications. Lacking reliable
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2021:DPM,
author = "Yue Liu and Shu-Lin Wang and Jun-Feng Zhang and Wei
Zhang and Su Zhou and Wen Li",
title = "{DMFMDA}: Prediction of Microbe-Disease Associations
Based on Deep Matrix Factorization Using {Bayesian}
Personalized Ranking",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1763--1772",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3018138",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3018138",
abstract = "Identifying the microbe-disease associations is
conducive to understanding the pathogenesis of disease
from the perspective of microbe. In this paper, we
propose a deep matrix factorization prediction model
(DMFMDA) based on deep neural network. First,
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lu:2021:FFR,
author = "Xinguo Lu and Xinyu Wang and Li Ding and Jinxin Li and
Yan Gao and Keren He",
title = "{frDriver}: a Functional Region Driver Identification
for Protein Sequence",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1773--1783",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3020096",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3020096",
abstract = "Identifying cancer drivers is a crucial challenge to
explain the underlying mechanisms of cancer
development. There are many methods to identify cancer
drivers based on the single mutation site or the entire
gene. But they ignore a large number of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xie:2021:PEG,
author = "Jiang Xie and Chang Zhao and Jiamin Sun and Jiaxin Li
and Fuzhang Yang and Jiao Wang and Qing Nie",
title = "Prediction of Essential Genes in Comparison States
Using Machine Learning",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1784--1792",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3027392",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3027392",
abstract = "Identifying essential genes in comparison states (EGS)
is vital to understanding cell differentiation,
performing drug discovery, and identifying disease
causes. Here, we present a machine learning method
termed Prediction of Essential Genes in Comparison
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:MSC,
author = "Qinhu Zhang and Wenbo Yu and Kyungsook Han and Asoke
K. Nandi and De-Shuang Huang",
title = "Multi-Scale Capsule Network for Predicting
{DNA-Protein} Binding Sites",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1793--1800",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3025579",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3025579",
abstract = "Discovering DNA-protein binding sites, also known as
motif discovery, is the foundation for further analysis
of transcription factors (TFs). Deep learning
algorithms such as convolutional neural networks (CNN)
have been introduced to motif discovery task \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2021:DLB,
author = "Menglu Li and Yanan Wang and Fuyi Li and Yun Zhao and
Mengya Liu and Sijia Zhang and Yannan Bin and A. Ian
Smith and Geoffrey I. Webb and Jian Li and Jiangning
Song and Junfeng Xia",
title = "A Deep Learning-Based Method for Identification of
Bacteriophage-Host Interaction",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1801--1810",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3017386",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3017386",
abstract = "Multi-drug resistance (MDR) has become one of the
greatest threats to human health worldwide, and novel
treatment methods of infections caused by MDR bacteria
are urgently needed. Phage therapy is a promising
alternative to solve this problem, to which \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yuan:2021:LOF,
author = "Xiguo Yuan and Junping Li and Jun Bai and Jianing Xi",
title = "A Local Outlier Factor-Based Detection of Copy Number
Variations From {NGS} Data",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1811--1820",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2961886",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2961886",
abstract = "Copy number variation (CNV) is a major type of genomic
structural variations that play an important role in
human disorders. Next generation sequencing (NGS) has
fueled the advancement in algorithm design to detect
CNVs at base-pair resolution. However, \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Huang:2021:NCP,
author = "Hai-Hui Huang and Yong Liang",
title = "A Novel {Cox} Proportional Hazards Model for
High-Dimensional Genomic Data in Cancer Prognosis",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1821--1830",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2961667",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2961667",
abstract = "The Cox proportional hazards model is a popular method
to study the connection between feature and survival
time. Because of the high-dimensionality of genomic
data, existing Cox models trained on any specific
dataset often generalize poorly to other \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:AEP,
author = "Jinhao Zhang and Zehua Zhang and Lianrong Pu and Jijun
Tang and Fei Guo",
title = "{AIEpred}: an Ensemble Predictive Model of Classifier
Chain to Identify Anti-Inflammatory Peptides",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1831--1840",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2968419",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2968419",
abstract = "Anti-inflammatory peptides (AIEs) have recently
emerged as promising therapeutic agent for treatment of
various inflammatory diseases, such as rheumatoid
arthritis and Alzheimer&\#x2019;s disease. Therefore,
detecting the correlation between amino acid \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Qian:2021:AFS,
author = "Ying Qian and Yu Zhang and Jiongmin Zhang",
title = "Alignment-Free Sequence Comparison With Multiple $k$
Values",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1841--1849",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2955081",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2955081",
abstract = "Alignment-free sequence comparison approaches have
become increasingly popular in computational biology,
because alignment-based approaches are inefficient to
process large-scale datasets. Still, there is no way to
determine the optimal value of the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xu:2021:ACT,
author = "Benlian Xu and Jian Shi and Mingli Lu and Jinliang
Cong and Ling Wang and Brett Nener",
title = "An Automated Cell Tracking Approach With
Multi-{Bernoulli} Filtering and Ant Colony Labor
Division",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1850--1863",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2954502",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2954502",
abstract = "In this article, we take as inspiration the labor
division into scouts and workers in an ant colony and
propose a novel approach for automated cell tracking in
the framework of multi-Bernoulli random finite sets. To
approximate the Bernoulli parameter \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Kumar:2021:AAP,
author = "Gautam Kumar and Rajnish Kumar and Manoj Kumar Pal and
Nilotpal Pramanik and Tapobrata Lahiri and Ankita Gupta
and Saket Pandey",
title = "{APT}: an Automated Probe Tracker From Gene Expression
Data",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1864--1874",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2958345",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2958345",
abstract = "Out of currently available semi-automatic tools for
detecting diagnostic probes relevant to a
pathophysiological condition, ArrayMining and GEO2R of
NCBI are most popular. The shortcomings of ArrayMining
and GEO2R are that both tools list the probes
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:BSM,
author = "Yue Zhang and Chunfang Zheng and Sindeed Islam and
Yong-Min Kim and David Sankoff",
title = "Branching Out to Speciation in a Model of
Fractionation: The {Malvaceae}",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1875--1884",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2955649",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2955649",
abstract = "Fractionation is the genome-wide process of losing one
gene per duplicate pair following whole genome doubling
(WGD). An important type of evidence for duplicate gene
loss is the frequency distribution of similarities
between paralogous gene pairs in a \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ghasemnejad:2021:DNM,
author = "Atefeh Ghasemnejad and Samira Bazmara and Mahsa
Shadmani and Kamran Pooshang Bagheri",
title = "Designing a New Multi-Epitope Pertussis Vaccine with
Highly Population Coverage Based on a Novel Sequence
and Structural Filtration Algorithm",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1885--1892",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2958803",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2958803",
abstract = "Pertussis vaccine is produced from physicochemically
inactivated toxin for many years. Recent advancements
in immunoinformatics [N. Tomar and R. K. De,
``Immunoinformatics: an integrated scenario,'' {\em
Immunology}, vol. 131, no. 2,. \ldots{}]",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yuan:2021:ENS,
author = "Xiguo Yuan and Xiangyan Xu and Haiyong Zhao and Junbo
Duan",
title = "{ERINS}: Novel Sequence Insertion Detection by
Constructing an Extended Reference",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1893--1901",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2954315",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2954315",
abstract = "Next generation sequencing technology has led to the
development of methods for the detection of novel
sequence insertions (nsINS). Multiple signatures from
short reads are usually extracted to improve nsINS
detection performance. However, \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Kumari:2021:EMD,
author = "Chetna Kumari and Muhammad Abulaish and Naidu
Subbarao",
title = "Exploring Molecular Descriptors and Fingerprints to
Predict {mTOR} Kinase Inhibitors using Machine Learning
Techniques",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1902--1913",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2964203",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2964203",
abstract = "Mammalian Target of Rapamycin (mTOR) is a Ser/Thr
protein kinase, and its role is integral to the
autophagy pathway in cancer. Targeting mTOR for
therapeutic interventions in cancer through autophagy
pathway is challenging due to the dual roles of
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2021:FSB,
author = "Jiaojiao Chen and Jianbo Jiao and Shengfeng He and
Guoqiang Han and Jing Qin",
title = "Few-Shot Breast Cancer Metastases Classification via
Unsupervised Cell Ranking",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1914--1923",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2960019",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2960019",
abstract = "Tumor metastases detection is of great importance for
the treatment of breast cancer patients. Various CNN
(convolutional neural network) based methods get
excellent performance in object detection/segmentation.
However, the detection of metastases in \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Medhat:2021:FRA,
author = "Belal Medhat and Ahmed Shawish",
title = "{FLR}: a Revolutionary Alignment-Free Similarity
Analysis Methodology for {DNA}-Sequences",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1924--1936",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2967385",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2967385",
abstract = "This paper introduces a novel alignment-free sequence
analysis methodology. Its main idea is based on
introducing a new representation of the DNA-Sequence.
This representation breaks the dependency between the
DNA bases that exist in the traditional \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jia:2021:FPL,
author = "Cangzhi Jia and Meng Zhang and Cunshuo Fan and Fuyi Li
and Jiangning Song",
title = "Formator: Predicting {Lysine} Formylation Sites Based
on the Most Distant Undersampling and Safe-Level
Synthetic Minority Oversampling",
journal = j-TCBB,
volume = "18",
number = "5",
pages = "1937--1945",
month = sep # "\slash " # oct,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2957758",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:56 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2957758",
abstract = "Lysine formylation is a reversible type of protein
post-translational modification and has been found to
be involved in a myriad of biological processes,
including modulation of chromatin conformation and gene
expression in histones and other nuclear \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Aluru:2021:ENE,
author = "Srinivas Aluru",
title = "Editorial: From the New {Editor-in-Chief}",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2058--2058",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3108133",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3108133",
abstract = "I am delighted to have the opportunity to serve you as
the next editor-in-chief of this prestigious journal,
beginning August 2021. I regard the IEEE/ACM
Transactions on Computational Biology and
Bioinformatics (TCBB) as the premier journal devoted to
the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Martin-Vide:2021:ACBb,
author = "Carlos Mart{\'\i}n-Vide and Miguel A.
Vega-Rodr{\'\i}guez",
title = "{{\booktitle{Algorithms for Computational Biology}}}:
Seventh Edition",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2059--2060",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3099915",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3099915",
abstract = "The papers in this special section were presented at
the Seventh International Conference on Algorithms for
Computational Biology, AlCoB 2020, held in Missoula,
Montana on November 8-11, 2021 merged with AlCoB 2021.
The conference was organized by the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Debnath:2021:FOC,
author = "Tathagata Debnath and Mingzhou Song",
title = "Fast Optimal Circular Clustering and Applications on
Round Genomes",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2061--2071",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3077573",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3077573",
abstract = "Round genomes are found in bacteria, plant
chloroplasts, and mitochondria. Genetic or epigenetic
marks can present biologically interesting clusters
along a circular genome. The circular data clustering
problem groups \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liang:2021:STA,
author = "Shaoheng Liang and Qingnan Liang and Rui Chen and Ken
Chen",
title = "Stratified Test Accurately Identifies Differentially
Expressed Genes Under Batch Effects in Single-Cell
Data",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2072--2079",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3094650",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3094650",
abstract = "Analyzing single-cell sequencing data from large
cohorts is challenging. Discrepancies across
experiments and differences among participants often
lead to omissions and false discoveries in
differentially expressed genes. We find that the Van
Elteren test,. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Oliveira:2021:SPI,
author = "Andre Rodrigues Oliveira and G{\'e}raldine Jean and
Guillaume Fertin and Klairton Lima Brito and Ulisses
Dias and Zanoni Dias",
title = "Sorting Permutations by Intergenic Operations",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2080--2093",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3077418",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3077418",
abstract = "Genome Rearrangements are events that affect large
stretches of genomes during evolution. Many
mathematical models have been used to estimate the
evolutionary distance between two genomes based on
genome rearrangements. However, most of them focused on
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Siqueira:2021:HGR,
author = "Gabriel Siqueira and Klairton Lima Brito and Ulisses
Dias and Zanoni Dias",
title = "Heuristics for Genome Rearrangement Distance With
Replicated Genes",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2094--2108",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3095021",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3095021",
abstract = "In comparative genomics, one goal is to find
similarities between genomes of different organisms.
Comparisons using genome features like genes, gene
order, and regulatory sequences are carried out with
this purpose in mind. Genome rearrangements are
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Steinauer:2021:CMG,
author = "Nickolas Steinauer and Kevin Zhang and Chun Guo and
Jinsong Zhang",
title = "Computational Modeling of Gene-Specific
Transcriptional Repression, Activation and Chromatin
Interactions in Leukemogenesis by {LASSO}-Regularized
Logistic Regression",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2109--2122",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3078128",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3078128",
abstract = "Many physiological and pathological pathways are
dependent on gene-specific on/off regulation of
transcription. Some genes are repressed, while others
are activated. Although many previous studies have
analyzed the mechanisms of gene-specific repression
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:GEA,
author = "Louxin Zhang and Shaoliang Peng and Yi-Ping Phoebe
Chen and David Sankoff and Guoliang Li and Hong-Yu
Zhang",
title = "Guest Editorial for the {17th Asia Pacific
Bioinformatics Conference}",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2123--2124",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3099948",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3099948",
abstract = "The eight papers in this special section were
presented at the 17th Asia Pacific Bioinformatics
Conference (APBC), which was held in Wuhan, China,
14-16 January 2019.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gorecki:2021:UDD,
author = "Pawe{\l} G{\'o}recki and Oliver Eulenstein and Jerzy
Tiuryn",
title = "The Unconstrained Diameters of the Duplication-Loss
Cost and the Loss Cost",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2125--2135",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2919617",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2919617",
abstract = "Tree reconciliation costs are a popular choice to
account for the discordance between the evolutionary
history of a gene family (i.e., a gene tree), and the
species tree through which this family has evolved.
This discordance is accounted for by the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Grueter:2021:RRS,
author = "Melissa Grueter and Kalani Duran and Ramya Ramalingam
and Ran Libeskind-Hadas",
title = "Reconciliation Reconsidered: In Search of a Most
Representative Reconciliation in the
Duplication-Transfer-Loss Model",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2136--2143",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2942015",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2942015",
abstract = "Maximum parsimony reconciliation is a fundamental
technique for studying the evolutionary histories of
pairs of entities such as genes and species, parasites
and hosts, and species and their biogeographical
habitats. In these contexts, reconciliation is
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Du:2021:MOR,
author = "Haoxing Du and Yi Sheng Ong and Marina Knittel and
Ross Mawhorter and Nuo Liu and Gianluca Gross and Reiko
Tojo and Ran Libeskind-Hadas and Yi-Chieh Wu",
title = "Multiple Optimal Reconciliations Under the
Duplication-Loss-Coalescence Model",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2144--2156",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2922337",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2922337",
abstract = "Gene trees can differ from species trees due to a
variety of biological phenomena, the most prevalent
being gene duplication, horizontal gene transfer, gene
loss, and coalescence. To explain topological
incongruence between the two trees, researchers
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Guo:2021:DMS,
author = "Hongzhe Guo and Yilei Fu and Yan Gao and Junyi Li and
Yadong Wang and Bo Liu",
title = "{deGSM}: Memory Scalable Construction Of Large Scale
{de Bruijn} Graph",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2157--2166",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2913932",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2913932",
abstract = "The de Bruijn graph, a fundamental data structure to
represent and organize genome sequence, plays important
roles in various kinds of sequence analysis tasks. With
the rapid development of HTS data and ever-increasing
number of assembled genomes, there \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Su:2021:TOD,
author = "Cui Su and Jun Pang and Soumya Paul",
title = "Towards Optimal Decomposition of {Boolean} Networks",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2167--2176",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2914051",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2914051",
abstract = "In recent years, great efforts have been made to
analyze biological systems to understand the long-run
behaviors. As a well-established formalism for
modelling real-life biological systems, Boolean
networks (BNs) allow their representation and analysis
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Hartmann:2021:SSP,
author = "Tom Hartmann and Max Bannach and Martin Middendorf",
title = "Sorting Signed Permutations by Inverse Tandem
Duplication Random Losses",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2177--2188",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2917198",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2917198",
abstract = "Gene order evolution of unichromosomal genomes, for
example mitochondrial genomes, has been modelled mostly
by four major types of genome rearrangements:
inversions, transpositions, inverse transpositions, and
tandem duplication random losses. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Le:2021:PFB,
author = "Nguyen Quoc Khanh Le and Binh P. Nguyen",
title = "Prediction of {FMN} Binding Sites in Electron
Transport Chains Based on {$2$-D} {CNN} and {PSSM}
Profiles",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2189--2197",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2019.2932416",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2019.2932416",
abstract = "Flavin mono-nucleotides (FMNs) are cofactors that hold
responsibility for carrying and transferring electrons
in the electron transport chain stage of cellular
respiration. Without being facilitated by FMNs, energy
production is stagnant due to the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ozgul:2021:CDC,
author = "Ozan Firat {\"O}zg{\"u}l and Batuhan Bardak and Mehmet
Tan",
title = "A Convolutional Deep Clustering Framework for Gene
Expression Time Series",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2198--2207",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2988985",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2988985",
abstract = "The functional or regulatory processes within the cell
are explicitly governed by the expression levels of a
subset of its genes. Gene expression time series
captures activities of individual genes over time and
aids revealing underlying cellular \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2021:DLF,
author = "Fuhao Zhang and Hong Song and Min Zeng and Fang-Xiang
Wu and Yaohang Li and Yi Pan and Min Li",
title = "A Deep Learning Framework for Gene Ontology
Annotations With Sequence- and Network-Based
Information",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2208--2217",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2968882",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2968882",
abstract = "Knowledge of protein functions plays an important role
in biology and medicine. With the rapid development of
high-throughput technologies, a huge number of proteins
have been discovered. However, there are a great number
of proteins without functional \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2021:MCI,
author = "Zilu Wang and Qinghui Hong and Xiaoping Wang",
title = "A Memristive Circuit Implementation of Eyes State
Detection in Fatigue Driving Based on Biological Long
Short-Term Memory Rule",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2218--2229",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2974944",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2974944",
abstract = "Biological long short-term memory (B-LSTM) can
effectively help human process all kinds of received
information. In this work, a memristive B-LSTM circuit
which mimics a conversion from short-term memory to
long-term memory is proposed. That is, the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{daSilva:2021:NFS,
author = "Pablo Nascimento da Silva and Alexandre Plastino and
Fabio Fabris and Alex A. Freitas",
title = "A Novel Feature Selection Method for Uncertain
Features: an Application to the Prediction of
{Pro-\slash} Anti-Longevity Genes",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2230--2238",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2988450",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2988450",
abstract = "Understanding the ageing process is a very challenging
problem for biologists. To help in this task, there has
been a growing use of classification methods (from
machine learning) to learn models that predict whether
a gene influences the process of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2021:AMI,
author = "Dong Li and Zhisong Pan and Guyu Hu and Graham
Anderson and Shan He",
title = "Active Module Identification From Multilayer Weighted
Gene Co-Expression Networks: a Continuous Optimization
Approach",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2239--2248",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2970400",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2970400",
abstract = "Searching for active modules, i.e., regions showing
striking changes in molecular activity in biological
networks is important to reveal regulatory and
signaling mechanisms of biological systems. Most
existing active modules identification methods are
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Fang:2021:EBB,
author = "Qiong Fang and Dewei Su and Wilfred Ng and Jianlin
Feng",
title = "An Effective Biclustering-Based Framework for
Identifying Cell Subpopulations From {scRNA}-seq Data",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2249--2260",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2979717",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2979717",
abstract = "The advent of single-cell RNA sequencing (scRNA-seq)
techniques opens up new opportunities for studying the
cell-specific changes in the transcriptomic data. An
important research problem related with scRNA-seq data
analysis is to identify cell \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2021:CMC,
author = "Junyi Chen and Junhui Hou and Ka-Chun Wong",
title = "Categorical Matrix Completion With Active Learning for
High-Throughput Screening",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2261--2270",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2982142",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2982142",
abstract = "The recent advances in wet-lab automation enable
high-throughput experiments to be conducted seamlessly.
In particular, the exhaustive enumeration of all
possible conditions is always involved in
high-throughput screening. Nonetheless, such a
screening \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ogundijo:2021:CIT,
author = "Oyetunji E. Ogundijo and Kaiyi Zhu and Xiaodong Wang
and Dimitris Anastassiou",
title = "Characterizing Intra-Tumor Heterogeneity From Somatic
Mutations Without Copy-Neutral Assumption",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2271--2280",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2973635",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2973635",
abstract = "Bulk samples of the same patient are heterogeneous in
nature, comprising of different subpopulations
(subclones) of cancer cells. Cells in a tumor subclone
are characterized by unique mutational genotype
profile. Resolving tumor heterogeneity by \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yuan:2021:CMC,
author = "Shaoxun Yuan and Haitao Li and Jiansheng Wu and Xiao
Sun",
title = "Classification of Mild Cognitive Impairment With
Multimodal Data Using Both Labeled and Unlabeled
Samples",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2281--2290",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3053061",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3053061",
abstract = "Mild Cognitive Impairment (MCI) is a preclinical stage
of Alzheimer&\#x0027;s Disease (AD) and is clinical
heterogeneity. The classification of MCI is crucial for
the early diagnosis and treatment of AD. In this study,
we investigated the potential of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lyne:2021:CPA,
author = "Anne-Marie Lyne and Le{\"\i}la Peri{\'e}",
title = "Comparing Phylogenetic Approaches to Reconstructing
Cell Lineage From Microsatellites With Missing Data",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2291--2301",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2992813",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2992813",
abstract = "Due to the imperfect fidelity of DNA replication,
somatic cells acquire DNA mutations at each division
which record their lineage history. Microsatellites,
tandem repeats of DNA nucleotide motifs, mutate more
frequently than other genomic regions and by \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Blanco:2021:CAS,
author = "Guillermo Blanco and Borja S{\'a}nchez and Lorena Ruiz
and Florentino Fdez-Riverola and Abelardo Margolles and
An{\'a}lia Louren{\c{c}}o",
title = "Computational Approach to the Systematic Prediction of
Glycolytic Abilities: Looking Into Human Microbiota",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2302--2313",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2978461",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2978461",
abstract = "Glycoside hydrolases are responsible for the enzymatic
deconstruction of complex carbohydrates. Most of the
families are known to conserve the catalytic machinery
and molecular mechanisms. This work introduces a new
method to predict glycolytic abilities \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Willing:2021:CII,
author = "Eyla Willing and Jens Stoye and Mar{\'\i}lia D. V.
Braga",
title = "Computing the Inversion-Indel Distance",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2314--2326",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2988950",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2988950",
abstract = "The inversion distance, that is the distance between
two unichromosomal genomes with the same content
allowing only inversions of DNA segments, can be
exactly computed thanks to a pioneering approach of
Hannenhalli and Pevzner from 1995. In 2000, El-.
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ji:2021:CNN,
author = "Junzhong Ji and Yao Yao",
title = "Convolutional Neural Network With Graphical Lasso to
Extract Sparse Topological Features for Brain Disease
Classification",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2327--2338",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2989315",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2989315",
abstract = "The functional connectivity provides new insights into
the mechanisms of the human brain at network-level,
which has been proved to be an effective biomarker for
brain disease classification. Recently, machine
learning methods have played an important \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Occhipinti:2021:DEM,
author = "Annalisa Occhipinti and Youssef Hamadi and Hillel
Kugler and Christoph M. Wintersteiger and Boyan
Yordanov and Claudio Angione",
title = "Discovering Essential Multiple Gene Effects Through
Large Scale Optimization: an Application to Human
Cancer Metabolism",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2339--2352",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2973386",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2973386",
abstract = "Computational modelling of metabolic processes has
proven to be a useful approach to formulate our
knowledge and improve our understanding of core
biochemical systems that are crucial to maintaining
cellular functions. Towards understanding the broader
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zeng:2021:DDL,
author = "Min Zeng and Chengqian Lu and Zhihui Fei and
Fang-Xiang Wu and Yaohang Li and Jianxin Wang and Min
Li",
title = "{DMFLDA}: a Deep Learning Framework for Predicting
{lncRNA}--Disease Associations",
journal = j-TCBB,
volume = "18",
number = "6",
pages = "2353--2363",
month = nov # "\slash " # dec,
year = "2021",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2983958",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Apr 20 07:14:58 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2983958",
abstract = "A growing amount of evidence suggests that long
non-coding RNAs (lncRNAs) play important roles in the
regulation of biological processes in many human
diseases. However, the number of experimentally
verified lncRNA-disease associations is very limited.
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sun:2022:E,
author = "Sunny Sun and Yi-Ping Phoebe Chen",
title = "Editorial",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "1--2",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3089195",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3089195",
abstract = "Presents the introductory editorial for this issue of
the publication.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bi:2022:RSF,
author = "Hui Bi and Shumei Cao and Hanying Yan and Zhongyi
Jiang and Jun Zhang and Ling Zou",
title = "Resting State Functional Connectivity Analysis During
General Anesthesia: a High-Density {EEG} Study",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "3--13",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3091000",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3091000",
abstract = "The depth of anesthesia monitoring is helpful to guide
administrations of general anesthetics during surgical
procedures,however, the conventional 2-4 channels
electroencephalogram (EEG) derived monitors have their
limitations in monitoring conscious \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Craveur:2022:SB,
author = "Pierrick Craveur and Tarun J. Narwani and
Narayanaswamy Srinivasan and Jean-Christophe Gelly and
Joseph Rebehmed and Alexandre G. de Brevern",
title = "Shaking the $ \beta $-Bulges",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "14--18",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3088444",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3088444",
abstract = "&\#x03B2;-bulges are irregularities inside the
&\#x03B2;-sheets. They represent more than 3 percent of
the protein residues, i.e., they are as frequent as
{3.$<$ sub$>$10$<$}/{sub$>$} helices. In terms of
evolution, &\#x03B2;-bulges are not more conserved than
any other \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Paul:2022:NFS,
author = "Madhusudan Paul and Ashish Anand",
title = "A New Family of Similarity Measures for Scoring
Confidence of Protein Interactions Using Gene
Ontology",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "19--30",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3083150",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3083150",
abstract = "The large-scale protein-protein interaction (PPI) data
has the potential to play a significant role in the
endeavor of understanding cellular processes. However,
the presence of a considerable fraction of false
positives is a bottleneck in realizing this \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Huang:2022:CDV,
author = "Runhua Huang and Chengchuang Lin and Aihua Yin and
Hanbiao Chen and Li Guo and Gansen Zhao and Xiaomao Fan
and Shuangyin Li and Jinji Yang",
title = "A Clinical Dataset and Various Baselines for
Chromosome Instance Segmentation",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "31--39",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3089507",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3089507",
abstract = "{$<$ italic$>$Background}:{$<$}/{italic$>$} In
medicine, chromosome karyotyping analysis plays a
crucial role in prenatal diagnosis for diagnosing
whether a fetus has severe defects or genetic diseases.
However, chromosome instance segmentation is the most
critical \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Qian:2022:PSC,
author = "Ying Qian and Xuelian Li and Qian Zhang and Jiongmin
Zhang",
title = "\pkg{SPP-CPI}: Predicting Compound--Protein
Interactions Based On Neural Networks",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "40--47",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3084397",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3084397",
abstract = "Identifying interactions between compound and protein
is a substantial part of the drug discovery process.
Accurate prediction of interaction relationships can
greatly reduce the time of drug development. The
uniqueness of our method lies in three aspects:.
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2022:PMA,
author = "Jin Zhao and Haodi Feng and Daming Zhu and Yu Lin",
title = "\pkg{MultiTrans}: an Algorithm for Path Extraction
Through Mixed Integer Linear Programming for
Transcriptome Assembly",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "48--56",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3083277",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3083277",
abstract = "Recent advances in RNA-seq technology have made
identification of expressed genes affordable, and thus
boosting repaid development of transcriptomic studies.
Transcriptome assembly, reconstructing all expressed
transcripts from RNA-seq reads, is an \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wickramarachchi:2022:PGR,
author = "Anuradha Wickramarachchi and Yu Lin",
title = "\pkg{GraphPlas}: Refined Classification of Plasmid
Sequences Using Assembly Graphs",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "57--67",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3082915",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3082915",
abstract = "Plasmids are extra-chromosomal genetic materials with
important markers that affect the function and
behaviour of the microorganisms supporting their
environmental adaptations. Hence the identification and
recovery of such plasmid sequences from \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lu:2022:LSS,
author = "Shuai Lu and Yuguang Li and Fei Wang and Xiaofei Nan
and Shoutao Zhang",
title = "Leveraging Sequential and Spatial Neighbors
Information by Using {CNNs} Linked With {GCNs} for
Paratope Prediction",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "68--74",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3083001",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3083001",
abstract = "Antibodies consisting of variable and constant
regions, are a special type of proteins playing a vital
role in immune system of the vertebrate. They have the
remarkable ability to bind a large range of diverse
antigens with extraordinary affinity and \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2022:AMI,
author = "Tzu-Hsien Yang",
title = "An Aggregation Method to Identify the {RNA}
Meta-Stable Secondary Structure and its Functionally
Interpretable Structure Ensemble",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "75--86",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3082396",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3082396",
abstract = "RNA can provide vital cellular functions through its
secondary or tertiary structure. Due to the
low-throughput nature of experimental approaches,
studies on RNA structures mainly resort to
computational methods. However, current existing tools
fail to \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Nguyen:2022:EED,
author = "Trinh-Trung-Duong Nguyen and The-Anh Tran and
Nguyen-Quoc-Khanh Le and Dinh-Minh Pham and Yu-Yen Ou",
title = "An Extensive Examination of Discovering
5-Methylcytosine Sites in Genome-Wide {DNA} Promoters
Using Machine Learning Based Approaches",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "87--94",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3082184",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3082184",
abstract = "It is well-known that the major reason for the rapid
proliferation of cancer cells are the hypomethylation
of the whole cancer genome and the hypermethylation of
the promoter of particular tumor suppressor genes.
Locating 5-methylcytosine (5mC) sites in \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{DiCamillo:2022:GED,
author = "Barbara {Di Camillo} and Giuseppe Nicosia",
title = "Guest Editorial: Deep Learning For Genomics",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "95--96",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3080094",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3080094",
abstract = "The six papers in this special section focus on deep
learning for genomics. Thanks to the development of
high-throughput technologies, a huge amount of omics
data is being produced relative to DNA and RNA
sequences and (and also) abundance at individual
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Clauwaert:2022:NTN,
author = "Jim Clauwaert and Willem Waegeman",
title = "Novel Transformer Networks for Improved Sequence
Labeling in genomics",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "97--106",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3035021",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3035021",
abstract = "In genomics, a wide range of machine learning
methodologies have been investigated to annotate
biological sequences for positions of interest such as
transcription start sites, translation initiation
sites, methylation sites, splice sites and promoter
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2022:ECS,
author = "Zhong Chen and Wensheng Zhang and Hongwen Deng and Kun
Zhang",
title = "Effective Cancer Subtype and Stage Prediction via
Dropfeature-{DNNs}",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "107--120",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3058941",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3058941",
abstract = "Precise cancer subtype and/or stage prediction is
instrumental for cancer diagnosis, treatment and
management. However, most of the existing methods based
on genomic profiles suffer from issues such as
overfitting, high computational complexity and
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cristovao:2022:IDL,
author = "Francisco Cristovao and Silvia Cascianelli and Arif
Canakoglu and Mark Carman and Luca Nanni and Pietro
Pinoli and Marco Masseroli",
title = "Investigating Deep Learning Based Breast Cancer
Subtyping Using Pan-Cancer and Multi-Omic Data",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "121--134",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3042309",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3042309",
abstract = "Breast Cancer comprises multiple subtypes implicated
in prognosis. Existing stratification methods rely on
the expression quantification of small gene sets. Next
Generation Sequencing promises large amounts of omic
data in the next years. In this scenario,. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Viaud:2022:RLC,
author = "Gautier Viaud and Prasanna Mayilvahanan and Paul-Henry
Courn{\`e}de",
title = "Representation Learning for the Clustering of
Multi-Omics Data",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "135--145",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3060340",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3060340",
abstract = "The integration of several sources of data for the
identification of subtypes of diseases has gained
attention over the past few years. The heterogeneity
and the high dimensions of the data sets calls for an
adequate representation of the data. We \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Nguyen:2022:GCN,
author = "Tuan Nguyen and Giang T. T. Nguyen and Thin Nguyen and
Duc-Hau Le",
title = "Graph Convolutional Networks for Drug Response
Prediction",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "146--154",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3060430",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3060430",
abstract = "{$<$ italic$>$Background$<$}/{italic$>$}: Drug
response prediction is an important problem in
computational personalized medicine. Many
machine-learning-based methods, especially deep
learning-based ones, have been proposed for this task.
However, these methods often \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Mahapatra:2022:DNN,
author = "Satyajit Mahapatra and Vivek Raj Gupta and Sitanshu
Sekhar Sahu and Ganapati Panda",
title = "Deep Neural Network and Extreme Gradient Boosting
Based Hybrid Classifier for Improved Prediction of
Protein-Protein Interaction",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "155--165",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3061300",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3061300",
abstract = "Understanding the behavioral process of life and
disease-causing mechanism, knowledge regarding
protein-protein interactions (PPI) is essential. In
this paper, a novel hybrid approach combining deep
neural network (DNN) and extreme gradient boosting
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cai:2022:GEIa,
author = "Zhipeng Cai and Min Li and Pavel Skums",
title = "{Guest Editors}' Introduction to the Special Section
on Bioinformatics Research and Applications",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "166--167",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3123699",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3123699",
abstract = "The papers in this special section were presented at
the 15th International Symposium on Bioinformatics
Research and Applications (ISBRA 2019), which was held
at Technical University of Catalonia, Barcelona, Spain
on June 3-6, 2019.",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yan:2022:PDD,
author = "Cheng Yan and Guihua Duan and Yayan Zhang and
Fang-Xiang Wu and Yi Pan and Jianxin Wang",
title = "Predicting Drug-Drug Interactions Based on Integrated
Similarity and Semi-Supervised Learning",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "168--179",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2988018",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2988018",
abstract = "A drug-drug interaction (DDI) is defined as an
association between two drugs where the pharmacological
effects of a drug are influenced by another drug.
Positive DDIs can usually improve the therapeutic
effects of patients, but negative DDIs cause the
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2022:CAD,
author = "Fengpan Zhao and Pavel Skums and Alex Zelikovsky and
Eric L. Sevigny and Monica Haavisto Swahn and Sheryl M.
Strasser and Yan Huang and Yubao Wu",
title = "Computational Approaches to Detect Illicit Drug Ads
and Find Vendor Communities Within Social Media
Platforms",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "180--191",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2978476",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2978476",
abstract = "The opioid abuse epidemic represents a major public
health threat to global populations. The role social
media may play in facilitating illicit drug trade is
largely unknown due to limited research. However, it is
known that social media use among adults \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Acharya:2022:RFP,
author = "Sudipta Acharya and Laizhong Cui and Yi Pan",
title = "A Refined 3-in-1 Fused Protein Similarity Measure:
Application in Threshold-Free Hub Detection",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "192--206",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2973563",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2973563",
abstract = "An exhaustive literature survey shows that finding
protein/gene similarity is an important step towards
solving widespread bioinformatics problems, such as
predicting protein-protein interactions, analyzing
Protein-Protein Interaction Networks (PPINs),
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cai:2022:GEIb,
author = "Zhipeng Cai and Giri Narasimhan and Pavel Skums",
title = "{Guest Editors}' Introduction to the Special Section
on Bioinformatics Research and Applications",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "207--208",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3121736",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3121736",
abstract = "The papers in this special section were presented at
the 16th International Symposium on Bioinformatics
Research and Applications (ISBRA 2020), which was held
virtually, on December 1-4, 2020. The ISBRA symposium
provides a forum for the exchange of ideas \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gao:2022:MSC,
author = "Shan Gao and Renmin Han and Xiangrui Zeng and Zhiyong
Liu and Min Xu and Fa Zhang",
title = "Macromolecules Structural Classification With a {$3$D}
Dilated Dense Network in Cryo-Electron Tomography",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "209--219",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3065986",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3065986",
abstract = "Cryo-electron tomography, combined with subtomogram
averaging (STA), can reveal three-dimensional (3D)
macromolecule structures in the near-native state from
cells and other biological samples. In STA, to get a
high-resolution 3D view of macromolecule \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Storato:2022:PKD,
author = "Davide Storato and Matteo Comin",
title = "\pkg{K2Mem}: Discovering Discriminative {$K$}-mers
From Sequencing Data for Metagenomic Reads
Classification",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "220--229",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3117406",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3117406",
abstract = "The major problem when analyzing a metagenomic sample
is to taxonomically annotate its reads to identify the
species they contain. Most of the methods currently
available focus on the classification of reads using a
set of reference genomes and their k-. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Dhar:2022:PTT,
author = "Saurav Dhar and Chengchen Zhang and Ion I. Mandoiu and
Mukul S. Bansal",
title = "\pkg{TNet}: Transmission Network Inference Using
Within-Host Strain Diversity and its Application to
Geographical Tracking of {COVID-19} Spread",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "230--242",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3096455",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3096455",
abstract = "The inference of disease transmission networks is an
important problem in epidemiology. One popular approach
for building transmission networks is to reconstruct a
phylogenetic tree using sequences from disease strains
sampled from infected hosts and \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2022:PED,
author = "Jun Wang and Huiling Zhang and Wei Ren and Maozu Guo
and Guoxian Yu",
title = "\pkg{EpiMC}: Detecting Epistatic Interactions Using
Multiple Clusterings",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "243--254",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3080462",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3080462",
abstract = "Detecting single nucleotide polymorphisms (SNPs)
interactions is crucial to identify susceptibility
genes associated with complex human diseases in
genome-wide association studies. Clustering-based
approaches are widely used in reducing search space and
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{V:2022:HIN,
author = "Sunil Kumar P. V. and Adheeba Thahsin and Manju M. and
Gopakumar G.",
title = "A Heterogeneous Information Network Model for Long
Non-Coding {RNA} Function Prediction",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "255--266",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3000518",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3000518",
abstract = "Exciting information on the functional roles played by
long non-coding RNA (lncRNA) has drawn substantial
research attention these days. With the advent of
techniques such as RNA-Seq, thousands of lncRNAs are
identified in very short time spans. However,
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2022:NED,
author = "Hansheng Li and JianPing Li and Yuxin Kang and Chunbao
Wang and Feihong Liu and Wenli Hui and Qirong Bo and
Lei Cui and Jun Feng and Lin Yang",
title = "A Novel Encoding and Decoding Calibration Guiding
Pathway for Pathological Image Analysis",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "267--274",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3023467",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3023467",
abstract = "Diagnostic pathology is the foundation and gold
standard for identifying carcinomas, and the accurate
quantification of pathological images can provide
objective clues for pathologists to make more
convincing diagnosis. Recently, the encoder-decoder
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gull:2022:PAS,
author = "Sadaf Gull and Fayyaz Minhas",
title = "\pkg{AMP$_0$}: Species-Specific Prediction of
Anti-microbial Peptides Using Zero and Few Shot
Learning",
journal = j-TCBB,
volume = "19",
number = "1",
pages = "275--283",
month = jan,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.2999399",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Mar 4 08:29:18 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.2999399",
abstract = "Evolution of drug-resistant microbial species is one
of the major challenges to global health. Development
of new antimicrobial treatments such as antimicrobial
peptides needs to be accelerated to combat this threat.
However, the discovery of novel \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Guzzi:2022:EDL,
author = "Pietro Hiram Guzzi and Marinka Zitnik",
title = "Editorial Deep Learning and Graph Embeddings for
Network Biology",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "653--654",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3110279",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3110279",
abstract = "This special issue contains a multitude of
high-quality manuscripts that cover a broad range of
applications supporting the need to discuss and foster
these advances in a systematic way, provide practical
tools for practitioners, and describe new \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2022:IIM,
author = "Jin Li and Jingru Wang and Hao Lv and Zhuoxuan Zhang
and Zaixia Wang",
title = "{IMCHGAN}: Inductive Matrix Completion With
Heterogeneous Graph Attention Networks for Drug-Target
Interactions Prediction",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "655--665",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3088614",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3088614",
abstract = "Identification of targets among known drugs plays an
important role in drug repurposing and discovery.
Computational approaches for prediction of
drug&\#x2013;target interactions (DTIs)are highly
desired in comparison to traditional biological
experiments \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Pan:2022:IPS,
author = "Xiaoyong Pan and Lei Chen and Min Liu and Zhibin Niu
and Tao Huang and Yu-Dong Cai",
title = "Identifying Protein Subcellular Locations With
Embeddings-Based {\tt node2loc}",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "666--675",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3080386",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3080386",
abstract = "Identifying protein subcellular locations is an
important topic in protein function prediction.
Interacting proteins may share similar locations. Thus,
it is imperative to infer protein subcellular locations
by taking protein-protein interactions (PPIs).
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{KC:2022:PBI,
author = "Kishan KC and Rui Li and Feng Cui and Anne R. Haake",
title = "Predicting Biomedical Interactions With Higher-Order
Graph Convolutional Networks",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "676--687",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3059415",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3059415",
abstract = "Biomedical interaction networks have incredible
potential to be useful in the prediction of
biologically meaningful interactions, identification of
network biomarkers of disease, and the discovery of
putative drug targets. Recently, graph neural networks
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lei:2022:IMD,
author = "Xiujuan Lei and Jiaojiao Tie and Yi Pan",
title = "Inferring Metabolite-Disease Association Using Graph
Convolutional Networks",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "688--698",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3065562",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3065562",
abstract = "As is well known, biological experiments are
time-consuming and laborious, so there is absolutely no
doubt that developing an effective computational model
will help solve these problems. Most of computational
models rely on the biological similarity and \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gao:2022:PSC,
author = "Jianliang Gao and Tengfei Lyu and Fan Xiong and
Jianxin Wang and Weimao Ke and Zhao Li",
title = "Predicting the Survival of Cancer Patients With
Multimodal Graph Neural Network",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "699--709",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3083566",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3083566",
abstract = "In recent years, cancer patients survival prediction
holds important significance for worldwide health
problems, and has gained many researchers attention in
medical information communities. Cancer patients
survival prediction can be seen the \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Nguyen:2022:IMG,
author = "Giang T. T. Nguyen and Hoa D. Vu and Duc-Hau Le",
title = "Integrating Molecular Graph Data of Drugs and Multiple
-Omic Data of Cell Lines for Drug Response Prediction",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "710--717",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3096960",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3096960",
abstract = "Previous studies have either learned drug&\#x0027;s
features from their string or numeric representations,
which are not natural forms of drugs, or only used
genomic data of cell lines for the drug response
prediction problem. Here, we proposed a deep \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Nguyen:2022:GEF,
author = "Tri Minh Nguyen and Thin Nguyen and Thao Minh Le and
Truyen Tran",
title = "{GEFA}: Early Fusion Approach in Drug-Target Affinity
Prediction",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "718--728",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3094217",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3094217",
abstract = "Predicting the interaction between a compound and a
target is crucial for rapid drug repurposing. Deep
learning has been successfully applied in drug-target
affinity (DTA)problem. However, previous deep
learning-based methods ignore modeling the direct
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Manipur:2022:NGE,
author = "Ichcha Manipur and Mario Manzo and Ilaria Granata and
Maurizio Giordano and Lucia Maddalena and Mario R.
Guarracino",
title = "{Netpro2vec}: a Graph Embedding Framework for
Biomedical Applications",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "729--740",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3078089",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3078089",
abstract = "The ever-increasing importance of structured data in
different applications, especially in the biomedical
field, has driven the need for reducing its complexity
through projections into a more manageable space. The
latest methods for learning features on \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ren:2022:PDM,
author = "Yuanfang Ren and Aisharjya Sarkar and Pierangelo
Veltri and Ahmet Ay and Alin Dobra and Tamer Kahveci",
title = "Pattern Discovery in Multilayer Networks",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "741--752",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3105001",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3105001",
abstract = "{$<$ italic$>$Motivation}:{$<$}/{italic$>$} In
bioinformatics, complex cellular modeling and behavior
simulation to identify significant molecular
interactions is considered a relevant problem.
Traditional methods model such complex systems using
single and binary \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Shen:2022:DLM,
author = "Zhen Shen and Qinhu Zhang and Kyungsook Han and
De-Shuang Huang",
title = "A Deep Learning Model for {RNA}-Protein Binding
Preference Prediction Based on Hierarchical {LSTM} and
Attention Network",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "753--762",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3007544",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3007544",
abstract = "Attention mechanism has the ability to find important
information in the sequence. The regions of the RNA
sequence that can bind to proteins are more important
than those that cannot bind to proteins. Neither
conventional methods nor deep learning-based \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gao:2022:NMB,
author = "Zhen Gao and Yu-Tian Wang and Qing-Wen Wu and Lei Li
and Jian-Cheng Ni and Chun-Hou Zheng",
title = "A New Method Based on Matrix Completion and
Non-Negative Matrix Factorization for Predicting
Disease-Associated {miRNAs}",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "763--772",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3027444",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3027444",
abstract = "Numerous studies have shown that microRNAs are
associated with the occurrence and development of human
diseases. Thus, studying disease-associated miRNAs is
significantly valuable to the prevention, diagnosis and
treatment of diseases. In this paper, we \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Mallick:2022:NGT,
author = "Koushik Mallick and Saurav Mallik and Sanghamitra
Bandyopadhyay and Sikim Chakraborty",
title = "A Novel Graph Topology-Based {GO}-Similarity Measure
for Signature Detection From Multi-Omics Data and its
Application to Other Problems",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "773--785",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3020537",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3020537",
abstract = "Large scale multi-omics data analysis and signature
prediction have been a topic of interest in the last
two decades. While various traditional
clustering/correlation-based methods have been
proposed, but the overall prediction is not always
satisfactory. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Huang:2022:NMC,
author = "Xin Huang and Zhenqian Liao and Bing Liu and Fengmei
Tao and Benzhe Su and Xiaohui Lin",
title = "A Novel Method for Constructing Classification Models
by Combining Different Biomarker Patterns",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "786--794",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3022076",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3022076",
abstract = "Different biomarker patterns, such as those of
molecular biomarkers and ratio biomarkers, have their
own merits in clinical applications. In this study, a
novel machine learning method used in biomedical data
analysis for constructing classification \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Feng:2022:III,
author = "Shi-Hao Feng and Chun-Qiu Xia and Pei-Dong Zhang and
Hong-Bin Shen",
title = "{{\em Ab-Initio}} Membrane Protein Amphipathic Helix
Structure Prediction Using Deep Neural Networks",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "795--805",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3029274",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3029274",
abstract = "Amphipathic helix (AH)features the segregation of
polar and nonpolar residues and plays important roles
in many membrane-associated biological processes
through interacting with both the lipid and the soluble
phases. Although the AH structure has been \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Giang:2022:FBA,
author = "Trinh Van Giang and Tatsuya Akutsu and Kunihiko
Hiraishi",
title = "An {FVS}-Based Approach to Attractor Detection in
Asynchronous Random {Boolean} Networks",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "806--818",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3028862",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3028862",
abstract = "Boolean networks (BNs)play a crucial role in modeling
and analyzing biological systems. One of the central
issues in the analysis of BNs is attractor detection,
i.e., identification of all possible attractors. This
problem becomes more challenging for \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2022:CSD,
author = "Yiding Zhang and Lyujie Chen and Shao Li",
title = "{CIPHER-SC}: Disease-Gene Association Inference Using
Graph Convolution on a Context-Aware Network With
Single-Cell Data",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "819--829",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3017547",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3017547",
abstract = "Inference of disease-gene associations helps unravel
the pathogenesis of diseases and contributes to the
treatment. Although many machine learning-based methods
have been developed to predict causative genes,
accurate association inference remains \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sarkar:2022:DPR,
author = "Aisharjya Sarkar and Prabhat Mishra and Tamer
Kahveci",
title = "Data Perturbation and Recovery of Time Series Gene
Expression Data",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "830--842",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3058342",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3058342",
abstract = "Cells, in order to regulate their activities, process
transcripts by controlling which genes to transcribe
and by what amount. The transcription level of genes
often change over time. Rate of change of gene
transcription varies between genes. It can even
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bhatt:2022:DKG,
author = "Sachin Bhatt and Prithvi Singh and Archana Sharma and
Arpita Rai and Ravins Dohare and Shweta Sankhwar and
Akash Sharma and Mansoor Ali Syed",
title = "Deciphering Key Genes and {miRNAs} Associated With
Hepatocellular Carcinoma via Network-Based Approach",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "843--853",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3016781",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3016781",
abstract = "Hepatocellular carcinoma (HCC)is a common type of
liver cancer and has a high mortality world-widely. The
diagnosis, prognoses, and therapeutics are very poor
due to the unclear molecular mechanism of progression
of the disease. To unveil the molecular \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Su:2022:DRL,
author = "Yu-Ting Su and Yao Lu and Mei Chen and An-An Liu",
title = "Deep Reinforcement Learning-Based Progressive Sequence
Saliency Discovery Network for Mitosis Detection In
Time-Lapse Phase-Contrast Microscopy Images",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "854--865",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3019042",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3019042",
abstract = "Mitosis detection plays an important role in the
analysis of cell status and behavior and is therefore
widely utilized in many biological research and medical
applications. In this article, we propose a deep
reinforcement learning-based progressive \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cao:2022:DUA,
author = "Ben Cao and Xue Ii and Xiaokang Zhang and Bin Wang and
Qiang Zhang and Xiaopeng Wei",
title = "Designing Uncorrelated Address Constrain for {DNA}
Storage by {DMVO} Algorithm",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "866--877",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3011582",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3011582",
abstract = "At present, huge amounts of data are being produced
every second, a situation that will gradually overwhelm
current storage technology. DNA is a storage medium
that features high storage density and long-term
stability and is now considered to be a \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Guan:2022:DDA,
author = "Boxin Guan and Yuhai Zhao and Ying Yin and Yuan Li",
title = "Detecting Disease-Associated {SNP--SNP} Interactions
Using Progressive Screening Memetic Algorithm",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "878--887",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3019256",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3019256",
abstract = "Hundreds of thousands of single nucleotide
polymorphisms (SNPs)are currently available for
genome-wide association study (GWAS). Detecting
disease-associated SNP-SNP interactions is considered
an important way to capture the underlying genetic
causes of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bae:2022:DPA,
author = "Ho Bae and Seonwoo Min and Hyun-Soo Choi and Sungroh
Yoon",
title = "{DNA} Privacy: Analyzing Malicious {DNA} Sequences
Using Deep Neural Networks",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "888--898",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3017191",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3017191",
abstract = "Recent advances in next-generation sequencing
technologies have led to the successful insertion of
video information into DNA using synthesized
oligonucleotides. Several attempts have been made to
embed larger data into living organisms. This process
of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Kim:2022:DTC,
author = "Seonho Kim and Juntae Yoon",
title = "Dual Triggered Correspondence Topic ({DTCT}) model for
{MeSH} annotation",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "899--911",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3016355",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3016355",
abstract = "Accurate Medical Subject Headings (MeSH) annotation is
an important issue for researchers in terms of
effective information retrieval and knowledge discovery
in the biomedical literature. We have developed a
powerful dual triggered correspondence topic \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ponte-Fernandez:2022:EEM,
author = "Christian Ponte-Fern{\'a}ndez and Jorge
Gonz{\'a}lez-Dom{\'\i}nguez and Antonio
Carvajal-Rodr{\'\i}guez and Mar{\'\i}a J. Mart{\'\i}n",
title = "Evaluation of Existing Methods for High-Order
Epistasis Detection",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "912--926",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3030312",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3030312",
abstract = "Finding epistatic interactions among loci when
expressing a phenotype is a widely employed strategy to
understand the genetic architecture of complex traits
in GWAS. The abundance of methods dedicated to the same
purpose, however, makes it increasingly \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chowdhury:2022:GAD,
author = "Tapan Chowdhury and Susanta Chakraborty and Argha
Nandan",
title = "{GPU} Accelerated Drug Application on Signaling
Pathways Containing Multiple Faults Using {Boolean}
Networks",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "927--939",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3014172",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3014172",
abstract = "Cell growth is governed by the flow of information
from growth factors to transcription factors. This flow
involves protein-protein interactions known as a
signaling pathway, which triggers the cell division.
The biological network in the presence of \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Seabolt:2022:FGP,
author = "Edward E. Seabolt and Gowri Nayar and Harsha
Krishnareddy and Akshay Agarwal and Kristen L. Beck and
Ignacio Terrizzano and Eser Kandogan and Mark Kunitomi
and Mary Roth and Vandana Mukherjee and James H.
Kaufman",
title = "Functional Genomics Platform, a Cloud-Based Platform
for Studying Microbial Life at Scale",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "940--952",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3021231",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3021231",
abstract = "The rapid growth in biological sequence data is
revolutionizing our understanding of genotypic
diversity and challenging conventional approaches to
informatics. With the increasing availability of
genomic data, traditional bioinformatic tools require
\ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2022:IGS,
author = "Fei Wang and Yulian Ding and Xiujuan Lei and Bo Liao
and Fang-Xiang Wu",
title = "Identifying Gene Signatures for Cancer Drug
Repositioning Based on Sample Clustering",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "953--965",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3019781",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3019781",
abstract = "Drug repositioning is an important approach for drug
discovery. Computational drug repositioning approaches
typically use a gene signature to represent a
particular disease and connect the gene signature with
drug perturbation profiles. Although disease \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Alazmi:2022:IIB,
author = "Meshari Alazmi and Olaa Motwalli",
title = "Immuno-Informatics Based Peptides: an Approach for
Vaccine Development Against Outer Membrane Proteins of
\bioname{Pseudomonas} Genus",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "966--973",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3032651",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3032651",
abstract = "Pseudomonas genus is among the top nosocomial
pathogens known to date. Being highly opportunistic,
members of pseudomonas genus are most commonly
connected with nosocomial infections of urinary tract
and ventilator-associated pneumonia. \ldots{}",
acknowledgement = ack-nhfb,
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Newaz:2022:IDA,
author = "Khalique Newaz and Tijana Milenkovi{\'c}",
title = "Inference of a Dynamic Aging-related Biological
Subnetwork via Network Propagation",
journal = j-TCBB,
volume = "19",
number = "2",
pages = "974--988",
month = mar,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3022767",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3022767",
abstract = "Gene expression (GE)data capture valuable condition-specific information (`condition'; can mean a biological process, disease stage, age, patient, etc.)However, GE analyses ignore physical interactions between gene products, i.e., proteins. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
bibdate = "Fri Apr 15 06:41:04 MDT 2022",
}
@Article{Tsui:2022:E,
author = "Stephen Kwok-Wing Tsui",
title = "Editorial",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1255--1256",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3155845",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3155845",
abstract = "This special section of IEEE/ACM Transactions on
Computational Biology and Bioinformatics (TCBB) is a
collection of papers presented at the 18th Asia Pacific
Bioinformatics Conference (APBC2020), which was a
virtual conference held in Seoul, Korea, from
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2022:PPP,
author = "Jiongmin Zhang and Man Zhu and Ying Qian",
title = "{protein2vec}: Predicting Protein--Protein
Interactions Based on {LSTM}",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1257--1266",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3003941",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3003941",
abstract = "The semantic similarity of gene ontology (GO) terms is
widely used to predict protein-protein interactions
(PPIs). The traditional semantic similarity measures
are based mainly on manually crafted features, which
may ignore some important hidden \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Park:2022:NAD,
author = "Byungkyu Park and Wook Lee and Kyungsook Han",
title = "A New Approach to Deriving Prognostic Gene Pairs From
Cancer Patient-Specific Gene Correlation Networks",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1267--1276",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3017209",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3017209",
abstract = "Many of the known prognostic gene signatures for
cancer are individual genes or combination of genes,
found by the analysis of microarray data. However, many
of the known cancer signatures are less predictive than
random gene expression signatures, and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2022:DDC,
author = "Yanbo Li and Yu Lin",
title = "{DCHap}: a Divide-and-Conquer Haplotype Phasing
Algorithm for Third-Generation Sequences",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1277--1284",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3005673",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3005673",
abstract = "The development of DNA sequencing technologies makes
it possible to obtain reads originated from both copies
of a chromosome (two parental chromosomes, or
haplotypes) of a single individual. Reconstruction of
both haplotypes (i.e., haplotype phasing) \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lin:2022:CNA,
author = "Chengchuang Lin and Gansen Zhao and Zhirong Yang and
Aihua Yin and Xinming Wang and Li Guo and Hanbiao Chen
and Zhaohui Ma and Lei Zhao and Haoyu Luo and Tianxing
Wang and Bichao Ding and Xiongwen Pang and Qiren Chen",
title = "{CIR-Net}: Automatic Classification of Human
Chromosome Based on {Inception-ResNet} Architecture",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1285--1293",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3003445",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3003445",
abstract = "Background: In medicine, karyotyping chromosomes is
important for medical diagnostics, drug development,
and biomedical research. Unfortunately, chromosome
karyotyping is usually done by skilled cytologists
manually, which requires \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sang:2022:SEB,
author = "Shengtian Sang and Xiaoxia Liu and Xiaoyu Chen and Di
Zhao",
title = "A Scalable Embedding Based Neural Network Method for
Discovering Knowledge From Biomedical Literature",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1294--1301",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3003947",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3003947",
abstract = "Nowadays, the amount of biomedical literatures is
growing at an explosive speed, and much useful
knowledge is yet undiscovered in the literature.
Classical information retrieval techniques allow to
access explicit information from a given collection of
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Kim:2022:HSC,
author = "Sun Ah Kim and Nayeon Kang and Taesung Park",
title = "Hierarchical Structured Component Analysis for
Microbiome Data Using Taxonomy Assignments",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1302--1312",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3039326",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3039326",
abstract = "The recent advent of high-throughput sequencing
technology has enabled us to study the associations
between human microbiome and diseases. The DNA
sequences of microbiome samples are clustered as
operational taxonomic units (OTUs) according to their
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yu:2022:EEP,
author = "Ken Hung-On Yu and Xiunan Fang and Haobin Yao and Bond
Ng and Tak Kwan Leung and Ling-Ling Wang and Chi Ho Lin
and Agnes Sze Wah Chan and Wai Keung Leung and Suet Yi
Leung and Joshua Wing Kei Ho",
title = "Evaluation of Experimental Protocols for Shotgun
Whole-Genome Metagenomic Discovery of Antibiotic
Resistance Genes",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1313--1321",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3004063",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3004063",
abstract = "Shotgun metagenomics has enabled the discovery of
antibiotic resistance genes (ARGs). Although there have
been numerous studies benchmarking the bioinformatics
methods for shotgun metagenomic data analysis, there
has not yet been a study that \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ma:2022:SBS,
author = "Yingjun Ma and Tingting He and Yuting Tan and Xingpeng
Jiang",
title = "{Seq-BEL}: Sequence-Based Ensemble Learning for
Predicting Virus-Human Protein--Protein Interaction",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1322--1333",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3008157",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3008157",
abstract = "Infectious diseases are currently the most important
and widespread health problem, and identifying viral
infection mechanisms is critical for controlling
diseases caused by highly infectious viruses. Because
of the lack of non-interactive protein pairs \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Huang:2022:E,
author = "Xiuzhen Huang and Yu Zhang and Xuan Guo",
title = "Editorial",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1334--1335",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3131688",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3131688",
abstract = "This special section gives the opportunity to know
recent advances in the application of intelligent
optimization algorithms in genomics and precision
medicine. Precision medicine is designed to optimize
the pathway for diagnosis, therapeutic intervention,.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Abdollahi:2022:DAD,
author = "Sina Abdollahi and Peng-Chan Lin and Jung-Hsien
Chiang",
title = "{DiaDeL}: an Accurate Deep Learning-Based Model With
Mutational Signatures for Predicting Metastasis Stage
and Cancer Types",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1336--1343",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3115504",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3115504",
abstract = "Mutational signatures help identify cancer-associated
genes that are being involved in tumorigenesis
pathways. Hence, these pathways guide precision
medicine approaches to find appropriate drugs and
treatments. The pattern of mutations varies in
different \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Adnan:2022:CER,
author = "Nahim Adnan and Maryam Zand and Tim H. M. Huang and
Jianhua Ruan",
title = "Construction and Evaluation of Robust Interpretation
Models for Breast Cancer Metastasis Prediction",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1344--1353",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3120673",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3120673",
abstract = "Interpretability of machine learning (ML) models
represents the extent to which a model&\#x2019;s
decision-making process can be understood by model
developers and/or end users. Transcriptomics-based
cancer prognosis models, for example, while achieving
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bhadra:2022:UFS,
author = "Tapas Bhadra and Saurav Mallik and Amir Sohel and
Zhongming Zhao",
title = "Unsupervised Feature Selection Using an Integrated
Strategy of Hierarchical Clustering With Singular Value
Decomposition: an Integrative Biomarker Discovery
Method With Application to Acute Myeloid Leukemia",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1354--1364",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3110989",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3110989",
abstract = "In this article, we propose a novel unsupervised
feature selection method by combining hierarchical
feature clustering with singular value decomposition
(SVD). The proposed algorithm first generates several
feature clusters by adopting the hierarchical
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bui:2022:HBB,
author = "Lien A. Bui and Dacosta Yeboah and Louis Steinmeister
and Sima Azizi and Daniel B. Hier and Donald C. Wunsch
and Gayla R. Olbricht and Tayo Obafemi-Ajayi",
title = "Heterogeneity in Blood Biomarker Trajectories After
Mild {TBI} Revealed by Unsupervised Learning",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1365--1378",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3091972",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3091972",
abstract = "Concussions, also known as mild traumatic brain injury
(mTBI), are a growing health challenge. Approximately
four million concussions are diagnosed annually in the
United States. Concussion is a heterogeneous disorder
in causation, symptoms, and outcome \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Manduchi:2022:GAC,
author = "Elisabetta Manduchi and Trang T. Le and Weixuan Fu and
Jason H. Moore",
title = "Genetic Analysis of Coronary Artery Disease Using
Tree-Based Automated Machine Learning Informed By
Biology-Based Feature Selection",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1379--1386",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3099068",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3099068",
abstract = "Machine Learning (ML) approaches are increasingly
being used in biomedical applications. Important
challenges of ML include choosing the right algorithm
and tuning the parameters for optimal performance.
Automated ML (AutoML) methods, such as Tree-based
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Causey:2022:EUN,
author = "Jason Causey and Jonathan Stubblefield and Jake Qualls
and Jennifer Fowler and Lingrui Cai and Karl Walker and
Yuanfang Guan and Xiuzhen Huang",
title = "An Ensemble of {U-Net} Models for Kidney Tumor
Segmentation With {CT} Images",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1387--1392",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3085608",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3085608",
abstract = "We present here the Arkansas AI-Campus solution method
for the 2019 Kidney Tumor Segmentation Challenge
(KiTS19). Our Arkansas AI-Campus team participated the
KiTS19 Challenge for four months, from March to July of
2019. This paper provides a summary of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2022:APD,
author = "Lechuan Li and Chonghao Zhang and Shiyu Liu and Hannah
Guan and Yu Zhang",
title = "Age Prediction by {DNA} Methylation in Neural
Networks",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1393--1402",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3084596",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3084596",
abstract = "Aging is traditionally thought to be caused by complex
and interacting factors such as DNA methylation. The
traditional formula of DNA methylation aging is based
on linear models and little work has explored the
effectiveness of neural \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Madhumita:2022:FWA,
author = "Madhumita and Sushmita Paul",
title = "A Feature Weighting-Assisted Approach for Cancer
Subtypes Identification From Paired Expression
Profiles",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1403--1414",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3041723",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3041723",
abstract = "Identification of cancer subtypes is critically
important for understanding the heterogeneity present
in tumors. Projects like The Cancer Genome Atlas
(TCGA), have made available the data-sets containing
expression profiles of multiple types of biomarkers
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhou:2022:NEM,
author = "Lewei Zhou and Yucong Tang and Guiying Yan",
title = "A New Estimation Method for the Biological Interaction
Predicting Problems",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1415--1423",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3049642",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3049642",
abstract = "For the past decades, computational methods have been
developed to predict various interactions in biological
problems. Usually these methods treated the predicting
problems as semi-supervised problem or
positive-unlabeled(PU) learning problem. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zou:2022:NAL,
author = "Chengye Zou and Xiaopeng Wei and Qiang Zhang and
Changjun Zhou",
title = "A Novel Adaptive Linear Neuron Based on {DNA} Strand
Displacement Reaction Network",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1424--1434",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3045567",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3045567",
abstract = "Analog DNA strand displacement circuits can be used to
build artificial neural network due to the continuity
of dynamic behavior. In this study, DNA implementations
of novel catalysis, novel degradation and adjustment
reaction modules are designed and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jiang:2022:RAB,
author = "Hao Jiang and Fei Zhan and Congtao Wang and Jianfeng
Qiu and Yansen Su and Chunhou Zheng and Xingyi Zhang
and Xiangxiang Zeng",
title = "A Robust Algorithm Based on Link Label Propagation for
Identifying Functional Modules From Protein--Protein
Interaction Networks",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1435--1448",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3038815",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3038815",
abstract = "Identifying functional modules in protein-protein
interaction (PPI) networks elucidates cellular
organization and mechanism. Various methods have been
proposed to identify the functional modules in PPI
networks, but most of these methods do not consider
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bankapur:2022:EML,
author = "Sanjay Bankapur and Nagamma Patil",
title = "An Effective Multi-Label Protein Sub-Chloroplast
Localization Prediction by Skipped-Grams of
Evolutionary Profiles Using Deep Neural Network",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1449--1458",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3037465",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3037465",
abstract = "Chloroplast is one of the most classic organelles in
algae and plant cells. Identifying the locations of
chloroplast proteins in the chloroplast organelle is an
important as well as a challenging task in deciphering
their functions. Biological-based \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2022:EHF,
author = "Liangliang Liu and Shaojie Tang and Fang-Xiang Wu and
Yu-Ping Wang and Jianxin Wang",
title = "An Ensemble Hybrid Feature Selection Method for
Neuropsychiatric Disorder Classification",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1459--1471",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3053181",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3053181",
abstract = "Magnetic resonance imagings (MRIs) are providing
increased access to neuropsychiatric disorders that can
be made available for advanced data analysis. However,
the single type of data limits the ability of
psychiatrists to distinguish the subclasses of
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Pouryahya:2022:ANI,
author = "Maryam Pouryahya and Jung Hun Oh and Pedram Javanmard
and James C. Mathews and Zehor Belkhatir and Joseph O.
Deasy and Allen R. Tannenbaum",
title = "{aWCluster}: a Novel Integrative Network-Based
Clustering of Multiomics for Subtype Analysis of Cancer
Data",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1472--1483",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3039511",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3039511",
abstract = "The remarkable growth of multi-platform genomic
profiles has led to the challenge of multiomics data
integration. In this study, we present a novel
network-based multiomics clustering founded on the
Wasserstein distance from optimal mass transport. This
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2022:BIS,
author = "Hu Zhang and Jingsong Chen and Tianhai Tian",
title = "{Bayesian} Inference of Stochastic Dynamic Models
Using Early-Rejection Methods Based on Sequential
Stochastic Simulations",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1484--1494",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3039490",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3039490",
abstract = "Stochastic modelling is an important method to
investigate the functions of noise in a wide range of
biological systems. However, the parameter inference
for stochastic models is still a challenging problem
partially due to the large computing time \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zuanetti:2022:BME,
author = "Daiane Aparecida Zuanetti and Luis Aparecido Milan",
title = "{Bayesian} Modeling for Epistasis Analysis Using
Data-Driven Reversible Jump",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1495--1506",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3043857",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3043857",
abstract = "We propose a procedure for modeling a phenotype using
QTLs which estimate the additive and dominance effects
of genotypes and epistasis. The estimation of the model
is implemented through a Bayesian approach which uses
the data-driven reversible jump \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ascension:2022:BPM,
author = "Alex M. Ascensi{\'o}n and Marcos J. Ara{\'u}zo-Bravo",
title = "{BigMPI4py}: {Python} Module for Parallelization of
Big Data Objects Discloses Germ Layer Specific {DNA}
Demethylation Motifs",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1507--1522",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3043979",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/python.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3043979",
abstract = "Parallelization in Python integrates Message Passing
Interface via the mpi4py module. Since mpi4py does not
support parallelization of objects greater than $
2^{31} $ ytes, we developed BigMPI4py, a Python module
that wraps mpi4py, supporting object sizes beyond this
boundary. BigMPI4py automatically determines the
optimal object distribution strategy, and uses
vectorized methods, achieving higher parallelization
efficiency. BigMPI4py facilitates the implementation of
Python for Big Data applications in multicore
workstations and High Performance Computer systems. We
use BigMPI4py to speed-up the search for germ line
specific de novo DNA methylated/unmethylated motifs
from the 59 whole genome bisulfite sequencing DNA
methylation samples from 27 human tissues of the ENCODE
project. We developed a parallel implementation of the
Kruskall-Wallis test to find CpGs with differential
methylation across germ layers. The parallel evaluation
of the significance of 55 million CpG achieved a 22x
speedup with 25 cores allowing us an efficient
identification of a set of hypermethylated genes in
ectoderm and mesoderm-related tissues, and another set
in endoderm-related tissues and finally, the discovery
of germ layer specific DNA demethylation motifs. Our
results point out that DNA methylation signal provide a
higher degree of information for the demethylated state
than for the methylated state. BigMPI4py is available
at https://https://www.arauzolab.org/tools/bigmpi4py
and https://gitlab.com/alexmascension/bigmpi4py and the
Jupyter Notebook with WGBS analysis at
https://gitlab.com/alexmascension/wgbs-analysis",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2022:CRR,
author = "Conghua Wang and Haihong Liu and Zhonghua Miao and Jin
Zhou",
title = "Circadian Rhythm Regulated by Tumor Suppressor p53 and
Time Delay in Unstressed Cells",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1523--1530",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3040368",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3040368",
abstract = "Circadian function and p53 network are interconnected
on the molecular level, but the dynamics induced by the
interaction between the circadian factor Per2 and the
tumor suppressor p53 remains poorly understood. Here,
we constructed an integrative model \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Khan:2022:CEE,
author = "Abhinandan Khan and Goutam Saha and Rajat Kumar Pal",
title = "Controlling the Effects of External Perturbations on a
Gene Regulatory Network Using
Proportional-Integral-Derivative Controller",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1531--1544",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3039038",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3039038",
abstract = "Gene regulatory networks are biologically robust,
which imparts resilience to living systems against most
external perturbations affecting them. However, there
is a limit to this and disturbances beyond this limit
can impart unwanted signalling on one or \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Abbaszadeh:2022:DDK,
author = "Omid Abbaszadeh and Ali Azarpeyvand and Alireza
Khanteymoori and Abbas Bahari",
title = "Data-Driven and Knowledge-Based Algorithms for Gene
Network Reconstruction on High-Dimensional Data",
journal = j-TCBB,
volume = "19",
number = "3",
pages = "1545--1557",
month = may,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2020.3034861",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2020.3034861",
abstract = "Previous efforts in gene network reconstruction have
mainly focused on data-driven modeling, with little
attention paid to knowledge-based approaches.
Leveraging prior knowledge, however, is a promising
paradigm that has been gaining momentum in network
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ma:2022:GES,
author = "Jian Ma",
title = "Guest Editorial for Selected Papers From {ACM-BCB
2019}",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "1919",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3140625",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3140625",
abstract = "The papers in this special issue were presented at the
ACM Conference on Bioinformatics, Computational
Biology, and Health Informatics (ACM-BCB) that was held
in Niagara Falls, NY, on September 7-10, 2019. The
conference continued the main focus of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Guo:2022:SNU,
author = "Yue Guo and Oleh Krupa and Jason Stein and Guorong Wu
and Ashok Krishnamurthy",
title = "{SAU-Net}: a Unified Network for Cell Counting in
{$2$D} and {$3$D} Microscopy Images",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "1920--1932",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3089608",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3089608",
abstract = "Image-based cell counting is a fundamental yet
challenging task with wide applications in biological
research. In this paper, we propose a novel unified
deep network framework designed to solve this problem
for various cell types in both 2D and 3D images.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lazarsfeld:2022:MVC,
author = "John Lazarsfeld and Jonathan Rodr{\'\i}guez and Mert
Erden and Yuelin Liu and Lenore J. Cowen",
title = "Majority Vote Cascading: a Semi-Supervised Framework
for Improving Protein Function Prediction",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "1933--1945",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3059812",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3059812",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Barshai:2022:GCN,
author = "Mira Barshai and Alice Aubert and Yaron Orenstein",
title = "{G4detector}: Convolutional Neural Network to Predict
{DNA} {G}-Quadruplexes",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "1946--1955",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3073595",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3073595",
abstract = "G-quadruplexes (G4s) are nucleic acid secondary
structures that form within guanine-rich DNA or RNA
sequences. G4 formation can affect chromatin
architecture and gene regulation, and has been
associated with genomic instability, genetic diseases,
and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Pinoli:2022:PDS,
author = "Pietro Pinoli and Gaia Ceddia and Stefano Ceri and
Marco Masseroli",
title = "Predicting Drug Synergism by Means of Non-Negative
Matrix Tri-Factorization",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "1956--1967",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3091814",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3091814",
abstract = "Traditional drug experiments to find synergistic drug
pairs are time-consuming and expensive due to the
numerous possible combinations of drugs that have to be
examined. Thus, computational methods that can give
suggestions for synergistic drug \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Qiu:2022:CRC,
author = "Yang Qiu and Yang Zhang and Yifan Deng and Shichao Liu
and Wen Zhang",
title = "A Comprehensive Review of Computational Methods For
Drug-Drug Interaction Detection",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "1968--1985",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3081268",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3081268",
abstract = "The detection of drug-drug interactions (DDIs) is a
crucial task for drug safety surveillance, which
provides effective and safe co-prescriptions of
multiple drugs. Since laboratory researches are often
complicated, costly and time-consuming, it&\#x0027;s
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhuang:2022:DEP,
author = "Yuanying Zhuang and Xiangrong Liu and Yue Zhong and
Longxin Wu",
title = "A Deep Ensemble Predictor for Identifying
Anti-Hypertensive Peptides Using Pretrained Protein
Embedding",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "1986--1992",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3068381",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3068381",
abstract = "Hypertension (HT), or high blood pressure is one of
the most common and main causes in cardiovascular
diseases, which is also related to a series of
detrimental diseases in humans. Deficiencies in
effective treatment in HT are often associated with a
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2022:DRA,
author = "Xingyi Li and Ju Xiang and Fang-Xiang Wu and Min Li",
title = "A Dual Ranking Algorithm Based on the Multiplex
Network for Heterogeneous Complex Disease Analysis",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "1993--2002",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3059046",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3059046",
abstract = "Identifying biomarkers of heterogeneous complex
diseases has always been one of the focuses in medical
research. In previous studies, the powerful network
propagation methods have been applied to finding marker
genes related to specific diseases, but \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jia:2022:MCA,
author = "Xibin Jia and Zheng Sun and Qing Mi and Zhenghan Yang
and Dawei Yang",
title = "A Multimodality-Contribution-Aware {TripNet} for
Histologic Grading of Hepatocellular Carcinoma",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2003--2016",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3079216",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3079216",
abstract = "Hepatocellular carcinoma (HCC) is a type of primary
liver malignant tumor with a high recurrence rate and
poor prognosis even undergoing resection or
transplantation. Accurate discrimination of the
histologic grades of HCC plays a critical role in the
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2022:NBG,
author = "Shiming Wang and Jie Li and Yadong Wang and Liran
Juan",
title = "A Neighborhood-Based Global Network Model to Predict
Drug-Target Interactions",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2017--2025",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3064614",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3064614",
abstract = "The detection of drug-target interactions (DTIs) plays
an important role in drug discovery and development,
making DTI prediction urgent to be solved. Existing
computational methods usually utilize drug similarity,
target similarity and DTI information to \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ding:2022:NRG,
author = "Xiaojian Ding and Fan Yang and Yaoyi Zhong and Jie
Cao",
title = "A Novel Recursive Gene Selection Method Based on Least
Square Kernel Extreme Learning Machine",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2026--2038",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3068846",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3068846",
abstract = "This paper presents a recursive feature elimination
(RFE) mechanism to select the most informative genes
with a least square kernel extreme learning machine
(LSKELM) classifier. Describing the generalization
ability of LSKELM in a way that is related to
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chaudhuri:2022:PMA,
author = "Anik Chaudhuri and Anwoy Kumar Mohanty and Manoranjan
Satpathy",
title = "A Parallelizable Model for Analyzing Cancer Tissue
Heterogeneity",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2039--2048",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3085894",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3085894",
abstract = "In a cancer study, the heterogeneous nature of a cell
population creates a lot of challenges. Efficient
determination of the compositional breakup of a cell
population, from gene expression measurements, is
critical to the success in a cancer study. This
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ji:2022:SSL,
author = "Cunmei Ji and Yutian Wang and Zhen Gao and Lei Li and
Jiancheng Ni and Chunhou Zheng",
title = "A Semi-Supervised Learning Method for {MiRNA-Disease}
Association Prediction Based on Variational
Autoencoder",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2049--2059",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3067338",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3067338",
abstract = "MicroRNAs (miRNAs) are a class of non-coding RNAs that
play critical role in many biological processes, such
as cell growth, development, differentiation and aging.
Increasing studies have revealed that miRNAs are
closely involved in many human diseases. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Mondol:2022:AAA,
author = "Raktim Kumar Mondol and Nhan Duy Truong and Mohammad
Reza and Samuel Ippolito and Esmaeil Ebrahimie and Omid
Kavehei",
title = "{AFExNet}: an Adversarial Autoencoder for
Differentiating Breast Cancer Sub-Types and Extracting
Biologically Relevant Genes",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2060--2070",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3066086",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3066086",
abstract = "Technological advancements in high-throughput genomics
enable the generation of complex and large data sets
that can be used for classification, clustering, and
bio-marker identification. Modern deep learning
algorithms provide us with the opportunity of
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ma:2022:AHS,
author = "Jingjing Ma and Haitao Jiang and Daming Zhu and Runmin
Yang",
title = "Algorithms and Hardness for Scaffold Filling to
Maximize Increased Duo-Preservations",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2071--2079",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3083896",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3083896",
abstract = "Scaffold filling is a critical step in DNA assembly.
Its basic task is to fill the missing genes (fragments)
into an incomplete genome (scaffold) to make it similar
to the reference genome. There have been a lot of work
under distinct measurements in the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Matroud:2022:AAA,
author = "Atheer Matroud and Christopher Tuffley and Michael
Hendy",
title = "An Asymmetric Alignment Algorithm for Estimating
Ancestor-Descendant Edit Distance for Tandem Repeats",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2080--2091",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3059239",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3059239",
abstract = "Tandem repeats are repetitive structures present in
some DNA sequences, consisting of many repeated copies
of a single motif. They can serve as important markers
for phylogenetic and population genetic studies, due to
the high polymorphism in the number \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2022:BDD,
author = "Qichang Zhao and Mengyun Yang and Zhongjian Cheng and
Yaohang Li and Jianxin Wang",
title = "Biomedical Data and Deep Learning Computational Models
for Predicting Compound-Protein Relations",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2092--2110",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3069040",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3069040",
abstract = "The identification of compound-protein relations
(CPRs), which includes compound-protein interactions
(CPIs) and compound-protein affinities (CPAs), is
critical to drug development. A common method for
compound-protein relation identification is the use
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ghosh:2022:BSS,
author = "Debraj Ghosh and Rajat K. De",
title = "Block Search Stochastic Simulation Algorithm ({{\em
BlSSSA\/}}): a Fast Stochastic Simulation Algorithm for
Modeling Large Biochemical Networks",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2111--2123",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3070123",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3070123",
abstract = "Stochastic simulation algorithms are extensively used
for exploring stochastic behavior of biochemical
pathways/networks. Computational cost of these
algorithms is high in simulating real biochemical
systems due to their large size, complex structure and
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sanyal:2022:CTC,
author = "Ritabrata Sanyal and Devroop Kar and Ram Sarkar",
title = "Carcinoma Type Classification From High-Resolution
Breast Microscopy Images Using a Hybrid Ensemble of
Deep Convolutional Features and Gradient Boosting Trees
Classifiers",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2124--2136",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3071022",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3071022",
abstract = "Breast cancer is one of the main causes behind cancer
deaths in women worldwide. Yet, owing to the complexity
of the histopathological images and the arduousness of
manual analysis task, the entire diagnosis process
becomes time-consuming and the results \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Dey:2022:CGA,
author = "Lopamudra Dey and Anirban Mukhopadhyay",
title = "Compact Genetic Algorithm-Based Feature Selection for
Sequence-Based Prediction of Dengue--Human Protein
Interactions",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2137--2148",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3066597",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3066597",
abstract = "Dengue Virus (DENV) infection is one of the rapidly
spreading mosquito-borne viral infections in humans.
Every year, around 50 million people get affected by
DENV infection, resulting in 20,000 deaths. Despite the
recent experiments focusing on dengue \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Feng:2022:CMI,
author = "Changli Feng and Jin Wu and Haiyan Wei and Lei Xu and
Quan Zou",
title = "{CRCF}: a Method of Identifying Secretory Proteins of
Malaria Parasites",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2149--2157",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3085589",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3085589",
abstract = "Malaria is a mosquito-borne disease that results in
millions of cases and deaths annually. The development
of a fast computational method that identifies
secretory proteins of the malaria parasite is important
for research on antimalarial drugs and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2022:DDL,
author = "Cheng-Hong Yang and Kuo-Chuan Wu and Li-Yeh Chuang and
Hsueh-Wei Chang",
title = "{DeepBarcoding}: Deep Learning for Species
Classification Using {DNA} Barcoding",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2158--2165",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3056570",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3056570",
abstract = "DNA barcodes with short sequence fragments are used
for species identification. Because of advances in
sequencing technologies, DNA barcodes have gradually
been emphasized. DNA sequences from different organisms
are easily and rapidly acquired. Therefore, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yu:2022:DPI,
author = "Guoxian Yu and Yeqian Yang and Yangyang Yan and Maozu
Guo and Xiangliang Zhang and Jun Wang",
title = "{DeepIDA}: Predicting Isoform-Disease Associations by
Data Fusion and Deep Neural Networks",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2166--2176",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3058801",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3058801",
abstract = "Alternative splicing produces different isoforms from
the same gene locus, it is an important mechanism for
regulating gene expression and proteome diversity.
Although the prediction of gene(ncRNA)-disease
associations has been extensively studied, few
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2022:DPI,
author = "Jun Wang and Long Zhang and An Zeng and Dawen Xia and
Jiantao Yu and Guoxian Yu",
title = "{DeepIII}: Predicting Isoform-Isoform Interactions by
Deep Neural Networks and Data Fusion",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2177--2187",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3068875",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3068875",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2022:DIR,
author = "Zhonghao Liu and Jing Jin and Yuxin Cui and Zheng
Xiong and Alireza Nasiri and Yong Zhao and Jianjun Hu",
title = "{DeepSeqPanII}: an Interpretable Recurrent Neural
Network Model With Attention Mechanism for Peptide-{HLA
Class II} Binding Prediction",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2188--2196",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3074927",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3074927",
abstract = "Human leukocyte antigen (HLA) complex molecules play
an essential role in immune interactions by presenting
peptides on the cell surface to T cells. With
significant deep learning progress, a series of neural
network-based models have been proposed and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Paltun:2022:DBD,
author = "Bet{\"u}l G{\"u}ven{\c{c}} Paltun and Samuel Kaski and
Hiroshi Mamitsuka",
title = "{DIVERSE}: {Bayesian Data IntegratiVE} Learning for
Precise Drug {ResponSE} Prediction",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2197--2207",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3065535",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3065535",
abstract = "Detecting predictive biomarkers from multi-omics data
is important for precision medicine, to improve
diagnostics of complex diseases and for better
treatments. This needs substantial experimental efforts
that are made difficult by the heterogeneity of
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cheng:2022:DTI,
author = "Zhongjian Cheng and Cheng Yan and Fang-Xiang Wu and
Jianxin Wang",
title = "Drug-Target Interaction Prediction Using Multi-Head
Self-Attention and Graph Attention Network",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2208--2218",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3077905",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3077905",
abstract = "Identifying drug-target interactions (DTIs) is an
important step in the process of new drug discovery and
drug repositioning. Accurate predictions for DTIs can
improve the efficiency in the drug discovery and
development. Although rapid advances in deep \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2022:DMD,
author = "Dongyuan Li and Shuyao Zhang and Xiaoke Ma",
title = "Dynamic Module Detection in Temporal Attributed
Networks of Cancers",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2219--2230",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3069441",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3069441",
abstract = "Tracking the dynamic modules (modules change over
time) during cancer progression is essential for
studying cancer pathogenesis, diagnosis, and therapy.
However, current algorithms only focus on detecting
dynamic modules from temporal cancer networks
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2022:ECD,
author = "Chuang Liu and Yao Dai and Keping Yu and Zi-Ke Zhang",
title = "Enhancing Cancer Driver Gene Prediction by
Protein--Protein Interaction Network",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2231--2240",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3063532",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3063532",
abstract = "With the advances in gene sequencing technologies,
millions of somatic mutations have been reported in the
past decades, but mining cancer driver genes with
oncogenic mutations from these data remains a critical
and challenging area of research. In this \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2022:FSD,
author = "Yan Wang and Lei Zhang and Xin Shu and Yangqin Feng
and Zhang Yi and Qing Lv",
title = "Feature-Sensitive Deep Convolutional Neural Network
for Multi-Instance Breast Cancer Detection",
journal = j-TCBB,
volume = "19",
number = "4",
pages = "2241--2251",
month = jul,
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3060183",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:00:59 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3060183",
abstract = "To obtain a well-performed computer-aided detection
model for detecting breast cancer, it is usually needed
to design an effective and efficient algorithm and a
well-labeled dataset to train it. In this paper, first,
a multi-instance mammography clinic \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yan:2022:GESa,
author = "Da Yan and Hong Qin and Hsiang-Yun Wu and Jake Y.
Chen",
title = "Guest Editorial for Selected Papers From {BIOKDD
2020}",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2545--2546",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3176912",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3176912",
abstract = "THE 19th International Workshop on Data Mining in
Bioinformatics (BIOKDD 2020) was held virtually on
August 24, 2020 due to the COVID-19 pandemic. BIOKDD
2020 featured the special theme of ``Battling
COVID-19'' which particularly welcomed paper
submissions \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Mendonca-Neto:2022:GSM,
author = "Rayol Mendonca-Neto and Zhi Li and David Feny{\"o} and
Claudio T. Silva and Fab{\'\i}ola G. Nakamura and
Eduardo F. Nakamura",
title = "A Gene Selection Method Based on Outliers for Breast
Cancer Subtype Classification",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2547--2559",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3132339",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3132339",
abstract = "Breast cancer is the second most common cancer type
and is the leading cause of cancer-related deaths
worldwide. Since it is a heterogeneous disease,
subtyping breast cancer plays an important role in
performing a specific treatment. Gene expression data
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jin:2022:KEM,
author = "Yuanyuan Jin and Wendi Ji and Wei Zhang and Xiangnan
He and Xinyu Wang and Xiaoling Wang",
title = "A {KG}-Enhanced Multi-Graph Neural Network for
Attentive Herb Recommendation",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2560--2571",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3115489",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3115489",
abstract = "Traditional Chinese Medicine (TCM) has the longest
clinical history in Asia and contributes a lot to
health maintenance worldwide. An essential step during
the TCM diagnostic process is syndrome induction, which
comprehensively analyzes the symptoms and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Martins:2022:LPM,
author = "Andreia S. Martins and Marta Gromicho and Susana Pinto
and Mamede de Carvalho and Sara C. Madeira",
title = "Learning Prognostic Models Using Disease Progression
Patterns: Predicting the Need for Non-Invasive
Ventilation in Amyotrophic Lateral Sclerosis",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2572--2583",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3078362",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3078362",
abstract = "Amyotrophic Lateral Sclerosis is a devastating
neurodegenerative disease causing rapid degeneration of
motor neurons and usually leading to death by
respiratory failure. Since there is no cure,
treatment&\#x2019;s goal is to improve symptoms and
prolong \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2022:LBT,
author = "Qingyu Chen and Jingcheng Du and Alexis Allot and
Zhiyong Lu",
title = "{LitMC-BERT}: Transformer-Based Multi-Label
Classification of Biomedical Literature With An
Application on {COVID-19} Literature Curation",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2584--2595",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3173562",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3173562",
abstract = "The rapid growth of biomedical literature poses a
significant challenge for curation and interpretation.
This has become more evident during the COVID-19
pandemic. LitCovid, a literature database of COVID-19
related papers in PubMed, has accumulated over
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yella:2022:MDD,
author = "Jaswanth K. Yella and Anil G. Jegga",
title = "{MGATRx}: Discovering Drug Repositioning Candidates
Using Multi-View Graph Attention",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2596--2604",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3082466",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3082466",
abstract = "In-silico drug repositioning or predicting new
indications for approved or late-stage clinical trial
drugs is a resourceful and time-efficient strategy in
drug discovery. However, inferring novel candidate
drugs for a disease is challenging, given the
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhou:2022:SDC,
author = "Jieli Zhou and Baoyu Jing and Zeya Wang and Hongyi Xin
and Hanghang Tong",
title = "{SODA}: Detecting {COVID-19} in Chest {X}-Rays With
Semi-Supervised Open Set Domain Adaptation",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2605--2612",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3066331",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3066331",
abstract = "Due to the shortage of COVID-19 viral testing kits,
radiology imaging is used to complement the screening
process. Deep learning based methods are promising in
automatically detecting COVID-19 disease in chest x-ray
images. Most of these works first train \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Guo:2022:CMC,
author = "Xiaojuan Guo and Kewei Chen and Yinghua Chen and
Chengjie Xiong and Yi Su and Li Yao and Eric M.
Reiman",
title = "A Computational {Monte Carlo} Simulation Strategy to
Determine the Temporal Ordering of Abnormal Age Onset
Among Biomarkers of {Alzheimer&\#x0027;s} Disease",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2613--2622",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3106939",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3106939",
abstract = "To quantitatively determining the temporal ordering of
abnormal age onsets (AAO) among various biomarkers for
Alzheimer&\#x0027;s disease (AD), we introduced a
computational Monte-Carlo simulation (CMCS) to
statistically examine such ordering of an AAO
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xiong:2022:MFI,
author = "Zhankun Xiong and Feng Huang and Ziyan Wang and
Shichao Liu and Wen Zhang",
title = "A Multimodal Framework for Improving {{\em in
Silico\/}} Drug Repositioning With the Prior Knowledge
From Knowledge Graphs",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2623--2631",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3103595",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3103595",
abstract = "Drug repositioning/repurposing is a very important
approach towards identifying novel treatments for
diseases in drug discovery. Recently, large-scale
biological datasets are increasingly available for
pharmaceutical research and promote the development
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ning:2022:NMI,
author = "Qiao Ning and Xiaowei Zhao and Zhiqiang Ma",
title = "A Novel Method for Identification of Glutarylation
Sites Combining Borderline-{SMOTE} With {Tomek} Links
Technique in Imbalanced Data",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2632--2641",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3095482",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3095482",
abstract = "Glutarylation is a type of post-translational
modification that occurs on lysine residues. It plays
an irreplaceable role in various cellular functions.
Therefore, identification of glutarylation sites is
significant for understanding the molecular \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{LeMay:2022:PTA,
author = "Matthew LeMay and Ran Libeskind-Hadas and Yi-Chieh
Wu",
title = "A Polynomial-Time Algorithm for Minimizing the Deep
Coalescence Cost for Level-1 Species Networks",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2642--2653",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3105922",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3105922",
abstract = "Phylogenetic analyses commonly assume that the species
history can be represented as a tree. However, in the
presence of hybridization, the species history is more
accurately captured as a network. Despite several
advances in modeling phylogenetic \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2022:AED,
author = "Xuan Yang and Chen Yang and Jimeng Lei and Jianxiao
Liu",
title = "An Approach of Epistasis Detection Using Integer
Linear Programming Optimizing {Bayesian} Network",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2654--2671",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3092719",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3092719",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2022:ESI,
author = "Chao Li and Jun Sun and Li-Wei Li and Xiaojun Wu and
Vasile Palade",
title = "An Effective Swarm Intelligence Optimization Algorithm
for Flexible Ligand Docking",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2672--2684",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3103777",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3103777",
abstract = "In general, flexible ligand docking is used for
docking simulations under the premise that the position
of the binding site is already known, and meanwhile it
can also be used without prior knowledge of the binding
site. However, most of the optimization \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ranjan:2022:ETI,
author = "Ashish Ranjan and David Fern{\'a}ndez-Baca and
Sudhakar Tripathi and Akshay Deepak",
title = "An Ensemble {Tf-Idf} Based Approach to Protein
Function Prediction via Sequence Segmentation",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2685--2696",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3093060",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3093060",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gudur:2022:FBE,
author = "Venkateshwarlu Yellaswamy Gudur and Sidharth
Maheshwari and Amit Acharyya and Rishad Shafik",
title = "An {FPGA} Based Energy-Efficient Read Mapper With
Parallel Filtering and In-Situ Verification",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2697--2711",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3106311",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/string-matching.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3106311",
abstract = "In the assembly pipeline of Whole Genome Sequencing
(WGS), read mapping is a widely used method to
re-assemble the genome. It employs approximate string
matching and dynamic programming-based algorithms on a
large volume of data and associated structures,
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Qin:2022:ADP,
author = "Xinyi Qin and Lu Zhang and Min Liu and Ziwei Xu and
Guangzhong Liu",
title = "{ASFold-DNN}: Protein Fold Recognition Based on
Evolutionary Features With Variable Parameters Using
Full Connected Neural Network",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2712--2722",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3089168",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3089168",
abstract = "Protein fold recognition contribute to comprehend the
function of proteins, which is of great help to the
gene therapy of diseases and the development of new
drugs. Researchers have been working in this direction
and have made considerable achievements, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cheng:2022:ADC,
author = "Jianhong Cheng and Wei Zhao and Jin Liu and Xingzhi
Xie and Shangjie Wu and Liangliang Liu and Hailin Yue
and Junjian Li and Jianxin Wang and Jun Liu",
title = "Automated Diagnosis of {COVID-19} Using Deep
Supervised Autoencoder With Multi-View Features From
{CT} Images",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2723--2736",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3102584",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3102584",
abstract = "Accurate and rapid diagnosis of coronavirus disease
2019 (COVID-19) from chest CT scans is of great
importance and urgency during the worldwide outbreak.
However, radiologists have to distinguish COVID-19
pneumonia from other pneumonia in a large number
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Huang:2022:CCA,
author = "Zhihao Huang and Yan Wang and Xiaoke Ma",
title = "Clustering of Cancer Attributed Networks by
Dynamically and Jointly Factorizing Multi-Layer
Graphs",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2737--2748",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3090586",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3090586",
abstract = "The accumulated omic data provides an opportunity to
exploit the mechanisms of cancers and poses a challenge
for their integrative analysis. Although extensive
efforts have been devoted to address this issue, the
current algorithms result in undesirable \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Arif:2022:DDL,
author = "Muhammad Arif and Muhammad Kabir and Saeed Ahmed and
Abid Khan and Fang Ge and Adel Khelifi and Dong-Jun
Yu",
title = "{DeepCPPred}: a Deep Learning Framework for the
Discrimination of Cell-Penetrating Peptides and Their
Uptake Efficiencies",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2749--2759",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3102133",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3102133",
abstract = "Cell-penetrating peptides (CPPs) are special peptides
capable of carrying a variety of bioactive molecules,
such as genetic materials, short interfering RNAs and
nanoparticles, into cells. Recently, research on CPP
has gained substantial interest from \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Pu:2022:DDT,
author = "Yuqian Pu and Jiawei Li and Jijun Tang and Fei Guo",
title = "{DeepFusionDTA}: Drug-Target Binding Affinity
Prediction With Information Fusion and Hybrid
Deep-Learning Ensemble Model",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2760--2769",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3103966",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3103966",
abstract = "Identification of drug-target interaction (DTI) is the
most important issue in the broad field of drug
discovery. Using purely biological experiments to
verify drug-target binding profiles takes lots of time
and effort, so computational technologies for
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Dutta:2022:DDA,
author = "Pratik Dutta and Aditya Prakash Patra and Sriparna
Saha",
title = "{DeePROG}: Deep Attention-Based Model for Diseased
Gene Prognosis by Fusing Multi-Omics Data",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2770--2781",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3090302",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3090302",
abstract = "An in-depth exploration of gene prognosis using
different methodologies aids in understanding various
biological regulations of genes in disease pathobiology
and molecular functions. Interpreting gene functions at
biological and molecular levels remains a \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2022:DLC,
author = "Weizhong Zhao and Jinyong Zhang and Jincai Yang and
Xingpeng Jiang and Tingting He",
title = "Document-Level Chemical-Induced Disease Relation
Extraction via Hierarchical Representation Learning",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2782--2793",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3086090",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3086090",
abstract = "Over the past decades, Chemical-induced Disease (CID)
relations have attracted extensive attention in
biomedical community, reflecting wide applications in
biomedical research and healthcare field. However,
prior efforts fail to make full use of the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ajmal:2022:DBN,
author = "Hamda B. Ajmal and Michael G. Madden",
title = "Dynamic {Bayesian} Network Learning to Infer Sparse
Models From Time Series Gene Expression Data",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2794--2805",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3092879",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3092879",
abstract = "One of the key challenges in systems biology is to
derive gene regulatory networks (GRNs) from complex
high-dimensional sparse data. Bayesian networks (BNs)
and dynamic Bayesian networks (DBNs) have been widely
applied to infer GRNs from gene expression \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2022:EIB,
author = "Jian Liu and Jialiang Sun and Yongzhuang Liu",
title = "Effective Identification of Bacterial Genomes From
Short and Long Read Sequencing Data",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2806--2816",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3095164",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3095164",
abstract = "With the development of sequencing technology,
microbiological genome sequencing analysis has
attracted extensive attention. For inexperienced users
without sufficient bioinformatics skills, making sense
of sequencing data for microbial identification,
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ghosh:2022:ERF,
author = "Debopriya Ghosh and Javier Cabrera",
title = "Enriched Random Forest for High Dimensional Genomic
Data",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2817--2828",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3089417",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3089417",
abstract = "Ensemble methods such as random forest works well on
high-dimensional datasets. However, when the number of
features is extremely large compared to the number of
samples and the percentage of truly informative feature
is very small, performance of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2022:GDC,
author = "Pei Wang and Daojie Wang",
title = "Gene Differential Co-Expression Networks Based on
{RNA-Seq}: Construction and Its Applications",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2829--2841",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3103280",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3103280",
abstract = "Gene co-expression network (GCN) becomes an
increasingly important tool in omics data analysis. A
great challenge for GCN construction is that the sample
size is far lower than the number of genes. Traditional
methods rely on considerable samples. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Naik:2022:IER,
author = "Musab Naik and Luis Rueda and Akram Vasighizaker",
title = "Identification of Enriched Regions in {ChIP-Seq} Data
via a Linear-Time Multi-Level Thresholding Algorithm",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2842--2850",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3104734",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3104734",
abstract = "Chromatin immunoprecipitation (ChIP&\#x2013;Seq) has
emerged as a superior alternative to microarray
technology as it provides higher resolution, less
noise, greater coverage and wider dynamic range. While
ChIP-Seq enables probing of DNA-protein \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ko:2022:IFM,
author = "Young-Joon Ko and Sangsoo Kim and Cheol-Ho Pan and
Keunwan Park",
title = "Identification of Functional Microbial Modules Through
Network-Based Analysis of Meta-Microbial Features Using
Matrix Factorization",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2851--2862",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3100893",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3100893",
abstract = "As the microbiome is composed of a variety of
microbial interactions, it is imperative in microbiome
research to identify a microbial sub-community that
collectively conducts a specific function. However,
current methodologies have been highly limited to
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Song:2022:ICP,
author = "Junrong Song and Wei Peng and Feng Wang",
title = "Identifying Cancer Patient Subgroups by Finding
Co-Modules From the Driver Mutation Profiles and
Downstream Gene Expression Profiles",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2863--2872",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3106344",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3106344",
abstract = "Nowadays, the heterogeneous characteristics of cancer
patients throw a big challenge to precision medicine
and targeted therapy. Identifying cancer subtypes shed
new light on effective personalized cancer medicine,
future therapeutic strategies and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2022:ILE,
author = "Siyuan Zhao and Jun Meng and Qiang Kang and Yushi
Luan",
title = "Identifying {LncRNA-Encoded} Short Peptides Using
Optimized Hybrid Features and Ensemble Learning",
journal = j-TCBB,
volume = "19",
number = "5",
pages = "2873--2881",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3104288",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:01 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3104288",
abstract = "Long non-coding RNA (lncRNA) contains short open
reading frames (sORFs), and sORFs-encoded short
peptides (SEPs) have become the focus of scientific
studies due to their crucial role in life activities.
The identification of SEPs is vital to further
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yan:2022:GESb,
author = "Da Yan and Zhaohui S. Qin and Debswapna Bhattacharya
and Jake Y. Chen",
title = "Guest Editorial for Selected Papers From {BIOKDD
2021}",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3068--3069",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3208759",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3208759",
abstract = "The papers in this special section were presented at
the 20th International Workshop on Data Mining in
Bioinformatics (BIOKDD 2021) that was held virtually on
August 15, 2021. The conference featured the special
theme of ``Artificial Intelligence'' in \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ye:2022:KGE,
author = "Cheng Ye and Rowan Swiers and Stephen Bonner and Ian
Barrett",
title = "A Knowledge Graph-Enhanced Tensor Factorisation Model
for Discovering Drug Targets",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3070--3080",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3197320",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3197320",
abstract = "The drug discovery and development process is a long
and expensive one, costing over 1 billion USD on
average per drug and taking 10-15 years. To reduce the
high levels of attrition throughout the process, there
has been a growing interest in applying \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Dong:2022:MBM,
author = "Ngan Dong and Stefanie M{\"u}cke and Megha Khosla",
title = "{MuCoMiD}: a Multitask Graph Convolutional Learning
Framework for {MiRNA}-Disease Association Prediction",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3081--3092",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3176456",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3176456",
abstract = "Growing evidence from recent studies implies that
microRNAs or miRNAs could serve as biomarkers in
various complex human diseases. Since wet-lab
experiments for detecting miRNAs associated with a
disease are expensive and time-consuming, machine
learning \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Aoki:2022:HMT,
author = "Raquel Aoki and Frederick Tung and Gabriel L.
Oliveira",
title = "Heterogeneous Multi-Task Learning With Expert
Diversity",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3093--3102",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3175456",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3175456",
abstract = "Predicting multiple heterogeneous biological and
medical targets is a challenge for traditional deep
learning models. In contrast to single-task learning,
in which a separate model is trained for each target,
multi-task learning (MTL) optimizes a single \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sefer:2022:BSI,
author = "Emre Sefer",
title = "{BioCode}: a Data-Driven Procedure to Learn the Growth
of Biological Networks",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3103--3113",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3165092",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3165092",
abstract = "Probabilistic biological network growth models have
been utilized for many tasks including but not limited
to capturing mechanism and dynamics of biological
growth activities, null model representation, capturing
anomalies, etc. Well-known examples of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Mukherjee:2022:FOR,
author = "Kingshuk Mukherjee and Daniel Dole-Muinos and
Massimiliano Rossi and Ayomide Ajayi and Mattia
Prosperi and Christina Boucher",
title = "Finding Overlapping {Rmaps} via Clustering",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3114--3123",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3132534",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3132534",
abstract = "Optical mapping is a method for creating high
resolution restriction maps of an entire genome.
Optical mapping has been largely automated, and first
produces single molecule restriction maps, called
Rmaps, which are assembled to generate genome wide
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Huang:2022:GES,
author = "De-Shuang Huang and Kyungsook Han and Tatsuya Akutsu",
title = "Guest Editorial for Special Section on the {16th
International Conference on Intelligent Computing
(ICIC)}",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3124--3125",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3150232",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3150232",
abstract = "The eight papers in this special section were
presented at the Sixteenth International Conference on
Intelligent Computing (ICIC) that was held in Bari,
Italy, on October 2-5, 2020. ICIC was formed to provide
an annual forum dedicated to the emerging and
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jiang:2022:GPC,
author = "Tengsheng Jiang and Yuhui Chen and Shixuan Guan and
Zhongtian Hu and Weizhong Lu and Qiming Fu and Yijie
Ding and Haiou Li and Hongjie Wu",
title = "{G} Protein-Coupled Receptor Interaction Prediction
Based on Deep Transfer Learning",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3126--3134",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3128172",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3128172",
abstract = "G protein-coupled receptors (GPCRs) account for about
40&\#x0025; to 50&\#x0025; of drug targets. Many human
diseases are related to G protein coupled receptors.
Accurate prediction of GPCR interaction is not only
essential to understand its structural role,.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chou:2022:NAI,
author = "Hsin-Hung Chou and Ching-Tien Hsu and Chin-Wei Hsu and
Kai-Hsun Yao and Hao-Ching Wang and Sun-Yuan Hsieh",
title = "Novel Algorithm for Improved Protein Classification
Using Graph Similarity",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3135--3143",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3125836",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3125836",
abstract = "Considerable sequence data are produced in genome
annotation projects that relate to molecular levels,
structural similarities, and molecular and biological
functions. In structural genomics, the most essential
task involves resolving protein structures \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2022:PVD,
author = "Qinhu Zhang and Yindong Zhang and Siguo Wang and
Zhan-Heng Chen and Valeriya Gribova and Vladimir
Fedorovich Filaretov and De-Shuang Huang",
title = "Predicting In-Vitro {DNA}--Protein Binding With a
Spatially Aligned Fusion of Sequence and Shape",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3144--3153",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3133869",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3133869",
abstract = "Discovery of transcription factor binding sites
(TFBSs) is of primary importance for understanding the
underlying binding mechanic and gene regulation
process. Growing evidence indicates that apart from the
primary DNA sequences, DNA shape landscape has a
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Fang:2022:DES,
author = "Min Fang and Yufeng He and Zhihua Du and Vladimir N.
Uversky",
title = "{DeepCLD}: an Efficient Sequence-Based Predictor of
Intrinsically Disordered Proteins",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3154--3159",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3124273",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3124273",
abstract = "Intrinsic disorder is common in proteins, plays
important roles in protein functionality, and is
commonly associated with various human diseases. To
have an accurate tool for the annotation of intrinsic
disorder in proteins, this paper proposes a novel
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lu:2022:PMD,
author = "Xinguo Lu and Jinxin Li and Zhenghao Zhu and Yue Yuan
and Guanyuan Chen and Keren He",
title = "Predicting {miRNA}--Disease Associations via Combining
Probability Matrix Feature Decomposition With Neighbor
Learning",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3160--3170",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3097037",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3097037",
abstract = "Predicting the associations of miRNAs and diseases may
uncover the causation of various diseases. Many methods
are emerging to tackle the sparse and unbalanced
disease related miRNA prediction. Here, we propose a
Probabilistic matrix decomposition \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wu:2022:ETM,
author = "Qing-Wen Wu and Rui-Fen Cao and Jun-Feng Xia and
Jian-Cheng Ni and Chun-Hou Zheng and Yan-Sen Su",
title = "Extra Trees Method for Predicting {LncRNA}--Disease
Association Based On Multi-Layer Graph Embedding
Aggregation",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3171--3178",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3113122",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3113122",
abstract = "Lots of experimental studies have revealed the
significant associations between lncRNAs and diseases.
Identifying accurate associations will provide a new
perspective for disease therapy. Calculation-based
methods have been developed to solve these \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2022:PCL,
author = "Bo Li and Yihui Tian and Yang Tian and Shihua Zhang
and Xiaolong Zhang",
title = "Predicting Cancer Lymph-Node Metastasis From {LncRNA}
Expression Profiles Using Local Linear Reconstruction
Guided Distance Metric Learning",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3179--3189",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3149791",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3149791",
abstract = "Lymph-node metastasis is the most perilous cancer
progressive state, where long non-coding RNA (lncRNA)
has been confirmed to be an important genetic indicator
in cancer prediction. However, lncRNA expression
profile is often characterized of large \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2022:ILM,
author = "Wen Li and Shulin Wang and Junlin Xu and Ju Xiang",
title = "Inferring Latent {MicroRNA}--Disease Associations on a
Gene-Mediated Tripartite Heterogeneous Multiplexing
Network",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3190--3201",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3143770",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3143770",
abstract = "MicroRNA (miRNA) is a class of non-coding
single-stranded RNA molecules encoded by endogenous
genes with a length of about 22 nucleotides. MiRNAs
have been successfully identified as differentially
expressed in various cancers. There is evidence that
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xie:2022:DLP,
author = "Yulian Xie and Min Liu and Shirui Zhou and Yaonan
Wang",
title = "A Deep Local Patch Matching Network for Cell Tracking
in Microscopy Image Sequences Without Registration",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3202--3212",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3113129",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3113129",
abstract = "Cell tracking is critical for the modeling of plant
cell growth patterns. A local graph matching algorithm
is proposed to track cells by exploiting the tight
spatial topology of cells. However, the local graph
matching approach lacks robustness in the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhanpeng:2022:MCM,
author = "Huang Zhanpeng and Wu Jiekang",
title = "A Multiview Clustering Method With Low-Rank and
Sparsity Constraints for Cancer Subtyping",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3213--3223",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3122917",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3122917",
abstract = "Multiomics data clustering is one of the major
challenges in the field of precision medicine.
Integration of multiomics data for cancer subtyping can
improve the understanding on cancer and reveal
systems-level insights. How to integrate multiomics
data \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Aleb:2022:MAM,
author = "Nassima Aleb",
title = "A Mutual Attention Model for Drug Target Binding
Affinity Prediction",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3224--3232",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3121275",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3121275",
abstract = "Vrious machine learning approaches have been developed
for drug-target interaction (DTI) prediction. One class
of these approaches, DTBA, is interested in Drug-Target
Binding Affinity strength, rather than focusing merely
on the presence or absence of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhu:2022:NMI,
author = "Jianshen Zhu and Naveed Ahmed Azam and Fan Zhang and
Aleksandar Shurbevski and Kazuya Haraguchi and Liang
Zhao and Hiroshi Nagamochi and Tatsuya Akutsu",
title = "A Novel Method for Inferring Chemical Compounds With
Prescribed Topological Substructures Based on Integer
Programming",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3233--3245",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3112598",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3112598",
abstract = "Drug discovery is one of the major goals of
computational biology and bioinformatics. A novel
framework has recently been proposed for the design of
chemical graphs using both artificial neural networks
(ANNs) and mixed integer linear programming (MILP).
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2022:RGI,
author = "Yueran Yang and Yu Zhang and Shuai Li and Xubin Zheng
and Man-Hon Wong and Kwong-Sak Leung and Lixin Cheng",
title = "A Robust and Generalizable Immune-Related Signature
for Sepsis Diagnostics",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3246--3254",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3107874",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3107874",
abstract = "High-throughput sequencing can detect tens of
thousands of genes in parallel, providing opportunities
for improving the diagnostic accuracy of multiple
diseases including sepsis, which is an aggressive
inflammatory response to infection that can cause
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ni:2022:AES,
author = "Xinzhe Ni and Bohao Geng and Haoyu Zheng and Jiawei
Shi and Gang Hu and Jianzhao Gao",
title = "Accurate Estimation of Single-Cell Differentiation
Potency Based on Network Topology and Gene Ontology
Information",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3255--3262",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3112951",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3112951",
abstract = "One important task in single-cell analysis is to
quantify the differentiation potential of single cells.
Though various single-cell potency measures have been
proposed, they are based on individual biological
sources, thus not robust and reliable. It is \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2022:APH,
author = "Yiming Li and Min Zeng and Yifan Wu and Yaohang Li and
Min Li",
title = "Accurate Prediction of Human Essential Proteins Using
Ensemble Deep Learning",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3263--3271",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3122294",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3122294",
abstract = "Essential proteins are considered the foundation of
life as they are indispensable for the survival of
living organisms. Computational methods for essential
protein discovery provide a fast way to identify
essential proteins. But most of them heavily rely
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bi:2022:ABB,
author = "Jingshu Bi and Yuanjie Zheng and Chongjing Wang and
Yanhui Ding",
title = "An Attention Based Bidirectional {LSTM} Method to
Predict the Binding of {TCR} and Epitope",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3272--3280",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3115353",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3115353",
abstract = "The T-cell epitope prediction has always been a
long-term challenge in immunoinformatics and
bioinformatics. Studying the specific recognition
between T-cell receptor (TCR) and peptide-major
histocompatibility complex (p-MHC) complexes can help
us better \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Mazrouee:2022:AAR,
author = "Sepideh Mazrouee",
title = "{ARHap}: Association Rule Haplotype Phasing",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3281--3294",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3119955",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3119955",
abstract = "This article proposes a novel approach for Individual
Human phasing through discovery of interesting hidden
relations among single variant sites. The proposed
framework, called ARHap, learns strong association
rules among variant loci on the genome and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ma:2022:BBR,
author = "Jiani Ma and Lin Zhang and Shaojie Li and Hui Liu",
title = "{BRPCA}: Bounded Robust Principal Component Analysis
to Incorporate Similarity Network for
{N7}-Methylguanosine({mo$^7$G}) Site-Disease
Association Prediction",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3295--3306",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3109055",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3109055",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Parvini:2022:CPD,
author = "Ghazaleh Parvini and Katherine Braught and David
Fern{\'a}ndez-Baca",
title = "Checking Phylogenetics Decisiveness in Theory and in
Practice",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3307--3316",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3128381",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3128381",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Battistella:2022:CCO,
author = "Enzo Battistella and Maria Vakalopoulou and Roger Sun
and Th{\'e}o Estienne and Marvin Lerousseau and Sergey
Nikolaev and {\'E}milie Alvarez Andres and Alexandre
Carr{\'e} and St{\'e}phane Niyoteka and Charlotte
Robert and Nikos Paragios and {\'E}ric Deutsch",
title = "{COMBING}: Clustering in Oncology for Mathematical and
Biological Identification of Novel Gene Signatures",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3317--3331",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3123910",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3123910",
abstract = "Precision medicine is a paradigm shift in healthcare
relying heavily on genomics data. However, the
complexity of biological interactions, the large number
of genes as well as the lack of comparisons on the
analysis of data, remain a tremendous bottleneck
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Mongia:2022:CPD,
author = "Aanchal Mongia and Emilie Chouzenoux and Angshul
Majumdar",
title = "Computational Prediction of Drug-Disease Association
Based on Graph-Regularized One Bit Matrix Completion",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3332--3339",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3189879",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3189879",
abstract = "Investigation of existing drugs is an effective
alternative to the discovery of new drugs for treating
diseases. This task of drug re-positioning can be
assisted by various kinds of computational methods to
predict the best indication for a drug given the
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Shetta:2022:CMV,
author = "Omar Shetta and Mahesan Niranjan and Srinandan
Dasmahapatra",
title = "Convex Multi-View Clustering Via Robust Low Rank
Approximation With Application to Multi-Omic Data",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3340--3352",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3122961",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3122961",
abstract = "Recent advances in high throughput technologies have
made large amounts of biomedical omics data accessible
to the scientific community. Single omic data
clustering has proved its impact in the biomedical and
biological research fields. Multi-omic data \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Aakesson:2022:CNN,
author = "Mattias {\AA}kesson and Prashant Singh and Fredrik
Wrede and Andreas Hellander",
title = "Convolutional Neural Networks as Summary Statistics
for Approximate {Bayesian} Computation",
journal = j-TCBB,
volume = "19",
number = "6",
pages = "3353--3365",
year = "2022",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3108695",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Wed Oct 18 13:01:03 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3108695",
abstract = "Approximate Bayesian Computation is widely used in
systems biology for inferring parameters in stochastic
gene regulatory network models. Its performance hinges
critically on the ability to summarize high-dimensional
system responses such as time series \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2023:CEM,
author = "Qiaoming Liu and Xudong Zhao and Guohua Wang",
title = "A Clustering Ensemble Method for Cell Type Detection
by Multiobjective Particle Optimization",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "1--14",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3132400",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3132400",
abstract = "Single-cell RNA sequencing (scRNA-seq) is a new
technology different from previous sequencing methods
that measure the average expression level for each gene
across a large population of cells. Thus, new
computational methods are required to reveal cell
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2023:CTA,
author = "Kun Liu and Hong-Dong Li and Yaohang Li and Jun Wang
and Jianxin Wang",
title = "A Comparison of Topologically Associating Domain
Callers Based on {Hi-C} Data",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "15--29",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3147805",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3147805",
abstract = "Topologically associating domains (TADs) are local
chromatin interaction domains, which have been shown to
play an important role in gene expression regulation.
TADs were originally discovered in the investigation of
3D genome organization based on High-. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Klosa:2023:FSG,
author = "Jan Klosa and Noah Simon and Volkmar Liebscher and
D{\"o}rte Wittenburg",
title = "A Fitted Sparse-Group Lasso for Genome-Based
Evaluations",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "30--38",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3156805",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3156805",
abstract = "In life sciences, high-throughput techniques typically
lead to high-dimensional data and often the number of
covariates is much larger than the number of
observations. This inherently comes with
multicollinearity challenging a statistical analysis in
a \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2023:LRD,
author = "Chengzhuan Yang and Lincong Fang and Qian Yu and Hui
Wei",
title = "A Learning Robust and Discriminative Shape Descriptor
for Plant Species Identification",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "39--51",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3148463",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3148463",
abstract = "Plant identification based on leaf images is a widely
concerned application field in artificial intelligence
and botany. The key problem is extracting robust
discriminative features from leaf images and assigning
a measure of similarity. This study \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhou:2023:MBD,
author = "Feng Zhou and Meng-Meng Yin and Jing-Xiu Zhao and
Junliang Shang and Jin-Xing Liu",
title = "A Method Based On Dual-Network Information Fusion to
Predict {MiRNA}--Disease Associations",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "52--60",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3133006",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3133006",
abstract = "MicroRNAs (miRNAs) are single-stranded small RNAs. An
increasing number of studies have shown that miRNAs
play a vital role in many important biological
processes. However, some experimental methods to
predict unknown miRNA-disease associations (MDAs) are
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yu:2023:MPD,
author = "Qiang Yu and Xiao Zhang and Yana Hu and Shengpin Chen
and Liying Yang",
title = "A Method for Predicting {DNA} Motif Length Based On
Deep Learning",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "61--73",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3158471",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3158471",
abstract = "A DNA motif is a sequence pattern shared by the DNA
sequence segments that bind to a specific protein.
Discovering motifs in a given DNA sequence dataset
plays a vital role in studying gene expression
regulation. As an important attribute of the DNA
motif,. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Huang:2023:RFM,
author = "Weixian Huang and Kaiwen Tan and Ziye Zhang and
Jinlong Hu and Shoubin Dong",
title = "A Review of Fusion Methods for Omics and Imaging
Data",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "74--93",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3143900",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3143900",
abstract = "The development of omics data and biomedical images
has greatly advanced the progress of precision medicine
in diagnosis, treatment, and prognosis. The fusion of
omics and imaging data, i.e., omics-imaging fusion,
offers a new strategy for understanding \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ranjan:2023:SSB,
author = "Ashish Ranjan and Archana Tiwari and Akshay Deepak",
title = "A Sub-Sequence Based Approach to Protein Function
Prediction via Multi-Attention Based Multi-Aspect
Network",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "94--105",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3130923",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3130923",
abstract = "Inferring the protein function(s) via the protein
sub-sequence classification is often obstructed due to
lack of knowledge about function(s) of sub-sequences in
the protein sequence. In this regard, we develop a
novel `multi-aspect' \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bai:2023:AKD,
author = "Jun Bai and Chuantao Yin and Jianfei Zhang and Yanmeng
Wang and Yi Dong and Wenge Rong and Zhang Xiong",
title = "Adversarial Knowledge Distillation Based Biomedical
Factoid Question Answering",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "106--118",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3161032",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3161032",
abstract = "Biomedical factoid question answering is an essential
application for biomedical information sharing.
Recently, neural network based approaches have shown
remarkable performance for this task. However, due to
the scarcity of annotated data which requires
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bohnsack:2023:AFS,
author = "Katrin Sophie Bohnsack and Marika Kaden and Julia Abel
and Thomas Villmann",
title = "Alignment-Free Sequence Comparison: a Systematic
Survey From a Machine Learning Perspective",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "119--135",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3140873",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3140873",
abstract = "The encounter of large amounts of biological sequence
data generated during the last decades and the
algorithmic and hardware improvements have offered the
possibility to apply machine learning techniques in
bioinformatics. While the machine learning \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Joseph:2023:ACM,
author = "Steffy Maria Joseph and P. S. Sathidevi",
title = "An Automated {cDNA} Microarray Image Analysis for the
Determination of Gene Expression Ratios",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "136--150",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3135650",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3135650",
abstract = "This paper proposes a fully automated technique for
cDNA microarray image analysis. Initially, an effective
preprocessing stage combined with gridding is built to
get the individual spot regions of images. Current work
begins with the proposal of a new \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Iravani:2023:IDL,
author = "Sahar Iravani and Tim O. F. Conrad",
title = "An Interpretable Deep Learning Approach for Biomarker
Detection in {LC-MS} Proteomics Data",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "151--161",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3141656",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3141656",
abstract = "Analyzing mass spectrometry-based proteomics data with
deep learning (DL) approaches poses several challenges
due to the high dimensionality, low sample size, and
high level of noise. Additionally, DL-based workflows
are often hindered to be integrated \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jha:2023:AEM,
author = "Kanchan Jha and Sriparna Saha",
title = "Analyzing Effect of Multi-Modality in Predicting
Protein-Protein Interactions",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "162--173",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3157531",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3157531",
abstract = "Nowadays, multiple sources of information about
proteins are available such as protein sequences, 3D
structures, Gene Ontology (GO), etc. Most of the works
on protein-protein interaction (PPI) identification had
utilized these information about proteins, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ragi:2023:AID,
author = "Shankarachary Ragi and Md Hafizur Rahman and Jamison
Duckworth and Kalimuthu Jawaharraj and Parvathi Chundi
and Venkataramana Gadhamshetty",
title = "Artificial Intelligence-Driven Image Analysis of
Bacterial Cells and Biofilms",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "174--184",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3138304",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3138304",
abstract = "The current study explores an artificial intelligence
framework for measuring the structural features from
microscopy images of the bacterial biofilms. {$<$
italic$>$Desulfovibrio} {alaskensis$<$}/{italic$>$} G20
(DA-G20) grown on mild steel surfaces is used as a
model \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2023:CGI,
author = "Hao Zhang and Chuanxu Yan and Yewei Xia and Jihong
Guan and Shuigeng Zhou",
title = "Causal Gene Identification Using Non-Linear
Regression-Based Independence Tests",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "185--195",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3149864",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3149864",
abstract = "With the development of biomedical techniques in the
past decades, causal gene identification has become one
of the most promising applications in human
genome-based business, which can help doctors to
evaluate the risk of certain genetic diseases and
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Anjum:2023:CMA,
author = "Naser Anjum and Raian Latif Nabil and Rakibul Islam
Rafi and Md. Shamsuzzoha Bayzid and M. Saifur Rahman",
title = "{CD-MAWS}: an Alignment-Free Phylogeny Estimation
Method Using Cosine Distance on Minimal Absent Word
Sets",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "196--205",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3136792",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3136792",
abstract = "Multiple sequence alignment has been the traditional
and well established approach of sequence analysis and
comparison, though it is time and memory consuming. As
the scale of sequencing data is increasing day by day,
the importance of faster yet accurate \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Pham:2023:CBE,
author = "Tuan D. Pham",
title = "Classification of \bioname{Caenorhabditis elegans}
Locomotion Behaviors With Eigenfeature-Enhanced Long
Short-Term Memory Networks",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "206--216",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3153668",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3153668",
abstract = "The free-living nematode {$<$ italic$>$Caenorhabditis}
{elegans$<$}/{italic$>$} is an ideal model for
understanding behavior and networks of neurons.
Experimental and quantitative analyses of neural
circuits and behavior have led to system-level
understanding of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Manipur:2023:CDP,
author = "Ichcha Manipur and Maurizio Giordano and Marina
Piccirillo and Seetharaman Parashuraman and Lucia
Maddalena",
title = "Community Detection in Protein--Protein Interaction
Networks and Applications",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "217--237",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3138142",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3138142",
abstract = "The ability to identify and characterize not only the
protein-protein interactions but also their internal
modular organization through network analysis is
fundamental for understanding the mechanisms of
biological processes at the molecular level. Indeed,.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Qureshi:2023:CMA,
author = "Rizwan Qureshi and Bin Zou and Tanvir Alam and Jia Wu
and Victor H. F. Lee and Hong Yan",
title = "Computational Methods for the Analysis and Prediction
of {EGFR}-Mutated Lung Cancer Drug Resistance: Recent
Advances in Drug Design, Challenges and Future
Prospects",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "238--255",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3141697",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3141697",
abstract = "Lung cancer is a major cause of cancer deaths
worldwide, and has a very low survival rate. Non-small
cell lung cancer (NSCLC) is the largest subset of lung
cancers, which accounts for about 85\% of all cases. It
has been well established that a \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2023:CPD,
author = "Jingbo Yang and Denan Zhang and Yiyang Cai and Kexin
Yu and Mingming Li and Lei Liu and Xiujie Chen",
title = "Computational Prediction of Drug Phenotypic Effects
Based on Substructure-Phenotype Associations",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "256--265",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3155453",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3155453",
abstract = "Identifying drug phenotypic effects, including
therapeutic effects and adverse drug reactions (ADRs),
is an inseparable part for evaluating the potentiality
of new drug candidates (NDCs). However, current
computational methods for predicting phenotypic
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lee:2023:CCP,
author = "Wook Lee and Seokwoo Lee and Kyungsook Han",
title = "Constructing a Cancer Patient-Specific Network Based
on Second-Order Partial Correlations of Gene Expression
and {DNA} Methylation",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "266--276",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3145796",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3145796",
abstract = "Typically patient-specific gene networks are
constructed with gene expression data only. Such
networks cannot distinguish direct gene interactions
from indirect interactions via others such as the
effect of epigenetic events to gene activity. There is
an \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2023:CNN,
author = "Zhi-Hao Liu and Cun-Mei Ji and Jian-Cheng Ni and
Yu-Tian Wang and Li-Juan Qiao and Chun-Hou Zheng",
title = "Convolution Neural Networks Using Deep Matrix
Factorization for Predicting Circrna-Disease
Association",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "277--284",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3138339",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3138339",
abstract = "CircRNAs have a stable structure, which gives them a
higher tolerance to nucleases. Therefore, the
properties of circular RNAs are beneficial in disease
diagnosis. However, there are few known associations
between circRNAs and disease. Biological \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Hua:2023:CER,
author = "Yang Hua and Xiaoning Song and Zhenhua Feng and
Xiao-Jun Wu and Josef Kittler and Dong-Jun Yu",
title = "{CPInformer} for Efficient and Robust Compound-Protein
Interaction Prediction",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "285--296",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3144008",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3144008",
abstract = "Recently, deep learning has become the mainstream
methodology for Compound-Protein Interaction (CPI)
prediction. However, the existing compound-protein
feature extraction methods have some issues that limit
their performance. First, graph networks are \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2023:CSS,
author = "Kailong Li and Lijun Quan and Yelu Jiang and Yan Li
and Yiting Zhou and Tingfang Wu and Qiang Lyu",
title = "{ctP$^2$ISP}: Protein--Protein Interaction Sites
Prediction Using Convolution and Transformer With Data
Augmentation",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "297--306",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3154413",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3154413",
abstract = "Protein--protein interactions are the basis of many
cellular biological processes, such as cellular
organization, signal transduction, and immune response.
Identifying protein--protein interaction sites is
essential for understanding the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Du:2023:DML,
author = "Xiuquan Du and Jiajia Hu",
title = "Deep Multi-Label Joint Learning for {RNA} and
{DNA-Binding} Proteins Prediction",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "307--320",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3150280",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3150280",
abstract = "The recognition of DNA- (DBPs) and RNA-binding
proteins (RBPs) is not only conducive to understanding
cell function, but also a challenging task. Previous
studies have shown that these proteins are usually
considered separately due to different binding
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2023:DTL,
author = "Xiao-Hui Yang and Zi-Jun Xi and Jie-Ping Li and
Xin-Lei Feng and Xiao-Hong Zhu and Si-Yi Guo and
Chun-Peng Song",
title = "Deep Transfer Learning-Based Multi-Object Detection
for Plant Stomata Phenotypic Traits Intelligent
Recognition",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "321--329",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3137810",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3137810",
abstract = "Plant stomata phenotypic traits can provide a basis
for enhancing crop tolerance in adversity. Manually
counting the number of stomata and measuring the height
and width of stomata obviously cannot satisfy the
high-throughput data. How to detect and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Uner:2023:DDL,
author = "Onur Can Uner and Halil Ibrahim Kuru and R. Gokberk
Cinbis and Oznur Tastan and A. Ercument Cicek",
title = "{DeepSide}: a Deep Learning Approach for Drug Side
Effect Prediction",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "330--339",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3141103",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3141103",
abstract = "Drug failures due to unforeseen adverse effects at
clinical trials pose health risks for the participants
and lead to substantial financial losses. Side effect
prediction algorithms have the potential to guide the
drug design process. LINCS L1000 dataset \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Huang:2023:DGE,
author = "Zimo Huang and Jun Wang and Zhongmin Yan and Lin Wan
and Maozu Guo",
title = "Differential Gene Expression Prediction by Ensemble
Deep Networks on Histone Modification Data",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "340--351",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3139634",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3139634",
abstract = "Predicting differential gene expression (DGE) from
Histone modifications (HM) signal is crucial to
understand how HM controls cell functional
heterogeneity through influencing differential gene
regulation. Most existing prediction methods use
fixed-length \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lin:2023:ENN,
author = "Jinghang Lin and Xiaoran Tong and Chenxi Li and Qing
Lu",
title = "Expectile Neural Networks for Genetic Data Analysis of
Complex Diseases",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "352--359",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3146795",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3146795",
abstract = "The genetic etiologies of common diseases are highly
complex and heterogeneous. Classic methods, such as
linear regression, have successfully identified
numerous variants associated with complex diseases.
Nonetheless, for most diseases, the identified
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Williams:2023:FDS,
author = "Lucia Williams and Alexandru I. Tomescu and Brendan
Mumey",
title = "Flow Decomposition With Subpath Constraints",
journal = j-TCBB,
volume = "20",
number = "1",
pages = "360--370",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3147697",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:42 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3147697",
abstract = "Flow network decomposition is a natural model for
problems where we are given a flow network arising from
superimposing a set of weighted paths and would like to
recover the underlying data, i.e., {$<$ italic$>$
decompose$<$}/{italic$>$} the flow into the original
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gonzalez-Crespo:2023:BMT,
author = "Isabel Gonz{\'a}lez-Crespo and Antonio
G{\'o}mez-Caama{\~n}o and {\'O}scar L{\'o}pez Pouso and
John D. Fenwick and Juan Pardo-Montero",
title = "A Biomathematical Model of Tumor Response to
Radioimmunotherapy With {$ \alpha $PDL1} and {$ \alpha
$CTLA4}",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "808--821",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3174454",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3174454",
abstract = "There is evidence of synergy between radiotherapy and
immunotherapy. Radiotherapy can increase liberation of
tumor antigens, causing activation of antitumor
T-cells. This effect can be boosted with immunotherapy.
Radioimmunotherapy has potential to \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2023:CMU,
author = "Xiong Li and Yangkai Lin and Chengwang Xie and Zejun
Li and Min Chen and Peng Wang and Juan Zhou",
title = "A Clustering Method Unifying Cell-Type Recognition and
Subtype Identification for Tumor Heterogeneity
Analysis",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "822--832",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3203185",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3203185",
abstract = "The rapid development of single-cell technology has
opened up a whole new perspective for identifying cell
types in multicellular organisms and understanding the
relationships between them. Distinguishing different
cell types and subtypes can identify the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2023:DLF,
author = "Min Li and Wenbo Shi and Fuhao Zhang and Min Zeng and
Yaohang Li",
title = "A Deep Learning Framework for Predicting Protein
Functions With Co-Occurrence of {GO} Terms",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "833--842",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3170719",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3170719",
abstract = "The understanding of protein functions is critical to
many biological problems such as the development of new
drugs and new crops. To reduce the huge gap between the
increase of protein sequences and annotations of
protein functions, many methods have \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Peng:2023:AMC,
author = "Xiaoqing Peng and Wenjin Zhang and Wanxin Cui and
Binrong Ding and Qingtong Lyu and Jianxin Wang",
title = "{ADmeth}: a Manually Curated Database for the
Differential Methylation in {Alzheimer}'s Disease",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "843--851",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3178087",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3178087",
abstract = "Alzheimer's disease (AD) is the most common
neurodegenerative disease. More and more evidence show
that DNA methylation is closely related to the
pathological mechanism of AD. Many AD-associated
differentially methylated genes, regions and CpG
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2023:ADT,
author = "Qichang Zhao and Guihua Duan and Mengyun Yang and
Zhongjian Cheng and Yaohang Li and Jianxin Wang",
title = "{AttentionDTA}: Drug-Target Binding Affinity
Prediction by Sequence-Based Deep Learning With
Attention Mechanism",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "852--863",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3170365",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3170365",
abstract = "The identification of drug--target relations (DTRs) is
substantial in drug development. A large number of
methods treat DTRs as drug-target interactions (DTIs),
a binary classification problem. The main drawback of
these methods are the lack of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Si:2023:BAM,
author = "Jiasheng Si and Liu Sun and Deyu Zhou and Jie Ren and
Lin Li",
title = "Biomedical Argument Mining Based on Sequential
Multi-Task Learning",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "864--874",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3173447",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3173447",
abstract = "Biomedical argument mining aims to automatically
identify and extract the argumentative structure in
biomedical text. It helps to determine not only what
positions people adopt, but also why they hold such
opinions, which provides valuable insights into
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ghasemi:2023:CDG,
author = "Mahdieh Ghasemi and Maseud Rahgozar and Kaveh
Kavousi",
title = "Complex Disease Genes Identification Using a
Heterogeneous Network Embedding Approach",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "875--882",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3175598",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3175598",
abstract = "Finding the causal relation between a gene and a
disease using experimental approaches is a
time-consuming and expensive task. However,
computational approaches are cost-efficient methods for
identifying candidate genes. This article proposes a
new \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jee:2023:DNM,
author = "Dong Jun Jee and Yixin Kong and Hyonho Chun",
title = "Deep Nonnegative Matrix Factorization Using a
Variational Autoencoder With Application to Single-Cell
{RNA} Sequencing Data",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "883--893",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3172723",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3172723",
abstract = "Single-cell RNA sequencing is used to analyze the gene
expression data of individual cells, thereby adding to
existing knowledge of biological phenomena.
Accordingly, this technology is widely used in numerous
biomedical studies. Recently, the variational
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Altuntas:2023:DAC,
author = "Volkan Altuntas",
title = "Diffusion Alignment Coefficient ({DAC}): a Novel
Similarity Metric for Protein-Protein Interaction
Network",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "894--903",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3185406",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3185406",
abstract = "Interaction networks can be used to predict the
functions of unknown proteins using known interactions
and proteins with known functions. Many graph theory or
diffusion-based methods have been proposed, using the
assumption that the topological properties \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Rehman:2023:DMI,
author = "Mobeen Ur Rehman and Hilal Tayara and Kil To Chong",
title = "{DL-m6A}: Identification of {N6-Methyladenosine} Sites
in Mammals Using Deep Learning Based on Different
Encoding Schemes",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "904--911",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3192572",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3192572",
abstract = "N6-methyladenosine (m6A) is a common
post-transcriptional alteration that plays a critical
function in a variety of biological processes. Although
experimental approaches for identifying m6A sites have
been developed and deployed, they are currently
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yu:2023:DPD,
author = "Zhong-Ze Yu and Chun-Xiang Peng and Jun Liu and Biao
Zhang and Xiao-Gen Zhou and Gui-Jun Zhang",
title = "{DomBpred}: Protein Domain Boundary Prediction Based
on Domain-Residue Clustering Using Inter-Residue
Distance",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "912--922",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3175905",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3175905",
abstract = "Domain boundary prediction is one of the most
important problems in the study of protein structure
and function, especially for large proteins. At
present, most domain boundary prediction methods have
low accuracy and limitations in dealing with multi-.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Huang:2023:DNC,
author = "Xin Huang and Benzhe Su and Chenbo Zhu and Xinyu He
and Xiaohui Lin",
title = "Dynamic Network Construction for Identifying Early
Warning Signals Based On a Data-Driven Approach: Early
Diagnosis Biomarker Discovery for Gastric Cancer",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "923--931",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3176319",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3176319",
abstract = "During the development of complex diseases, there is a
critical transition from one status to another at a
tipping point, which can be an early indicator of
disease deterioration. To effectively enhance the
performance of early risk identification, a \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lin:2023:EIC,
author = "Xuan Lin and Zhe Quan and Zhi-Jie Wang and Yan Guo and
Xiangxiang Zeng and Philip S. Yu",
title = "Effectively Identifying Compound-Protein Interaction
Using Graph Neural Representation",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "932--943",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3198003",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3198003",
abstract = "Effectively identifying compound-protein interactions
(CPIs) is crucial for new drug design, which is an
important step in silico drug discovery. Current
machine learning methods for CPI prediction mainly use
one-demensional (1D) compound/protein strings
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Fan:2023:ECL,
author = "Yongxian Fan and Guicong Sun and Xiaoyong Pan",
title = "{ELMo4m6A}: a Contextual Language Embedding-Based
Predictor for Detecting {RNA} {N6}-Methyladenosine
Sites",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "944--954",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3173323",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3173323",
abstract = "N6-methyladenosine (m6A) is a universal
post-transcriptional modification of RNAs, and it is
widely involved in various biological processes.
Identifying m6A modification sites accurately is
indispensable to further investigate m6A-mediated
biological \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Rashid:2023:ESP,
author = "Shamima Rashid and Suresh Sundaram and Chee Keong
Kwoh",
title = "Empirical Study of Protein Feature Representation on
Deep Belief Networks Trained With Small Data for
Secondary Structure Prediction",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "955--966",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3168676",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3168676",
abstract = "Protein secondary structure (SS) prediction is a
classic problem of computational biology and is widely
used in structural characterization and to infer
homology. While most SS predictors have been trained on
thousands of sequences, a previous approach \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2023:EFI,
author = "Chao Wang and Quan Zou and Ying Ju and Hua Shi",
title = "{Enhancer-FRL}: Improved and Robust Identification of
Enhancers and Their Activities Using Feature
Representation Learning",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "967--975",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3204365",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3204365",
abstract = "Enhancers are crucial for precise regulation of gene
expression, while enhancer identification and strength
prediction are challenging because of their free
distribution and tremendous number of similar fractions
in the genome. Although several \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2023:EDD,
author = "Shichao Liu and Yang Zhang and Yuxin Cui and Yang Qiu
and Yifan Deng and Zhongfei Zhang and Wen Zhang",
title = "Enhancing Drug-Drug Interaction Prediction Using Deep
Attention Neural Networks",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "976--985",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3172421",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3172421",
abstract = "Drug-drug interactions are one of the main concerns in
drug discovery. Accurate prediction of drug-drug
interactions plays a key role in increasing the
efficiency of drug research and safety when multiple
drugs are co-prescribed. With various data sources
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Shen:2023:EED,
author = "Yang Shen and Jinlin Zhu and Zhaohong Deng and Wenwei
Lu and Hongchao Wang",
title = "{EnsDeepDP}: an Ensemble Deep Learning Approach for
Disease Prediction Through Metagenomics",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "986--998",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3201295",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3201295",
abstract = "A growing number of studies show that the human
microbiome plays a vital role in human health and can
be a crucial factor in predicting certain human
diseases. However, microbiome data are often
characterized by the limited samples and
high-dimensional \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{DiPersia:2023:EIP,
author = "Leandro {Di Persia} and Tiago Lopez and Agustin Arce
and Diego H. Milone and Georgina Stegmayer",
title = "{exp2GO}: Improving Prediction of Functions in the
Gene Ontology With Expression Data",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "999--1008",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3167245",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3167245",
abstract = "The computational methods for the prediction of gene
function annotations aim to automatically find
associations between a gene and a set of Gene Ontology
(GO) terms describing its functions. Since the
hand-made curation process of novel annotations and
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Castiglione:2023:EDR,
author = "Filippo Castiglione and Christine Nardini and Elia
Onofri and Marco Pedicini and Paolo Tieri",
title = "Explainable Drug Repurposing Approach From Biased
Random Walks",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "1009--1019",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3191392",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3191392",
abstract = "Drug repurposing is a highly active research area,
aiming at finding novel uses for drugs that have been
previously developed for other therapeutic purposes.
Despite the flourishing of methodologies, success is
still partial, and different approaches \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Nguyen:2023:EBB,
author = "Tri Minh Nguyen and Thomas P. Quinn and Thin Nguyen
and Truyen Tran",
title = "Explaining Black Box Drug Target Prediction Through
Model Agnostic Counterfactual Samples",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "1020--1029",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3190266",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3190266",
abstract = "Many high-performance DTA deep learning models have
been proposed, but they are mostly black-box and thus
lack human interpretability. Explainable AI (XAI) can
make DTA models more trustworthy, and allows to distill
biological knowledge from the models. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Tang:2023:FMP,
author = "Chunyan Tang and Cheng Zhong and Mian Wang and
Fengfeng Zhou",
title = "{FMGNN}: a Method to Predict Compound-Protein
Interaction With Pharmacophore Features and
Physicochemical Properties of Amino Acids",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "1030--1040",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3172340",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3172340",
abstract = "Identifying interactions between compounds and
proteins is an essential task in drug discovery. To
recommend compounds as new drug candidates, applying
the computational approaches has a lower cost than
conducting the wet-lab experiments. Machine learning-.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2023:GMD,
author = "Jiwen Liu and Zhufang Kuang and Lei Deng",
title = "{GCNPCA}: {miRNA-Disease} Associations Prediction
Algorithm Based on Graph Convolutional Neural
Networks",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "1041--1052",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3203564",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3203564",
abstract = "A growing number of studies have confirmed the
important role of microRNAs (miRNAs) in human diseases
and the aberrant expression of miRNAs affects the onset
and progression of human diseases. The discovery of
disease-associated miRNAs as new biomarkers \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Tian:2023:GGC,
author = "Zhen Tian and Haichuan Fang and Zhixia Teng and
Yangdong Ye",
title = "{GOGCN}: Graph Convolutional Network on Gene Ontology
for Functional Similarity Analysis of Genes",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "1053--1064",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3181300",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3181300",
abstract = "The measurement of gene functional similarity plays a
critical role in numerous biological applications, such
as gene clustering, the construction of gene similarity
networks. However, most existing approaches still rely
heavily on traditional \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chu:2023:GTD,
author = "Thang Chu and Thuy Trang Nguyen and Bui Duong Hai and
Quang Huy Nguyen and Tuan Nguyen",
title = "Graph Transformer for Drug Response Prediction",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "1065--1072",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3206888",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3206888",
abstract = "{$<$ italic$>$Background$<$}/{italic$>$}: Previous
models have shown that learning drug features from
their graph representation is more efficient than
learning from their strings or numeric representations.
Furthermore, integrating multi-omics data of cell lines
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Herrera-Romero:2023:GIT,
author = "Bryan Herrera-Romero and Diego Almeida-Gal{\'a}rraga
and Graciela M. Salum and Fernando Villalba-Meneses and
Marco Esteban Gudi{\~n}o-Gomezjurado",
title = "{GUSignal}: an Informatics Tool to Analyze
Glucuronidase Gene Expression in \bioname{Arabidopsis
thaliana} Roots",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "1073--1080",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3190427",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3190427",
abstract = "The {$<$ italic$>$ uidA$<$}/{italic$>$} gene codifies
for a glucuronidase (GUS) enzyme which has been used as
a biotechnological tool during the last years. When
{$<$ italic$>$ uidA$<$}/{italic$>$} gene is fused to a
gene's promotor region, it is possible to evaluate the
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sun:2023:HPS,
author = "Junwei Sun and Haoping Ji and Yingcong Wang and
Yanfeng Wang",
title = "Hybrid Projective Synchronization via {PI} Controller
Based on {DNA} {Strand} Displacement",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "1081--1091",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3190397",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3190397",
abstract = "Classical three-variable chaotic system coupling
synchronization has been implemented in previous work
based on DNA strand displacement (DSD). Herein, by
using DSD reactions as the foundation, a proportional
integral (PI) controller for chaotic system is
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zheng:2023:ITU,
author = "Vicky Zheng and Ahmet Erdem Sariyuce and Jaroslaw
Zola",
title = "Identifying Taxonomic Units in Metagenomic {DNA}
Streams on Mobile Devices",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "1092--1103",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3172661",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3172661",
abstract = "With the emergence of portable DNA sequencers, such as
Oxford Nanopore Technology MinION, metagenomic DNA
sequencing can be performed in real-time and directly
in the field. However, because metagenomic DNA analysis
tasks, e.g., classification, taxonomic \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gormez:2023:IMD,
author = "Yasin G{\"o}rmez and Zafer Aydin",
title = "{IGPRED-MultiTask}: a Deep Learning Model to Predict
Protein Secondary Structure, Torsion Angles and Solvent
Accessibility",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "1104--1113",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3191395",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3191395",
abstract = "Protein secondary structure, solvent accessibility and
torsion angle predictions are preliminary steps to
predict 3D structure of a protein. Deep learning
approaches have achieved significant improvements in
predicting various features of protein \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Du:2023:IBQ,
author = "Yongping Du and Jingya Yan and Yuxuan Lu and Yiliang
Zhao and Xingnan Jin",
title = "Improving Biomedical Question Answering by Data
Augmentation and Model Weighting",
journal = j-TCBB,
volume = "20",
number = "2",
pages = "1114--1124",
month = mar,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3171388",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3171388",
abstract = "Biomedical Question Answering aims to extract an
answer to the given question from a biomedical context.
Due to the strong professionalism of specific domain,
it's more difficult to build large-scale datasets for
specific domain question answering. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Martin-Vide:2023:ACB,
author = "Carlos Mart{\'\i}n-Vide and Miguel A.
Vega-Rodr{\'\i}guez",
title = "Algorithms for Computational Biology: Eighth Edition",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1626--1627",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3218808",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3218808",
abstract = "This special section of {$<$ italic$>$IEEE}/ACM
Transactions on Computational Biology and
{Bioinformatics$<$}/{italic$>$} presents extended
versions of some of the best papers accepted at the
Eighth International Conference on Algorithms for
Computational Biology, AlCoB \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Alexandrino:2023:RID,
author = "Alexsandro Oliveira Alexandrino and Klairton Lima
Brito and Andre Rodrigues Oliveira and Ulisses Dias and
Zanoni Dias",
title = "Reversal and Indel Distance With Intergenic Region
Information",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1628--1640",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3215615",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3215615",
abstract = "Recent works on genome rearrangements have shown that
incorporating intergenic region information along with
gene order in models provides better estimations for
the rearrangement distance than using gene order alone.
The reversal distance is one of the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Brito:2023:GRD,
author = "Klairton Lima Brito and Alexsandro Oliveira
Alexandrino and Andre Rodrigues Oliveira and Ulisses
Dias and Zanoni Dias",
title = "Genome Rearrangement Distance With a Flexible
Intergenic Regions Aspect",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1641--1653",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3165443",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3165443",
abstract = "Most mathematical models for genome rearrangement
problems have considered only gene order. In this way,
the rearrangement distance considering some set of
events, such as reversal and transposition events, is
commonly defined as the minimum number of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Landry:2023:DPN,
author = "Kaari Landry and Aivee Teodocio and Manuel Lafond and
Olivier Tremblay-Savard",
title = "Defining Phylogenetic Network Distances Using Cherry
Operations",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1654--1666",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3162991",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3162991",
abstract = "In phylogenetic networks, picking a cherry consists of
removing a leaf that shares a parent with another leaf,
or removing a reticulate edge whose endpoints are
parents of leaves. Cherry-picking operations were
recently shown to have several structural \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Mou:2023:STD,
author = "Chenqi Mou and Wenwen Ju",
title = "Sparse Triangular Decomposition for Computing
Equilibria of Biological Dynamic Systems Based on
Chordal Graphs",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1667--1678",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3156759",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3156759",
abstract = "Many biological systems are modeled mathematically as
dynamic systems in the form of polynomial or rational
differential equations. In this paper we apply sparse
triangular decomposition to compute the equilibria of
biological dynamic systems by \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Schaller:2023:BMG,
author = "David Schaller and Manuela Gei{\ss} and Marc Hellmuth
and Peter F. Stadler",
title = "Best Match Graphs With Binary Trees",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1679--1690",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3143870",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3143870",
abstract = "Best match graphs (BMG) are a key intermediate in
graph-based orthology detection and contain a large
amount of information on the gene tree. We provide a
near-cubic algorithm to determine whether a BMG is
binary-explainable, i.e., whether it can be \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yao:2023:IDV,
author = "Yin Yao and Martin C. Frith",
title = "Improved {DNA}-Versus-Protein Homology Search for
Protein Fossils",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1691--1699",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3177855",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3177855",
abstract = "Protein fossils, i.e., noncoding DNA descended from
coding DNA, arise frequently from transposable elements
(TEs), decayed genes, and viral integrations. They can
reveal, and mislead about, evolutionary history and
relationships. They have been detected \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zaharias:2023:LSM,
author = "Paul Zaharias and Vladimir Smirnov and Tandy Warnow",
title = "Large-Scale Multiple Sequence Alignment and the
Maximum Weight Trace Alignment Merging Problem",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1700--1712",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3191848",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3191848",
abstract = "MAGUS is a recent multiple sequence alignment method
that provides excellent accuracy on large challenging
datasets. MAGUS uses divide-and-conquer: it divides the
sequences into disjoint sets, computes alignments on
the disjoint sets, and then merges the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wu:2023:PCS,
author = "Kaitao Wu and Lexiang Wang and Bo Liu and Yang Liu and
Yadong Wang and Junyi Li",
title = "{PSPGO}: Cross-Species Heterogeneous Network
Propagation for Protein Function Prediction",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1713--1724",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3215257",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3215257",
abstract = "How to use computational methods to effectively
predict the function of proteins remains a challenge.
Most prediction methods based on single species or
single data source have some limitations: the former
need to train different models for different \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Villaverde:2023:APU,
author = "Alejandro F. Villaverde and Elba Raim{\'u}ndez and Jan
Hasenauer and Julio R. Banga",
title = "Assessment of Prediction Uncertainty Quantification
Methods in Systems Biology",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1725--1736",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3213914",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3213914",
abstract = "Biological processes are often modelled using ordinary
differential equations. The unknown parameters of these
models are estimated by optimizing the fit of model
simulation and experimental data. The resulting
parameter estimates inevitably possess some \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Song:2023:IPL,
author = "Jinmiao Song and Shengwei Tian and Long Yu and Qimeng
Yang and Yuanxu Wang and Qiguo Dai and Xiaodong Duan",
title = "{ISLMI}: Predicting {lncRNA--miRNA} Interactions Based
on Information Injection and Second-Order Graph
Convolution Network",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1737--1745",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3215151",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3215151",
abstract = "Studies have shown that IncRNA-miRNA interactions can
affect cellular expression at the level of gene
molecules through a variety of regulatory mechanisms
and have important effects on the biological activities
of living organisms. Several biomolecular \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Kang:2023:HTS,
author = "Yan Kang and Haining Wang and Bin Pu and Liu Tao and
Jianguo Chen and Philip S. Yu",
title = "A Hybrid Two-Stage Teaching-Learning-Based
Optimization Algorithm for Feature Selection in
Bioinformatics",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1746--1760",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3215129",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3215129",
abstract = "The `curse of dimensionality' brings new challenges to
the feature selection (FS) problem, especially in
bioinformatics filed. In this paper, we propose a
hybrid Two-Stage Teaching-Learning-Based Optimization
(TS-TLBO) algorithm to improve \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Das:2023:BBR,
author = "Pranab Das and Yogita Thakran and S. R. Ngamwal Anal
and Vipin Pal and Anju Yadav",
title = "{BRMCF}: Binary Relevance and {MLSMOTE} Based
Computational Framework to Predict Drug Functions From
Chemical and Biological Properties of Drugs",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1761--1773",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3215645",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3215645",
abstract = "In silico machine learning based prediction of drug
functions considering the drug properties would
substantially enhance the speed and reduce the cost of
identifying promising drug leads. The drug function
prediction capability of different drug \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xi:2023:LPL,
author = "Wen-Yu Xi and Feng Zhou and Ying-Lian Gao and Jin-Xing
Liu and Chun-Hou Zheng",
title = "{LDCMFC}: Predicting Long Non-Coding {RNA} and Disease
Association Using Collaborative Matrix Factorization
Based on Correntropy",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1774--1782",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3215194",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3215194",
abstract = "With the development of bioinformatics, the important
role played by lncRNAs in various intractable diseases
has aroused the interest of many experts. In recent
studies, researchers have found that several human
diseases are related to lncRANs. Moreover, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bhattacharyya:2023:BAD,
author = "Ramkishore Bhattacharyya",
title = "Bidirectional Association Discovery Leads to Precise
Identification of Lung Cancer Biomarkers and Genome
Taxa Class",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1783--1794",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3215630",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3215630",
abstract = "Identifying proximity between pairs of expression
vectors is one of the fundamental requirements in
machine learning and data mining algorithms. We propose
a new metric, Bidirectional Association Similarity
({$<$ italic$>$BiAS$<$}/{italic$>$}), to measure the
degree \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Tchendji:2023:PTS,
author = "Vianney Kengne Tchendji and Franklin Ingrid Kamga
Youmbi and Cl{\'e}mentin Tayou Djamegni and Jerry
Lacmou Zeutouo",
title = "A Parallel Tiled and Sparsified Four-{Russians}
Algorithm for {Nussinov}'s {RNA} Folding",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1795--1806",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3216826",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3216826",
abstract = "To enable extensive research on the ribonucleic acid
(RNA) molecule, predicting its spatial structure stands
as a much-valued research field. In this regard,
Nussinov and Jacobson published the (now) {$<$
italic$>$ de} {facto$<$}/{italic$>$} solution to
predict the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xu:2023:IGR,
author = "Jie Xu and Guanxue Yang and Guohai Liu and Hui Liu",
title = "Inferring Gene Regulatory Networks via Ensemble Path
Consistency Algorithm Based on Conditional Mutual
Information",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1807--1816",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3220581",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3220581",
abstract = "Utilizing gene expression data to infer gene
regulatory networks has received great attention
because gene regulation networks can reveal complex
life phenomena by studying the interaction mechanism
among nodes. However, the reconstruction of large-scale
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Das:2023:EDC,
author = "Pradeep Kumar Das and Biswajeet Sahoo and Sukadev
Meher",
title = "An Efficient Detection and Classification of Acute
Leukemia Using Transfer Learning and Orthogonal Softmax
Layer-Based Model",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1817--1828",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3218590",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3218590",
abstract = "For the early diagnosis of hematological disorders
like blood cancer, microscopic analysis of blood cells
is very important. Traditional deep CNNs lead to
overfitting when it receives small medical image
datasets such as ALLIDB1, ALLIDB2, and ASH. This
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Maia:2023:IEC,
author = "Marcelo Rodrigues de Holanda Maia and Alexandre
Plastino and Alex Freitas and Jo{\~a}o Pedro de
Magalh{\~a}es",
title = "Interpretable Ensembles of Classifiers for Uncertain
Data With Bioinformatics Applications",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1829--1841",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3218588",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3218588",
abstract = "Data uncertainty remains a challenging issue in many
applications, but few classification algorithms can
effectively cope with it. An ensemble approach for
uncertain categorical features has recently been
proposed, achieving promising results. It consists
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2023:FIC,
author = "Shutao Chen and Lin Zhang and Xiangzhi Chen and Hui
Liu",
title = "{FGFICA}: Independent Component Analysis of Fusion
Genomic Features for Mining Epi-Transcriptome Profiling
Data",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1842--1853",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3220552",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3220552",
abstract = "Existing studies indicate that in-depth studies of the
{$<$ italic$>$N$<$ sup$>$6$<$}/{sup$ > $$ <$ } / {i t a
l i c $ >$ } - methyladenosine ({m $ <$ s u p $ >$6$ <$
} / {s u p$ >$ A}) co - methylation patterns in epi -
transcriptome profiling data may contribute to
understanding its complex regulatory mechanisms. In
order \ldots {}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yu:2023:DDG,
author = "Hui Yu and KangKang Li and JianYu Shi",
title = "{DGANDDI}: Double Generative Adversarial Networks for
Drug-Drug Interaction Prediction",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1854--1863",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3219883",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3219883",
abstract = "Co-administration of multiple drugs may cause adverse
drug interactions and side effects that damage the
body. Therefore, accurate prediction of drug-drug
interaction (DDI) events is of great importance.
Recently, many computational methods have been
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bai:2023:IBR,
author = "Jun Bai and Chuantao Yin and Zimeng Wu and Jianfei
Zhang and Yanmeng Wang and Guanyi Jia and Wenge Rong
and Zhang Xiong",
title = "Improving Biomedical {ReQA} With Consistent
{NLI}-Transfer and Post-Whitening",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1864--1875",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3219375",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3219375",
abstract = "Retrieval Question Answering (ReQA) is an essential
mechanism of information sharing which aims to find the
answer to a posed question from large-scale candidates.
Currently, the most efficient solution is Dual-Encoder
which has shown great potential in \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lin:2023:ASD,
author = "Xiangbin Lin and Weizhuang Kong and Jianxiu Li and
Xuexiao Shao and Changting Jiang and Ruilan Yu and
Xiaowei Li and Bin Hu",
title = "Aberrant Static and Dynamic Functional Brain Network
in Depression Based on {EEG} Source Localization",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1876--1889",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3222592",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3222592",
abstract = "Objective. Depression is accompanied by abnormalities
in large-scale functional brain networks. This paper
combined static and dynamic methods to analyze the
abnormal topology and changes of functional
connectivity network (FCN) of depression. Methods. We
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Tripathi:2023:AGC,
author = "Prasun Chandra Tripathi and Soumen Bag",
title = "An Attention-Guided {CNN} Framework for Segmentation
and Grading of Glioma Using {$3$D} {MRI} Scans",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1890--1904",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3220902",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3220902",
abstract = "Glioma has emerged as the deadliest form of brain
tumor for human beings. Timely diagnosis of these
tumors is a major step towards effective oncological
treatment. Magnetic Resonance Imaging (MRI) typically
offers a non-invasive inspection of brain \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2023:GID,
author = "Shuhui Liu and Yupei Zhang and Xuequn Shang",
title = "{GLassonet}: Identifying Discriminative Gene Sets
Among Molecular Subtypes of Breast Cancer",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1905--1916",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3220623",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3220623",
abstract = "Breast cancer is a heterogeneous disease caused by
various alterations in the genome or transcriptome.
Molecular subtypes of breast cancer have been reported,
but useful biomarkers remain to be identified to
uncover underlying biological mechanisms and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2023:DCP,
author = "Hegang Chen and Yuyin Lu and Yuedong Yang and Yanghui
Rao",
title = "A Drug Combination Prediction Framework Based on Graph
Convolutional Network and Heterogeneous Information",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1917--1925",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3224734",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3224734",
abstract = "Combination therapy, which can improve therapeutic
efficacy and reduce side effects, plays an important
role in the treatment of complex diseases. Yet, a large
number of possible combinations among candidate
compounds limits our ability to identify \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2023:PRD,
author = "Shengli Zhang and Yuanyuan Jing",
title = "{PreVFs-RG}: a Deep Hybrid Model for Identifying
Virulence Factors Based on Residual Block and Gated
Recurrent Unit",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1926--1934",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3223038",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3223038",
abstract = "Many infectious diseases are caused by bacterial
pathogens. The pathogenic mechanisms of bacterial
pathogens are complex and it is usually caused by
virulence factors (VFs) in many cases. Whether VFs
exist is the main difference between the genomes of
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2023:CPC,
author = "Minghua Zhao and Min Yuan and Yaning Yang and Steven
X. Xu",
title = "{CPGL}: Prediction of Compound-Protein Interaction by
Integrating Graph Attention Network With Long
Short-Term Memory Neural Network",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1935--1942",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3225296",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3225296",
abstract = "Recent advancements of artificial intelligence based
on deep learning algorithms have made it possible to
computationally predict compound-protein interaction
(CPI) without conducting laboratory experiments. In
this manuscript, we integrated a graph \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2023:GPD,
author = "Qichang Zhao and Guihua Duan and Haochen Zhao and Kai
Zheng and Yaohang Li and Jianxin Wang",
title = "{GIFDTI}: Prediction of Drug-Target Interactions Based
on Global Molecular and Intermolecular Interaction
Representation Learning",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1943--1952",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3225423",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3225423",
abstract = "Drug discovery and drug repurposing often rely on the
successful prediction of drug-target interactions
(DTIs). Recent advances have shown great promise in
applying deep learning to drug-target interaction
prediction. One challenge in building deep \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Dang:2023:ICD,
author = "Qi Dang and Yong Liang and Dong Ouyang and Rui Miao
and Caijin Ling and Xiaoying Liu and Shengli Xie",
title = "Improved Computational Drug-Repositioning by
Self-Paced Non-Negative Matrix Tri-Factorization",
journal = j-TCBB,
volume = "20",
number = "3",
pages = "1953--1962",
month = may,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3225300",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3225300",
abstract = "Drug repositioning (DR) is a strategy to find new
targets for existing drugs, which plays an important
role in reducing the costs, time, and risk of
traditional drug development. Recently, the matrix
factorization approach has been widely used in the
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gao:2023:GES,
author = "Honghao Gao and Zijian Zhang and Ram{\'o}n J.
Dur{\'a}n Barroso",
title = "Guest Editorial Special Issue on Multi-Modal
Biomedical Computing-Deep Transfer Learning",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2363--2366",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3284603",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3284603",
abstract = "In Recent years, the development of biomedical imaging
techniques, integrative sensors, and artificial
intelligence has brought many benefits to the
protection of health. We can collect, measure, and
analyze vast volumes of health-related data using the
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2023:CML,
author = "Wei Wang and Xinhua Yu and Bo Fang and Yue Zhao and
Yongyong Chen and Wei Wei and Junxin Chen",
title = "Cross-Modality {LGE-CMR} Segmentation Using
Image-to-Image Translation Based Data Augmentation",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2367--2375",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3140306",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3140306",
abstract = "Accurate segmentation of ventricle and myocardium from
the late gadolinium enhancement (LGE) cardiac magnetic
resonance (CMR) is an important tool for myocardial
infarction (MI) analysis. However, the complex
enhancement pattern of LGE-CMR and the lack of
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ni:2023:MMS,
author = "Zhichen Ni and Honglong Chen and Zhe Li and Xiaomeng
Wang and Na Yan and Weifeng Liu and Feng Xia",
title = "{MSCET}: a Multi-Scenario Offloading Schedule for
Biomedical Data Processing and Analysis in
Cloud-Edge-Terminal Collaborative Vehicular Networks",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2376--2386",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3131177",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3131177",
abstract = "With the rapid development of Artificial Intelligence
(AI) and Internet of Things (IoTs), an increasing
number of computation intensive or delay sensitive
biomedical data processing and analysis tasks are
produced in vehicles, bringing more and more \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2023:MMF,
author = "Yuanpeng Zhang and Kaijian Xia and Yizhang Jiang and
Pengjiang Qian and Weiwei Cai and Chengyu Qiu and Khin
Wee Lai and Dongrui Wu",
title = "Multi-Modality Fusion \& Inductive Knowledge Transfer
Underlying Non-Sparse Multi-Kernel Learning and
Distribution Adaption",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2387--2397",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3142748",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3142748",
abstract = "With the development of sensors, more and more
multimodal data are accumulated, especially in
biomedical and bioinformatics fields. Therefore,
multimodal data analysis becomes very important and
urgent. In this study, we combine multi-kernel learning
and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2023:EDM,
author = "Nan Chen and Man Guo and Yongchao Li and Xiping Hu and
Zhijun Yao and Bin Hu",
title = "Estimation of Discriminative Multimodal Brain Network
Connectivity Using Message-Passing-Based Nonlinear
Network Fusion",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2398--2406",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3137498",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3137498",
abstract = "Effective estimation of brain network connectivity
enables better unraveling of the extraordinary
complexity interactions of brain regions and helps in
auxiliary diagnosis of psychiatric disorders.
Considering different modalities can provide \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2023:DTL,
author = "Jinxia Wang and Liang Qiao and Haibin Lv and Zhihan
Lv",
title = "Deep Transfer Learning-Based Multi-Modal Digital Twins
for Enhancement and Diagnostic Analysis of Brain {MRI}
Image",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2407--2419",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3168189",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3168189",
abstract = "Objective: it aims to adopt deep transfer learning
combined with Digital Twins (DTs) in Magnetic Resonance
Imaging (MRI) medical image enhancement. Methods: MRI
image enhancement method based on metamaterial
composite technology is proposed by analyzing
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Dharejo:2023:MBM,
author = "Fayaz Ali Dharejo and Muhammad Zawish and Farah Deeba
and Yuanchun Zhou and Kapal Dev and Sunder Ali Khowaja
and Nawab Muhammad Faseeh Qureshi",
title = "Multimodal-Boost: Multimodal Medical Image
Super-Resolution Using Multi-Attention Network With
Wavelet Transform",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2420--2433",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3191387",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3191387",
abstract = "Multimodal medical images are widely used by
clinicians and physicians to analyze and retrieve
complementary information from high-resolution images
in a non-invasive manner. Loss of corresponding image
resolution adversely affects the overall performance
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bian:2023:IEA,
author = "Yuexin Bian and Jintai Chen and Xiaojun Chen and
Xiaoxian Yang and Danny Z. Chen and Jian Wu",
title = "Identifying Electrocardiogram Abnormalities Using a
Handcrafted-Rule-Enhanced Neural Network",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2434--2444",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3140785",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3140785",
abstract = "A large number of people suffer from life-threatening
cardiac abnormalities, and electrocardiogram (ECG)
analysis is beneficial to determining whether an
individual is at risk of such abnormalities. Automatic
ECG classification methods, especially the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ahmed:2023:APN,
author = "Imran Ahmed and Abdellah Chehri and Gwanggil Jeon and
Francesco Piccialli",
title = "Automated Pulmonary Nodule Classification and
Detection Using Deep Learning Architectures",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2445--2456",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3192139",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3192139",
abstract = "Recent advancement in biomedical imaging technologies
has contributed to tremendous opportunities for the
health care sector and the biomedical community.
However, collecting, measuring, and analyzing large
volumes of health-related data like images is a
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2023:SSM,
author = "Qi-Qi Chen and Zhao-Hui Sun and Chuan-Feng Wei and
Edmond Q. Wu and Dong Ming",
title = "Semi-Supervised {$3$D} Medical Image Segmentation
Based on Dual-Task Consistent Joint Learning and
Task-Level Regularization",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2457--2467",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3144428",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3144428",
abstract = "Semi-supervised learning has attracted wide attention
from many researchers since its ability to utilize a
few data with labels and relatively more data without
labels to learn information. Some existing
semi-supervised methods for medical image \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Adhikari:2023:DTL,
author = "Mainak Adhikari and Abhishek Hazra and Sudarshan
Nandy",
title = "Deep Transfer Learning for Communicable Disease
Detection and Recommendation in Edge Networks",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2468--2479",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3180393",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3180393",
abstract = "Considering the increasing number of communicable
disease cases such as COVID-19 worldwide, the early
detection of the disease can prevent and limit the
outbreak. Besides that, the PCR test kits are not
available in most parts of the world, and there is
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xu:2023:HMD,
author = "Xiaolong Xu and Haoyan Xu and Liying Wang and Yuanyuan
Zhang and Fu Xaio",
title = "{Hygeia}: a Multilabel Deep Learning-Based
Classification Method for Imbalanced Electrocardiogram
Data",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2480--2493",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3176905",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3176905",
abstract = "Electrocardiogram (ECG) is a common diagnostic
indicator of heart disease in hospitals. Because of the
low price and noninvasiveness of ECG diagnosis, it is
widely used for prescreening and physical examination
of heart diseases. In several studies on ECG \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ma:2023:PDA,
author = "Yuxin Ma and Shuo Wang and Yang Hua and Ruhui Ma and
Tao Song and Zhengui Xue and Heng Cao and Haibing
Guan",
title = "Perceptual Data Augmentation for Biomedical Coronary
Vessel Segmentation",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2494--2505",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3188148",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3188148",
abstract = "Sufficient annotated data is critical to the success
of deep learning methods. Annotating for vessel
segmentation in X-ray coronary angiograms is extremely
difficult because of the small and complex structures
to be processed. Although unsupervised domain
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2023:HDT,
author = "Jianyuan Li and Xiong Luo and Huimin Ma and Wenbing
Zhao",
title = "A Hybrid Deep Transfer Learning Model With Kernel
Metric for {COVID-19} Pneumonia Classification Using
Chest {CT} Images",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2506--2517",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3216661",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3216661",
abstract = "Coronavirus disease-2019 (COVID-19) as a new pneumonia
which is extremely infectious, the classification of
this coronavirus is essential to effectively control
the development of the epidemic. Pathological changes
in the chest computed tomography (CT) \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Siniosoglou:2023:PPF,
author = "Ilias Siniosoglou and Vasileios Argyriou and
Panagiotis Sarigiannidis and Thomas Lagkas and Antonios
Sarigiannidis and Sotirios K. Goudos and Shaohua Wan",
title = "Post-Processing Fairness Evaluation of Federated
Models: an Unsupervised Approach in Healthcare",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2518--2529",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3269767",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3269767",
abstract = "Modern Healthcare cyberphysical systems have begun to
rely more and more on distributed AI leveraging the
power of Federated Learning (FL). Its ability to train
Machine Learning (ML) and Deep Learning (DL) models for
the wide variety of medical fields, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cai:2023:DDP,
author = "Wentian Cai and Linsen Xie and Weixian Yang and
Yijiang Li and Ying Gao and Tingting Wang",
title = "{DFTNet}: Dual-Path Feature Transfer Network for
Weakly Supervised Medical Image Segmentation",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2530--2540",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3198284",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3198284",
abstract = "Medical image segmentation has long suffered from the
problem of expensive labels. Acquiring pixel-level
annotations is time-consuming, labor-intensive, and
relies on extensive expert knowledge. Bounding box
annotations, in contrast, are relatively easy
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2023:CAB,
author = "Qingbin Wang and Kaiyi Chen and Wanrong Dou and Yutao
Ma",
title = "Cross-Attention Based Multi-Resolution Feature Fusion
Model for Self-Supervised Cervical {OCT} Image
Classification",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2541--2554",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3246979",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3246979",
abstract = "Cervical cancer seriously endangers the health of the
female reproductive system and even risks women's life
in severe cases. Optical coherence tomography (OCT) is
a non-invasive, real-time, high-resolution imaging
technology for cervical tissues. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yan:2023:DTL,
author = "Ke Yan and Xinlu Guo and Zhiwei Ji and Xiaokang Zhou",
title = "Deep Transfer Learning for Cross-Species Plant Disease
Diagnosis Adapting Mixed Subdomains",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2555--2564",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3135882",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3135882",
abstract = "A deep transfer learning framework adapting mixed
subdomains is proposed for cross-species plant disease
diagnosis. Most existing deep transfer learning studies
focus on knowledge transfer between highly correlated
domains. These methods may fail to deal \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ding:2023:RMI,
author = "Yi Ding and Xue Qin and Mingfeng Zhang and Ji Geng and
Dajiang Chen and Fuhu Deng and Chunhe Song",
title = "{RLSegNet}: an Medical Image Segmentation Network
Based on Reinforcement Learning",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2565--2576",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3195705",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3195705",
abstract = "In the area of medical image segmentation, the spatial
information can be further used to enhance the image
segmentation performance. And the 3D convolution is
mainly used to better utilize the spatial information.
However, how to better utilize the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liang:2023:TTE,
author = "Tingting Liang and Congying Xia and Ziqiang Zhao and
Yixuan Jiang and Yuyu Yin and Philip S. Yu",
title = "Transferring From Textual Entailment to Biomedical
Named Entity Recognition",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2577--2586",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3236477",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3236477",
abstract = "Biomedical Named Entity Recognition (BioNER) aims at
identifying biomedical entities such as genes,
proteins, diseases, and chemical compounds in the given
textual data. However, due to the issues of ethics,
privacy, and high specialization of biomedical
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Qayyum:2023:HDE,
author = "Abdul Qayyum and Imran Razzak and M. Tanveer and Moona
Mazher and Bandar Alhaqbani",
title = "High-Density Electroencephalography and Speech Signal
Based Deep Framework for Clinical Depression
Diagnosis",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2587--2597",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3257175",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3257175",
abstract = "Depression is a mental disorder characterized by
persistent depressed mood or loss of interest in
performing activities, causing significant impairment
in daily routine. Possible causes include
psychological, biological, and social sources of
distress. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ren:2023:MIS,
author = "Sheng Ren and Kehua Guo and Xiaokang Zhou and Bin Hu
and Feihong Zhu and Entao Luo",
title = "Medical Image Super-Resolution Based on Semantic
Perception Transfer Learning",
journal = j-TCBB,
volume = "20",
number = "4",
pages = "2598--2609",
month = jul,
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3212343",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:48 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3212343",
abstract = "Medical images are an important basis for doctors to
diagnose diseases, but some medical images have low
resolution due to hardware technology and cost
constraints. Super-resolution technology can
reconstruct low-resolution medical images into high-.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2023:CNN,
author = "Mei-Neng Wang and Xue-Jun Xie and Zhu-Hong You and
Leon Wong and Li-Ping Li and Zhan-Heng Chen",
title = "Combining {$K$} Nearest Neighbor With Nonnegative
Matrix Factorization for Predicting {CircRNA}-Disease
Associations",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2610--2618",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3180903",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3180903",
abstract = "Accumulating evidences show that circular RNAs
(circRNAs) play an important role in regulating gene
expression, and involve in many complex human diseases.
Identifying associations of circRNA with disease helps
to understand the pathogenesis, treatment \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Guan:2023:PDB,
author = "Shixuan Guan and Quan Zou and Hongjie Wu and Yijie
Ding",
title = "{Protein-DNA} Binding Residues Prediction Using a Deep
Learning Model With Hierarchical Feature Extraction",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2619--2628",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3190933",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3190933",
abstract = "Biologically important effects occur when proteins
bind to other substances, of which binding to DNA is a
crucial one. Therefore, accurate identification of
protein-DNA binding residues is important for further
understanding of the protein-DNA interaction \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2023:PMD,
author = "Zheng-Wei Li and Qian-Kun Wang and Chang-An Yuan and
Peng-Yong Han and Zhu-Hong You and Lei Wang",
title = "Predicting {MiRNA-Disease} Associations by Graph
Representation Learning Based on Jumping Knowledge
Networks",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2629--2638",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3196394",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3196394",
abstract = "Growing studies have shown that miRNAs are
inextricably linked with many human diseases, and a
great deal of effort has been spent on identifying
their potential associations. Compared with traditional
experimental methods, computational approaches have
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Huang:2023:TDU,
author = "Chengxi Huang and Wei Wang and Xin Zhang and Shui-Hua
Wang and Yu-Dong Zhang",
title = "Tuberculosis Diagnosis Using Deep Transferred
{EfficientNet}",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2639--2646",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3199572",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3199572",
abstract = "Tuberculosis is a very deadly disease, with more than
half of all tuberculosis cases dead in countries and
regions with relatively poor health care resources.
Fortunately, the disease is curable, and early
diagnosis and medication can go a long way toward
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ye:2023:DTI,
author = "Qing Ye and Xiaolong Zhang and Xiaoli Lin",
title = "Drug-Target Interaction Prediction via Graph
Auto-Encoder and Multi-Subspace Deep Neural Networks",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2647--2658",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3206907",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3206907",
abstract = "Computational prediction of drug-target interaction
(DTI) is important for the new drug discovery.
Currently, the deep neural network (DNN) has been
widely used in DTI prediction. However, parameters of
the DNN could be insufficiently trained and features
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2023:PPB,
author = "Shanshan Wang and Ruoyou Wu and Cheng Li and Juan Zou
and Ziyao Zhang and Qiegen Liu and Yan Xi and Hairong
Zheng",
title = "{PARCEL}: Physics-Based Unsupervised Contrastive
Representation Learning for Multi-Coil {MR} Imaging",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2659--2670",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3213669",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3213669",
abstract = "With the successful application of deep learning to
magnetic resonance (MR) imaging, parallel imaging
techniques based on neural networks have attracted wide
attention. However, in the absence of high-quality,
fully sampled datasets for training, the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lee:2023:CIC,
author = "Seokwoo Lee and Wook Lee and Shulei Ren and Byungkyu
Park and Kyungsook Han",
title = "Constructing Integrative {ceRNA} Networks and Finding
Prognostic Biomarkers in Renal Cell Carcinoma",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2671--2680",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3214190",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3214190",
abstract = "Inspired by a newly discovered gene regulation
mechanism known as competing endogenous RNA (ceRNA)
interactions, several computational methods have been
proposed to generate ceRNA networks. However, most of
these methods have focused on deriving \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lu:2023:MMA,
author = "Xinguo Lu and Guanyuan Chen and Jinxin Li and Xiangjin
Hu and Fengxu Sun",
title = "{MAGCN}: a Multiple Attention Graph Convolution
Networks for Predicting Synthetic Lethality",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2681--2689",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3221736",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3221736",
abstract = "Synthetic lethality (SL) is a potential cancer
therapeutic strategy and drug discovery. Computational
approaches to identify synthetic lethality genes have
become an effective complement to wet experiments which
are time consuming and costly. Graph \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2023:UFC,
author = "Qinhu Zhang and Youhong Xu and Siguo Wang and Yong Wu
and Yuannong Ye and Chang-An Yuan and Valeriya Gribova
and Vladimir Fedorovich Filaretov and De-Shuang Huang",
title = "Using Fully Convolutional Network to Locate
Transcription Factor Binding Sites Based on {DNA}
Sequence and Conservation Information",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2690--2699",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3219831",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3219831",
abstract = "Transcription factors (TFs) play a part in gene
expression. TFs can form complex gene expression
regulation system by combining with DNA. Thereby,
identifying the binding regions has become an
indispensable step for understanding the regulatory
mechanism \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Alatrany:2023:TLC,
author = "Abbas Saad Alatrany and Wasiq Khan and Abir J. Hussain
and Jamila Mustafina and Dhiya Al-Jumeily",
title = "Transfer Learning for Classification of {Alzheimer}'s
Disease Based on Genome Wide Data",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2700--2711",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3233869",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3233869",
abstract = "Alzheimer's disease (AD) is a type of brain disorder
that is regarded as a degenerative disease because the
corresponding symptoms aggravate with the time
progression. Single nucleotide polymorphisms (SNPs)
have been identified as relevant \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2023:RDR,
author = "Haochen Zhao and Guihua Duan and Peng Ni and Cheng Yan
and Yaohang Li and Jianxin Wang",
title = "{RNPredATC}: a Deep Residual Learning-Based Model With
Applications to the Prediction of {Drug-ATC} Code
Association",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2712--2723",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3088256",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3088256",
abstract = "The Anatomical Therapeutic Chemical (ATC)
classification system, designated by the World Health
Organization Collaborating Center (WHOCC), has been
widely used in drug screening, repositioning, and
similarity research. The ATC classification system
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xu:2023:IDI,
author = "Wuli Xu and Lei Duan and Huiru Zheng and Jesse Li-Ling
and Weipeng Jiang and Yidan Zhang and Tingting Wang and
Ruiqi Qin",
title = "An Integrative Disease Information Network Approach to
Similar Disease Detection",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2724--2735",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3110127",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3110127",
abstract = "Disease similarity analysis impacts significantly in
pathogenesis revealing, treatment recommending, and
disease-causing genes predicting. Previous works study
the disease similarity based on the semantics obtaining
from biomedical ontologies (e.g., \ldots{})",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sutanto:2023:AGL,
author = "Kevin Sutanto and Marcel Turcotte",
title = "Assessing Global-Local Secondary Structure
Fingerprints to Classify {RNA} Sequences With Deep
Learning",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2736--2747",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3118358",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3118358",
abstract = "RNA elements that are transcribed but not translated
into proteins are called non-coding RNAs (ncRNAs). They
play wide-ranging roles in biological processes and
disorders. Just like proteins, their structure is often
intimately linked to their function. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Peng:2023:MVF,
author = "Wei Peng and Ming Liu and Wei Dai and Tielin Chen and
Yu Fu and Yi Pan",
title = "Multi-View Feature Aggregation for Predicting
Microbe-Disease Association",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2748--2758",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3132611",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3132611",
abstract = "Microbes play a crucial role in human health and
disease. Figuring out the relationship between microbes
and diseases leads to significant potential
applications in disease treatments. It is an urgent
need to devise robust and effective computational
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{BinZaman:2023:ASO,
author = "Ahmed {Bin Zaman} and Toki Tahmid Inan and Kenneth {De
Jong} and Amarda Shehu",
title = "Adaptive Stochastic Optimization to Improve Protein
Conformation Sampling",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2759--2771",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3134103",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3134103",
abstract = "We have long known that characterizing protein
structures structure is key to understanding protein
function. Computational approaches have largely
addressed a narrow formulation of the problem, seeking
to compute one native structure from an amino-acid
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2023:PPG,
author = "Xiaoshuai Zhang and Lixin Wang and Hucheng Liu and
Xiaofeng Zhang and Bo Liu and Yadong Wang and Junyi
Li",
title = "{Prot2GO}: Predicting {GO} Annotations From Protein
Sequences and Interactions",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2772--2780",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2021.3139841",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2021.3139841",
abstract = "Protein is the main material basis of living organisms
and plays crucial role in life activities.
Understanding the function of protein is of great
significance for new drug discovery, disease treatment
and vaccine development. In recent years, with the
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2023:PDT,
author = "Jiatao Chen and Liang Zhang and Ke Cheng and Bo Jin
and Xinjiang Lu and Chao Che",
title = "Predicting Drug-Target Interaction Via Self-Supervised
Learning",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2781--2789",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3153963",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3153963",
abstract = "Recent advances in graph representation learning
provide new opportunities for computational drug-target
interaction (DTI) prediction. However, it still suffers
from deficiencies of dependence on manual labels and
vulnerability to attacks. Inspired by the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sun:2023:CMD,
author = "Jing Sun and Li Pan and Bin Li and Haoyue Wang and Bo
Yang and Wenbin Li",
title = "A Construction Method of Dynamic Protein Interaction
Networks by Using Relevant Features of Gene Expression
Data",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2790--2801",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3264241",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3264241",
abstract = "Essential proteins play an important role in various
life activities and are considered to be a vital part
of the organism. Gene expression data are an important
dataset to construct dynamic protein-protein
interaction networks (DPIN). The existing \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chu:2023:NBB,
author = "He-Ming Chu and Xiang-Zhen Kong and Jin-Xing Liu and
Chun-Hou Zheng and Han Zhang",
title = "A New Binary Biclustering Algorithm Based on Weight
Adjacency Difference Matrix for Analyzing Gene
Expression Data",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2802--2809",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3283801",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3283801",
abstract = "Biclustering algorithms are essential for processing
gene expression data. However, to process the dataset,
most biclustering algorithms require preprocessing the
data matrix into a binary matrix. Regrettably, this
type of preprocessing may introduce \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sheng:2023:SCM,
author = "Nan Sheng and Lan Huang and Ling Gao and Yangkun Cao
and Xuping Xie and Yan Wang",
title = "A Survey of Computational Methods and Databases for
{lncRNA-MiRNA} Interaction Prediction",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2810--2826",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3264254",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3264254",
abstract = "Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs)
are two prevalent non-coding RNAs in current research.
They play critical regulatory roles in the life
processes of animals and plants. Studies have shown
that lncRNAs can interact with miRNAs to \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lv:2023:AMC,
author = "Ji Lv and Guixia Liu and Yuan Ju and Houhou Huang and
Ying Sun",
title = "{AADB}: a Manually Collected Database for Combinations
of Antibiotics With Adjuvants",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2827--2836",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3283221",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3283221",
abstract = "Antimicrobial resistance is a global public health
concern. The lack of innovations in antibiotic
development has led to renewed interest in antibiotic
adjuvants. However, there is no database to collect
antibiotic adjuvants. Herein, we build a \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Madadi:2023:AIM,
author = "Yeganeh Madadi and Aboozar Monavarfeshani and Hao Chen
and W. Daniel Stamer and Robert W. Williams and Siamak
Yousefi",
title = "Artificial Intelligence Models for Cell Type and
Subtype Identification Based on Single-Cell {RNA}
Sequencing Data in Vision Science",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2837--2852",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3284795",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3284795",
abstract = "Single-cell RNA sequencing (scRNA-seq) provides a high
throughput, quantitative and unbiased framework for
scientists in many research fields to identify and
characterize cell types within heterogeneous cell
populations from various tissues. However, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gao:2023:CCN,
author = "Zhen Gao and Jin Tang and Junfeng Xia and Chun-Hou
Zheng and Pi-Jing Wei",
title = "{CNNGRN}: a Convolutional Neural Network-Based Method
for Gene Regulatory Network Inference From Bulk
Time-Series Expression Data",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2853--2861",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3282212",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3282212",
abstract = "Gene regulatory networks (GRNs) participate in many
biological processes, and reconstructing them plays an
important role in systems biology. Although many
advanced methods have been proposed for GRN
reconstruction, their predictive performance is far
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cao:2023:CAM,
author = "Yu Cao and Wenya Pi and Chun-Yu Lin and Ulrike
M{\"u}nzner and Masahiro Ohtomo and Tatsuya Akutsu",
title = "Common Attractors in Multiple {Boolean} Networks",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2862--2873",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3268795",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3268795",
abstract = "Analyzing multiple networks is important to understand
relevant features among different networks. Although
many studies have been conducted for that purpose, not
much attention has been paid to the analysis of
attractors (i.e., steady states) in multiple \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wei:2023:CMB,
author = "Ze-Gang Wei and Xu Chen and Xiao-Dan Zhang and Hao
Zhang and Xing-Guo Fan and Hong-Yan Gao and Fei Liu and
Yu Qian",
title = "Comparison of Methods for Biological Sequence
Clustering",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2874--2888",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3253138",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3253138",
abstract = "Recent advances in sequencing technology have
considerably promoted genomics research by providing
high-throughput sequencing economically. This great
advancement has resulted in a huge amount of sequencing
data. Clustering analysis is powerful to study
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Romashchenko:2023:CPM,
author = "Nikolai Romashchenko and Benjamin Linard and Eric
Rivals and Fabio Pardi",
title = "Computing Phylo-$k$-Mers",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2889--2897",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3278049",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3278049",
abstract = "Finding the correct position of new sequences within
an established phylogenetic tree is an increasingly
relevant problem in evolutionary bioinformatics and
metagenomics. Recently, alignment-free approaches for
this task have been proposed. One such \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2023:CPC,
author = "Niannian Liu and Zequn Zhang and Yanan Wu and Yinglong
Wang and Ying Liang",
title = "{CRBSP:Prediction} of {CircRNA--RBP} Binding Sites
Based on Multimodal Intermediate Fusion",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2898--2906",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3272400",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3272400",
abstract = "Circular RNA (CircRNA) is widely expressed and has
physiological and pathological significance, regulating
post-transcriptional processes via its protein-binding
activity. However, whereas much work has been done on
linear RNA and RNA binding protein (RBP). \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2023:DPP,
author = "Fan Zhang and Yawei Zhang and Xiaoke Zhu and Xiaopan
Chen and Fuhao Lu and Xinhong Zhang",
title = "{DeepSG2PPI}: a Protein-Protein Interaction Prediction
Method Based on Deep Learning",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2907--2919",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3268661",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3268661",
abstract = "Protein-protein interaction (PPI) plays an important
role in almost all life activities. Many protein
interaction sites have been confirmed by biological
experiments, but these PPI site identification methods
are time-consuming and expensive. In this \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ravikumar:2023:EIS,
author = "Visweswaran Ravikumar and Tong Xu and Wajd N. Al-Holou
and Salar Fattahi and Arvind Rao",
title = "Efficient Inference of Spatially-Varying {Gaussian}
{Markov} Random Fields With Applications in Gene
Regulatory Networks",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2920--2932",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3282028",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3282028",
abstract = "In this paper, we study the problem of inferring
spatially-varying Gaussian Markov random fields
(SV-GMRF) where the goal is to learn a network of
sparse, context-specific GMRFs representing network
relationships between genes. An important application
of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2023:EBR,
author = "Bo Zhao and Jun Bai and Chen Li and Jianfei Zhang and
Wenge Rong and Yuanxin Ouyang and Zhang Xiong",
title = "Enhancing Biomedical {ReQA} With Adversarial Hard
In-Batch Negative Samples",
journal = j-TCBB,
volume = "20",
number = "5",
pages = "2933--2944",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3261315",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:49 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3261315",
abstract = "Question answering (QA) plays a vital role in
biomedical natural language processing. Among question
answering tasks, the retrieval question answering
(ReQA) aims to directly retrieve the correct answer
from candidates and has attracted much attention in
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cai:2023:GEI,
author = "Zhipeng Cai and Min Li and Pavel Skums and Yanjie
Wei",
title = "{Guest Editors}' Introduction to the Special Section
on Bioinformatics Research and Applications",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3329--3331",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3325032",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3325032",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lee:2023:EVI,
author = "Hunmin Lee and Mingon Kang and Donghyun Kim and Daehee
Seo and Yingshu Li",
title = "Epidemic Vulnerability Index for Effective Vaccine
Distribution Against Pandemic",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3332--3342",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3198365",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3198365",
abstract = "COVID-19 vaccine distribution route directly impacts
the community's mortality and infection rate.
Therefore, optimal vaccination dissemination would
appreciably lower the death and infection rates. This
paper proposes the Epidemic Vulnerability \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2023:KGF,
author = "Qing Zhao and Jianqiang Li and Linna Zhao and Zhichao
Zhu",
title = "Knowledge Guided Feature Aggregation for the
Prediction of Chronic Obstructive Pulmonary Disease
With {Chinese} {EMRs}",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3343--3352",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3198798",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3198798",
abstract = "The automatic disease diagnosis utilizing clinical
data has been suffering from the issues of feature
sparse and high probability of missing values. Since
the graph neural network is a effective tool to model
the structural information and infer the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2023:MDA,
author = "Yueyue Wang and Xiujuan Lei and Yi Pan",
title = "Microbe-Disease Association Prediction Using {RGCN}
Through Microbe-Drug-Disease Network",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3353--3362",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3247035",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3247035",
abstract = "Accumulating evidence has shown that microbes play
significant roles in human health and diseases.
Therefore, identifying microbe-disease associations is
conducive to disease prevention. In this article, a
predictive method called TNRGCN is designed for
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Peng:2023:PMD,
author = "Wei Peng and Zicheng Che and Wei Dai and Shoulin Wei
and Wei Lan",
title = "Predicting {miRNA}-Disease Associations From
{miRNA}-Gene-Disease Heterogeneous Network With
Multi-Relational Graph Convolutional Network Model",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3363--3375",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3187739",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3187739",
abstract = "MiRNAs are reported to be linked to the pathogenesis
of human complex diseases. Disease-related miRNAs may
serve as novel bio-marks and drug targets. This work
focuses on designing a multi-relational Graph
Convolutional Network model to predict miRNA-.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ali:2023:EAK,
author = "Sarwan Ali and Bikram Sahoo and Muhammad Asad Khan and
Alexander Zelikovsky and Imdad Ullah Khan and Murray
Patterson",
title = "Efficient Approximate Kernel Based Spike Sequence
Classification",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3376--3388",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3206284",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3206284",
abstract = "Machine learning (ML) models, such as SVM, for tasks
like classification and clustering of sequences,
require a definition of distance/similarity between
pairs of sequences. Several methods have been proposed
to compute the similarity between sequences, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Guan:2023:RRN,
author = "Yuxia Guan and Ying An and Jingrui Xu and Ning Liu and
Jianxin Wang",
title = "{HA-ResNet}: Residual Neural Network With Hidden
Attention for {ECG} Arrhythmia Detection Using
Two-Dimensional Signal",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3389--3398",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3198998",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3198998",
abstract = "Arrhythmia is an abnormal heart rhythm, a common
clinical problem in cardiology. Long-term or severe
arrhythmia may lead to stroke and sudden cardiac death.
The electrocardiogram (ECG) is the most commonly used
tool to diagnose arrhythmia. However, the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2023:DCU,
author = "Qiang Li and Hong Song and Zenghui Wei and Fengbo Yang
and Jingfan Fan and Danni Ai and Yucong Lin and
Xiaoling Yu and Jian Yang",
title = "Densely Connected {U-Net} With Criss-Cross Attention
for Automatic Liver Tumor Segmentation in {CT} Images",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3399--3410",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3198425",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3198425",
abstract = "Automatic liver tumor segmentation plays a key role in
radiation therapy of hepatocellular carcinoma. In this
paper, we propose a novel densely connected U-Net model
with criss-cross attention (CC-DenseUNet) to segment
liver tumors in computed tomography ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lv:2023:TTB,
author = "Zhilong Lv and Yuexiao Lin and Rui Yan and Ying Wang
and Fa Zhang",
title = "{TransSurv}: Transformer-Based Survival Analysis Model
Integrating Histopathological Images and Genomic Data
for Colorectal Cancer",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3411--3420",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3199244",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3199244",
abstract = "Survival analysis is a significant study in cancer
prognosis, and the multi-modal data, including
histopathological images, genomic data, and clinical
information, provides unprecedented opportunities for
its development. However, because of the high
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2023:CAS,
author = "Cheng Chen and Yuguo Zha and Daming Zhu and Kang Ning
and Xuefeng Cui",
title = "{ContactLib-ATT}: a Structure-Based Search Engine for
Homologous Proteins",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3421--3429",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3197802",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3197802",
abstract = "General-purpose protein structure embedding can be
used for many important protein biology tasks, such as
protein design, drug design and binding affinity
prediction. Recent researches have shown that
attention-based encoder layers are more suitable to
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2023:AAM,
author = "Jiamin Chen and Jianliang Gao and Tengfei Lyu and
Babatounde Moctard Oloulade and Xiaohua Hu",
title = "{AutoMSR}: Auto Molecular Structure Representation
Learning for Multi-label Metabolic Pathway Prediction",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3430--3439",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3198119",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3198119",
abstract = "It is significant to comprehend the relationship
between metabolic pathway and molecular pathway for
synthesizing new molecules, for instance optimizing
drug metabolization. In bioinformatics fields,
multi-label prediction of metabolic pathways is a
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2023:GSI,
author = "Hong-Dong Li and Chao Deng and Xiao-Qi Zhang and
Cui-Xiang Lin",
title = "A Gene Set-Integrated Approach for Predicting
Disease-Associated Genes",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3440--3450",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3214517",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3214517",
abstract = "It is important to identify disease-associated genes
for studying the pathogenic mechanism of complex
diseases. Recently, models for disease gene prediction
are dominantly based on molecular expression data and
networks, including gene expression, protein \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Fu:2023:DRD,
author = "Haitao Fu and Cecheng Zhao and Xiaohui Niu and Wen
Zhang",
title = "{DRLM}: a Robust Drug Representation Learning Method
and its Applications",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3451--3460",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3213979",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3213979",
abstract = "Learning representations from data is a fundamental
step for machine learning. High-quality and robust drug
representations can broaden the understanding of
pharmacology, and improve the modeling of multiple
drug-related prediction tasks, which further \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Qian:2023:PCM,
author = "Yurong Qian and Jingjing Zheng and Ying Jiang and
Shaoqiu Li and Lei Deng",
title = "Prediction of {circRNA-MiRNA} Association Using
Singular Value Decomposition and Graph Neural
Networks",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3461--3468",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3222777",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3222777",
abstract = "A large number of experimental studies have shown that
circRNAs can act as molecular sponges of microRNAs,
interacting with miRNAs to regulate gene expression
levels, thereby affecting the development of human
diseases. Exploring the potential \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cheng:2023:DNC,
author = "Enhao Cheng and Jun Zhao and Hong Wang and Shuguang
Song and Shuxian Xiong and Yanshen Sun",
title = "Dual Network Contrastive Learning for Predicting
Microbe-Disease Associations",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3469--3481",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3228617",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3228617",
abstract = "Predicting microbe-disease associations is crucial for
demystifying the causes of diseases and preventing them
proactively. However, most of existing approaches are
feeble to comprehensively investigate the interactive
relationships between diseases and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gong:2023:SNS,
author = "Tiansu Gong and Fusong Ju and Shiwei Sun and Dongbo
Bu",
title = "{SASA-Net}: a Spatial-Aware Self-Attention Mechanism
for Building Protein {$3$D} Structure Directly From
Inter- Residue Distances",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3482--3488",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3240456",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3240456",
abstract = "Protein functions are tightly related to the fine
details of their 3D structures. To understand protein
structures, computational prediction approaches are
highly needed. Recently, protein structure prediction
has achieved considerable progresses mainly \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2023:MPM,
author = "Huan Wang and Ruigang Liu and Baijing Wang and Yifan
Hong and Ziwen Cui and Qiufen Ni",
title = "Multitype Perception Method for Drug-Target
Interaction Prediction",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3489--3498",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3285042",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3285042",
abstract = "With the growing popularity of artificial intelligence
in drug discovery, many deep-learning technologies have
been used to automatically predict unknown drug-target
interactions (DTIs). A unique challenge in using these
technologies to predict DTI is \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cao:2023:IMB,
author = "Qingqing Cao and Jianping Zhao and Haiyun Wang and Qi
Guan and Chunhou Zheng",
title = "An Integrated Method Based on {Wasserstein} Distance
and Graph for Cancer Subtype Discovery",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3499--3510",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3293472",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3293472",
abstract = "Due to the complexity of cancer pathogenesis at
different omics levels, it is necessary to find a
comprehensive method to accurately distinguish and find
cancer subtypes for cancer treatment. In this paper, we
proposed a new cancer multi-omics subtype \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Mondal:2023:GDG,
author = "Abhijit Mondal and Mukul S. Bansal",
title = "Generalizing the Domain-Gene-Species Reconciliation
Framework to Microbial Genes and Domains",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3511--3522",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3294480",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3294480",
abstract = "Protein domains play an important role in the function
and evolution of many gene families. Previous studies
have shown that domains are frequently lost or gained
during gene family evolution. Yet, most computational
approaches for studying gene family \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2023:NBD,
author = "Han Zhang and Zexuan Zhu and Hui Li and Shan He",
title = "Network Biomarker Detection From Gene Co-Expression
Network Using {Gaussian} Mixture Model Clustering",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3523--3534",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3297388",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3297388",
abstract = "Finding network biomarkers from gene co-expression
networks (GCNs) has attracted a lot of research
interest. A network biomarker is a topological module,
i.e., a group of densely connected nodes in a GCN, in
which the gene expression values correlate with
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wu:2023:MVC,
author = "Wenming Wu and Wensheng Zhang and Weimin Hou and
Xiaoke Ma",
title = "Multi-View Clustering With Graph Learning for
{scRNA-Seq} Data",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3535--3546",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3298334",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3298334",
abstract = "Advances in single-cell biotechnologies have generated
the single-cell RNA sequencing (scRNA-seq) of gene
expression profiles at cell levels, providing an
opportunity to study cellular distribution. Although
significant efforts developed in their analysis,.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2023:HPL,
author = "Dayun Liu and Xianghui Li and Liangliang Zhang and
Xiaowen Hu and Jiaxuan Zhang and Zhirong Liu and Lei
Deng",
title = "{HGNNLDA}: Predicting {lncRNA-Drug} Sensitivity
Associations via a Dual Channel Hypergraph Neural
Network",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3547--3555",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3302468",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3302468",
abstract = "Drug sensitivity is critical for enabling personalized
treatment. Many studies have shown that long non-coding
RNAs (lncRNAs) are closely related to drug sensitivity
because lncRNAs can regulate genes related to drug
sensitivity to affect drug efficacy. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2023:GCG,
author = "Jing Yang and Zhengshu Lu and Xu Chen and Deling Xu
and Dewu Ding and Yanrui Ding",
title = "{GCNA-Cluster}: a Gene Co-Expression Network Alignment
to Cluster Cancer Patients Algorithm for Identifying
Subtypes of Pancreatic Ductal Adenocarcinoma",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3556--3566",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3300102",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3300102",
abstract = "Cancer heterogeneity makes it necessary to use
different treatment strategies for patients with the
same pathological features. Accurate identification of
cancer subtypes is a crucial step in this approach. The
current studies of pancreatic ductal \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jia:2023:PMP,
author = "Baoli Jia and Qingfang Meng and Yuehui Chen and Hongri
Yang",
title = "Prediction of Membrane Protein Amphiphilic Helix Based
on Horizontal Visibility Graph and Graph Convolution
Network",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3567--3574",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3305493",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3305493",
abstract = "Membrane protein amphiphilic helices play an important
role in many biological processes. Based on the graph
convolution network and the horizontal visibility graph
the prediction method of membrane protein amphiphilic
helix structure is proposed in this \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Saheed:2023:MGE,
author = "Yakub K. Saheed and Bukola F. Balogun and Braimah
Joseph Odunayo and Mustapha Abdulsalam",
title = "Microarray Gene Expression Data Classification via
{Wilcoxon} Sign Rank Sum and Novel Grey Wolf Optimized
Ensemble Learning Models",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3575--3587",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3305429",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3305429",
abstract = "Cancer is a deadly disease that affects the lives of
people all over the world. Finding a few genes relevant
to a single cancer disease can lead to effective
treatments. The difficulty with microarray datasets is
their high dimensionality; they have a \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Gong:2023:CLE,
author = "Yinyin Gong and Rui Li and Bin Fu and Yan Liu and
Jilong Wang and Renfa Li and Danny Z. Chen",
title = "A {CNN-LSTM} Ensemble Model for Predicting
Protein-Protein Interaction Binding Sites",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3588--3599",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3306948",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3306948",
abstract = "Proteins commonly perform biological functions through
protein-protein interactions (PPIs). The knowledge of
PPI sites is imperative for the understanding of
protein functions, disease mechanisms, and drug design.
Traditional biological experimental \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2023:MPI,
author = "Xin Wang and Jie Li and Guohua Wang",
title = "{MicroRNA} Promoter Identification in Human With a
Three-level Prediction Method",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3600--3608",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3305992",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3305992",
abstract = "The accurate annotation of miRNA promoters is critical
for the mechanistic understanding of miRNA gene
regulation. Various computational methods have been
developed for the prediction of miRNA promoters solely
employing a single classifier. Most of these \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cao:2023:MMP,
author = "Yahui Cao and Tao Zhang and Xin Zhao and Xue Jia and
Bingzhi Li",
title = "{MooSeeker}: a Metabolic Pathway Design Tool Based on
Multi-Objective Optimization Algorithm",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3609--3622",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3307363",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3307363",
abstract = "Recently, metabolic pathway design has attracted
considerable attention and become an increasingly
important area in metabolic engineering. Manual or
computational methods have been introduced to retrieve
the metabolic pathway. These methods model \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2023:TIT,
author = "Taoning Chen and Tingfang Wu and Deng Pan and Jinxing
Xie and Jin Zhi and Xuejiao Wang and Lijun Quan and
Qiang Lyu",
title = "{TransRNAm}: Identifying Twelve Types of {RNA}
Modifications by an Interpretable Multi-Label Deep
Learning Model Based on Transformer",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3623--3634",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3307419",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3307419",
abstract = "Accurate identification of RNA modification sites is
of great significance in understanding the functions
and regulatory mechanisms of RNAs. Recent advances have
shown great promise in applying computational methods
based on deep learning for accurate \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2023:EFP,
author = "Weizhong Zhao and Wenjie Yao and Xingpeng Jiang and
Tingting He and Chuan Shi and Xiaohua Hu",
title = "An Explainable Framework for Predicting Drug-Side
Effect Associations via Meta-Path-Based Feature
Learning in Heterogeneous Information Network",
journal = j-TCBB,
volume = "20",
number = "6",
pages = "3635--3647",
year = "2023",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3308094",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Tue Mar 19 08:33:52 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3308094",
abstract = "Side effects of drugs have gained increasing attention
in the biomedical field, and accurate identification of
drug side effects is essential for drug development and
drug safety surveillance. Although the traditional
pharmacological experiments can \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{ElZein:2024:PCP,
author = "Yamane {El Zein} and Mathieu Lemay and K{\'e}vin
Huguenin",
title = "{PrivaTree}: Collaborative Privacy-Preserving Training
of Decision Trees on Biomedical Data",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "1--13",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3286274",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3286274",
abstract = "Biomedical data generation and collection have become
faster and more ubiquitous. Consequently, datasets are
increasingly spread across hospitals, research
institutions, or other entities. Exploiting such
distributed datasets simultaneously can be \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Tu:2024:DMD,
author = "Chao Tu and Denghui Du and Tieyong Zeng and Yu Zhang",
title = "Deep Multi-Dictionary Learning for Survival Prediction
With Multi-Zoom Histopathological Whole Slide Images",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "14--25",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3321593",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3321593",
abstract = "Survival prediction based on histopathological whole
slide images (WSIs) is of great significance for
risk-benefit assessment and clinical decision. However,
complex microenvironments and heterogeneous tissue
structures in WSIs bring challenges to \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Morris:2024:EDA,
author = "Jordan Morris and Ashur Rafiev and Graeme M. Bragg and
Mark L. Vousden and David B. Thomas and Alex Yakovlev
and Andrew D. Brown",
title = "An Event-Driven Approach to Genotype Imputation on a
Custom {RISC-V} Cluster",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "26--35",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3328714",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/risc-v.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3328714",
abstract = "This article proposes an event-driven solution to
genotype imputation, a technique used to statistically
infer missing genetic markers in DNA. The work
implements the widely accepted Li and Stephens model,
primary contributor to the computational \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Choudhuri:2024:CPP,
author = "Souradipto Choudhuri and Keya Sau",
title = "{CodonU}: a {Python} Package for Codon Usage
Analysis",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "36--44",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3335823",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/python.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3335823",
abstract = "Codon Usage Analysis (CUA) has been accompanied by
several web servers and independent programs written in
several programming languages. Also this diversity
speaks for the need of a reusable software that can be
helpful in reading, manipulating and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yu:2024:MRG,
author = "Shengpeng Yu and Hong Wang and Jing Li and Jun Zhao
and Cheng Liang and Yanshen Sun",
title = "A Multi-Relational Graph Encoder Network for
Fine-Grained Prediction of {MiRNA}-Disease
Associations",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "45--56",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3335007",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3335007",
abstract = "MicroRNAs (miRNAs) are critical in diagnosing and
treating various diseases. Automatically demystifying
the interdependent relationships between miRNAs and
diseases has recently made remarkable progress, but
their fine-grained interactive relationships \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Guo:2024:GBF,
author = "Rui Guo and Xu Tian and Hanhe Lin and Stephen McKenna
and Hong-Dong Li and Fei Guo and Jin Liu",
title = "Graph-Based Fusion of Imaging, Genetic and Clinical
Data for Degenerative Disease Diagnosis",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "57--68",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3335369",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3335369",
abstract = "Graph learning methods have achieved noteworthy
performance in disease diagnosis due to their ability
to represent unstructured information such as
inter-subject relationships. While it has been shown
that imaging, genetic and clinical data are crucial
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Noland:2024:ESP,
author = "Jonas Kristiansen N{\o}land and Steinar Thorvaldsen",
title = "The Exact Stochastic Process of the Haploid
Multi-Allelic {Wright--Fisher} Mutation Model",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "69--83",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3336850",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3336850",
abstract = "Diffusion models are widely applied in population
genetics, but their approximate solutions may not
accurately capture the exact stochastic process.
Nevertheless, this practice was necessary due to
computing limitations, particularly for large
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Tang:2024:MNM,
author = "Wenliang Tang and Zhaohong Deng and Hanwen Zhou and
Wei Zhang and Fuping Hu and Kup-Sze Choi and Shitong
Wang",
title = "{MVDINET}: a Novel Multi-Level Enzyme Function
Predictor With Multi-View Deep Interactive Learning",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "84--94",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3337158",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3337158",
abstract = "As a class of extremely significant of biocatalysts,
enzymes play an important role in the process of
biological reproduction and metabolism. Therefore, the
prediction of enzyme function is of great significance
in biomedicine fields. Recently, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Dong:2024:SCA,
author = "Shujie Dong and Yuansheng Liu and Yongshun Gong and
Xiangjun Dong and Xiangxiang Zeng",
title = "{scCAN}: Clustering With Adaptive Neighbor-Based
Imputation Method for Single-Cell {RNA-Seq} Data",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "95--105",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3337231",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3337231",
abstract = "Single-cell RNA sequencing (scRNA-seq) is widely used
to study cellular heterogeneity in different samples.
However, due to technical deficiencies, dropout events
often result in zero gene expression values in the gene
expression matrix. In this paper, we \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Rossi:2024:IMC,
author = "Nicol{\`o} Rossi and Nicola Gigante and Nicola
Vitacolonna and Carla Piazza",
title = "Inferring {Markov} Chains to Describe Convergent Tumor
Evolution With {CIMICE}",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "106--119",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3337258",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3337258",
abstract = "The field of tumor phylogenetics focuses on studying
the differences within cancer cell populations. Many
efforts are done within the scientific community to
build cancer progression models trying to understand
the heterogeneity of such diseases. These \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2024:PDD,
author = "Dandan Li and Zhen Xiao and Han Sun and Xingpeng Jiang
and Weizhong Zhao and Xianjun Shen",
title = "Prediction of Drug--Disease Associations Based on
Multi-Kernel Deep Learning Method in Heterogeneous
Graph Embedding",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "120--128",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3339189",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3339189",
abstract = "Computational drug repositioning can identify
potential associations between drugs and diseases. This
technology has been shown to be effective in
accelerating drug development and reducing experimental
costs. Although there has been plenty of research
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yu:2024:MNM,
author = "Changyong Yu and Yuhai Zhao and Chu Zhao and Jianyu
Jin and Keming Mao and Guoren Wang",
title = "{MiniDBG}: a Novel and Minimal {de Bruijn} Graph for
Read Mapping",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "129--142",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3340251",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3340251",
abstract = "The De Bruijn graph (DBG) has been widely used in the
algorithms for indexing or organizing read and
reference sequences in bioinformatics. However, a DBG
model that can locate each node, edge and path on
sequence has not been proposed so far. Recently,
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2024:SMS,
author = "Wei Wang and Mengxue Yu and Bin Sun and Juntao Li and
Dong Liu and Hongjun Zhang and Xianfang Wang and Yun
Zhou",
title = "{SMGCN}: Multiple Similarity and Multiple Kernel
Fusion Based Graph Convolutional Neural Network for
Drug-Target Interactions Prediction",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "143--154",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3339645",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3339645",
abstract = "Accurately identifying potential drug-target
interactions (DTIs) is a critical step in accelerating
drug discovery. Despite many studies that have been
conducted over the past decades, detecting DTIs remains
a highly challenging and complicated process.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sinha:2024:ESA,
author = "Rituparna Sinha and Rajat Kumar Pal and Rajat K. De",
title = "{ENLIGHTENMENT}: a Scalable Annotated Database of
Genomics and {NGS}-Based Nucleotide Level Profiles",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "155--168",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3340067",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3340067",
abstract = "The revolution in sequencing technologies has enabled
human genomes to be sequenced at a very low cost and
time leading to exponential growth in the availability
of whole-genome sequences. However, the complete
understanding of our genome and its \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Barnett:2024:GML,
author = "Eric J. Barnett and Daniel G. Onete and Asif Salekin
and Stephen V. Faraone",
title = "Genomic Machine Learning Meta-regression: Insights on
Associations of Study Features With Reported Model
Performance",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "169--177",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3343808",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3343808",
abstract = "Many studies have been conducted with the goal of
correctly predicting diagnostic status of a disorder
using the combination of genomic data and machine
learning. It is often hard to judge which components of
a study led to better results and whether \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2024:NMS,
author = "Yuerui Liu and Yongquan Jiang and Fan Zhang and Yan
Yang",
title = "A Novel Multi-Scale Graph Neural Network for Metabolic
Pathway Prediction",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "178--187",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3345647",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3345647",
abstract = "Predicting the metabolic pathway classes of compounds
in the human body is an important problem in drug
research and development. For this purpose, we propose
a Multi-Scale Graph Neural Network framework, named
MSGNN. The framework includes a subgraph \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xin:2024:BLH,
author = "Junchang Xin and Mingcan Wang and Luxuan Qu and Qi
Chen and Weiyiqi Wang and Zhiqiong Wang",
title = "{BIC-LP}: a Hybrid Higher-Order Dynamic {Bayesian}
Network Score Function for Gene Regulatory Network
Reconstruction",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "188--199",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3345317",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3345317",
abstract = "Reconstructing gene regulatory networks(GRNs) is an
increasingly hot topic in bioinformatics. Dynamic
Bayesian network(DBN) is a stochastic graph model
commonly used as a vital model for GRN reconstruction.
But probabilistic characteristics of biological
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zeng:2024:SIT,
author = "Pengcheng Zeng and Zhixiang Lin",
title = "{scICML}: Information-Theoretic Co-Clustering-Based
Multi-View Learning for the Integrative Analysis of
Single-Cell Multi-Omics Data",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "200--207",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3305989",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3305989",
abstract = "Modern high-throughput sequencing technologies have
enabled us to profile multiple molecular modalities
from the same single cell, providing unprecedented
opportunities to assay cellular heterogeneity from
multiple biological layers. However, the datasets
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Nagda:2024:PHP,
author = "Bindi M. Nagda and Van Minh Nguyen and Ryan T. White",
title = "{promSEMBLE}: Hard Pattern Mining and Ensemble
Learning for Detecting {DNA} Promoter Sequences",
journal = j-TCBB,
volume = "21",
number = "1",
pages = "208--214",
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3339597",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Fri May 31 09:09:21 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3339597",
abstract = "Accurate identification of DNA promoter sequences is
of crucial importance in unraveling the underlying
mechanisms that regulate gene transcription. Initiation
of transcription is controlled through regulatory
transcription factors binding to promoter \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cruz:2024:BOO,
author = "Fernando Cruz and Jo{\~a}o Capela and Eug{\'e}nio C.
Ferreira and Miguel Rocha and Oscar Dias",
title = "{BioISO}: an Objective-Oriented Application for
Assisting the Curation of Genome-Scale Metabolic
Models",
journal = j-TCBB,
volume = "21",
number = "2",
pages = "215--226",
month = mar,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3339972",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:16:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3339972",
abstract = "As the reconstruction of Genome-Scale Metabolic Models
(GEMs) becomes standard practice in systems biology,
the number of organisms having at least one metabolic
model is peaking at an unprecedented scale. The
automation of laborious tasks, such as gap-. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2024:GIK,
author = "Hao Zhang and Jiao Jiao and Tianheng Zhao and Enshuang
Zhao and Lanhui Li and Guihua Li and Borui Zhang and
Qing-Ming Qin",
title = "{GERWR}: Identifying the Key Pathogenicity-Associated
{sRNAs} of \bioname{Magnaporthe oryzae} Infection in
Rice Based on Graph Embedding and Random Walk With
Restart",
journal = j-TCBB,
volume = "21",
number = "2",
pages = "227--239",
month = mar,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3348080",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:16:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3348080",
abstract = "Rice blast, caused by \bioname{Magnaporthe oryzae}
(\bioname{M.oryzae}), is a destructive rice disease
that reduces rice yield by 10\% to 30\% annually. It
also affects other cereal crops such as barley, wheat,
rye, millet, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Manu:2024:GFG,
author = "Daniel Manu and Jingjing Yao and Wuji Liu and Xiang
Sun",
title = "{GraphGANFed}: a Federated Generative Framework for
Graph-Structured Molecules Towards Efficient Drug
Discovery",
journal = j-TCBB,
volume = "21",
number = "2",
pages = "240--253",
month = mar,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3349990",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:16:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3349990",
abstract = "Recent advances in deep learning have accelerated its
use in various applications, such as cellular image
analysis and molecular discovery. In molecular
discovery, a generative adversarial network (GAN),
which comprises a discriminator to distinguish
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Hu:2024:NNO,
author = "Zhao-Qi Hu and Yuan-Mao Hung and Li-Han Chen and
Liang-Chuan Lai and Min-Hsiung Pan and Eric Y. Chuang
and Mong-Hsun Tsai",
title = "{NURECON}: a Novel Online System for Determining
Nutrition Requirements Based on Microbial Composition",
journal = j-TCBB,
volume = "21",
number = "2",
pages = "254--264",
month = mar,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3349572",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:16:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3349572",
abstract = "Dietary habits have been proven to have an impact on
the microbial composition and health of the human gut.
Over the past decade, researchers have discovered that
gut microbiota can use nutrients to produce metabolites
that have major implications for \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jin:2024:SSS,
author = "Sichen Jin and Yijia Zhang and Huimin Yu and Mingyu
Lu",
title = "{SADR}: Self-Supervised Graph Learning With Adaptive
Denoising for Drug Repositioning",
journal = j-TCBB,
volume = "21",
number = "2",
pages = "265--277",
month = mar,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3351079",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:16:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3351079",
abstract = "Traditional drug development is often high-risk and
time-consuming. A promising alternative is to reuse or
relocate approved drugs. Recently, some methods based
on graph representation learning have started to be
used for drug repositioning. These models \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Leuchtenberger:2024:LAN,
author = "Alina F. Leuchtenberger and Arndt von Haeseler",
title = "Learning From an Artificial Neural Network in
Phylogenetics",
journal = j-TCBB,
volume = "21",
number = "2",
pages = "278--288",
month = mar,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3352268",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:16:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3352268",
abstract = "We show that an iterative ansatz of deep learning and
human intelligence guided simplification may lead to
surprisingly simple solutions for a difficult problem
in phylogenetics. Distinguishing Farris and Felsenstein
trees is a longstanding problem in \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2024:LGN,
author = "Wenjing Wang and Pengyong Han and Zhengwei Li and Ru
Nie and Kangwei Wang and Lei Wang and Hongmei Liao",
title = "{LMGATCDA}: Graph Neural Network With Labeling Trick
for Predicting {circRNA}-Disease Associations",
journal = j-TCBB,
volume = "21",
number = "2",
pages = "289--300",
month = mar,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3355093",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:16:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3355093",
abstract = "Previous studies have proven that circular RNAs
(circRNAs) are inextricably connected to the etiology
and pathophysiology of complicated diseases. Since
conventional biological research are frequently
small-scale, expensive, and time-consuming, it is
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2024:FBI,
author = "Tiantian Li and Haitao Jiang and Binhai Zhu and
Lusheng Wang and Daming Zhu",
title = "Flanked Block-Interchange Distance on Strings",
journal = j-TCBB,
volume = "21",
number = "2",
pages = "301--311",
month = mar,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3351440",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:16:12 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3351440",
abstract = "Rearrangement sorting problems impact profoundly in
measuring genome similarities and tracing historic
scenarios of species. However, recent studies on genome
rearrangement mechanisms disclosed a statistically
significant evidence, repeats are situated at
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zanetti:2024:CSS,
author = "Jo{\~a}o Paulo Pereira Zanetti and Lucas Peres
Oliveira and Jo{\~a}o Meidanis and Leonid
Chindelevitch",
title = "Counting Sorting Scenarios and Intermediate Genomes
for the Rank Distance",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "316--327",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3277733",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3277733",
abstract = "An important problem in genome comparison is the
genome sorting problem, that is, the problem of finding
a sequence of basic operations that transforms one
genome into another whose length (possibly weighted)
equals the distance between them. These \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sheng:2024:SDL,
author = "Nan Sheng and Xuping Xie and Yan Wang and Lan Huang
and Shuangquan Zhang and Ling Gao and Hao Wang",
title = "A Survey of Deep Learning for Detecting
{miRNA}-Disease Associations: Databases, Computational
Methods, Challenges, and Future Directions",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "328--347",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3351752",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3351752",
abstract = "MicroRNAs (miRNAs) are an important class of
non-coding RNAs that play an essential role in the
occurrence and development of various diseases.
Identifying the potential miRNA-disease associations
(MDAs) can be beneficial in understanding disease
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Newman:2024:DDI,
author = "Tara Newman and Hiu Fung Kevin Chang and Hosna
Jabbari",
title = "{DinoKnot}: Duplex Interaction of Nucleic Acids With
{PseudoKnots}",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "348--359",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3362308",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3362308",
abstract = "Interaction of nucleic acid molecules is essential for
their functional roles in the cell and their
applications in biotechnology. While simple duplex
interactions have been studied before, the problem of
efficiently predicting the minimum free energy
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2024:CMS,
author = "Juan Wang and Zhenchang Wang and Shasha Yuan and
Chunhou Zheng and Jinxing Liu and Junliang Shang",
title = "A Clustering Method for Single-Cell {RNA-Seq} Data
Based on Automatic Weighting Penalty and Low-Rank
Representation",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "360--371",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3362472",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3362472",
abstract = "Advances in high-throughput single-cell RNA sequencing
(scRNA-seq) technology have provided more comprehensive
biological information on cell expression. Clustering
analysis is a critical step in scRNA-seq research and
provides clear knowledge of the cell \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2024:SNR,
author = "Xiao-Hui Yang and Ye-Tong Wang and Ming-Hui Wu and Fan
Li and Cheng-Long Zhou and Li-Jun Yang and Chen Zheng
and Yong Li and Zhi Li and Si-Yi Guo and Chun-Peng
Song",
title = "{SLPA-Net}: a Real-Time Recognition Network for
Intelligent Stomata Localization and Phenotypic
Analysis",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "372--382",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3364208",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3364208",
abstract = "Plant stomatal phenotype traits play an important role
in improving crop water use efficiency, stress
resistance and yield. However, at present, the
acquisition of phenotype traits mainly relies on manual
measurement, which is time-consuming and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2024:FNN,
author = "Shan Zhang and Yuan Zhou and Pei Geng and Qing Lu",
title = "Functional Neural Networks for High-Dimensional
Genetic Data Analysis",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "383--393",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3364614",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3364614",
abstract = "Artificial intelligence (AI) is a thriving research
field with many successful applications in areas such
as computer vision and speech recognition. Machine
learning methods, such as artificial neural networks
(ANN), play a central role in modern AI \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yan:2024:PPM,
author = "Cheng Yan and Guihua Duan",
title = "{PMDAGS}: Predicting {miRNA-Disease} Associations With
Graph Nonlinear Diffusion Convolution Network and
Similarities",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "394--404",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3366175",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3366175",
abstract = "Many studies have proven that microRNAs (miRNAs) can
participate in a wide range of biological processes and
can be considered as potential noninvasive biomarkers
for disease diagnosis and prognosis. Therefore, many
computational methods have been \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{S:2024:GNC,
author = "Sheena K. S. and Madhu S. Nair",
title = "{GenCoder}: a Novel Convolutional Neural Network Based
Autoencoder for Genomic Sequence Data Compression",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "405--415",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3366240",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3366240",
abstract = "Revolutionary advances in DNA sequencing technologies
fundamentally change the nature of genomics.
Today&\#x0027;s sequencing technologies have opened
into an outburst in genomic data volume. These data can
be used in various applications where long-term
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2024:MPH,
author = "Wei Wang and Zhenxi Sun and Dong Liu and Hongjun Zhang
and Juntao Li and Xianfang Wang and Yun Zhou",
title = "{MAHyNet}: Parallel Hybrid Network for {RNA-Protein}
Binding Sites Prediction Based on Multi-Head Attention
and Expectation Pooling",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "416--427",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3366545",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3366545",
abstract = "RNA-binding proteins (RBPs) can regulate biological
functions by interacting with specific RNAs, and play
an important role in many life activities. Therefore,
the rapid identification of RNA-protein binding sites
is crucial for functional annotation and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yoo:2024:IPS,
author = "Sunyong Yoo and Myeonghyeon Jeong and Subhin Seomun
and Kiseong Kim and Youngmahn Han",
title = "Interpretable Prediction of {SARS-CoV-2}
Epitope-Specific {TCR} Recognition Using a Pre-Trained
Protein Language Model",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "428--438",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3368046",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3368046",
abstract = "The emergence of the novel coronavirus, designated as
severe acute respiratory syndrome coronavirus-2
(SARS-CoV-2), has posed a significant threat to public
health worldwide. There has been progress in reducing
hospitalizations and deaths due to SARS-CoV-.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Shanthappa:2024:CPP,
author = "Pallavi M. Shanthappa and Neeraj Verma and Anu George
and Pawan K. Dhar and Prashanth Athri",
title = "Computational Prediction of Potential Vaccine
Candidates From {tRNA} Encoded peptides {(tREP)} Using
a Bioinformatic Workflow and Molecular Dynamics
Validations",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "439--449",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3371984",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3371984",
abstract = "Transfer RNAs (tRNA) are non-coding RNAs. Encouraged
by biological applications discovered for peptides
derived from other non-coding genomic regions, we
explore the possibility of deriving epitope-based
vaccines from tRNA encoded peptides (tREP) in this
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Solanki:2024:ESV,
author = "Arnav Solanki and James Cornette and Julia Udell and
George Vasmatzis and Marc Riedel",
title = "Evasive Spike Variants Elucidate the Preservation of
{T} Cell Immune Response to the {SARS-CoV-2} Omicron
Variant",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "450--460",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3372100",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3372100",
abstract = "The Omicron variants boast the highest infectivity
rates among all SARS-CoV-2 variants. Despite their
lower disease severity, they can reinfect COVID-19
patients and infect vaccinated individuals as well. The
high number of mutations in these variants \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jia:2024:AAD,
author = "Yuhang Jia and Siyu Li and Rui Jiang and Shengquan
Chen",
title = "Accurate Annotation for Differentiating and Imbalanced
Cell Types in Single-Cell Chromatin Accessibility
Data",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "461--471",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3372970",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3372970",
abstract = "Rapid advances in single-cell chromatin accessibility
sequencing (scCAS) technologies have enabled the
characterization of epigenomic heterogeneity and
increased the demand for automatic annotation of cell
types. However, there are few computational \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2024:TTS,
author = "Minglie Li and Shusen Zhou and Tong Liu and Chanjuan
Liu and Mujun Zang and Qingjun Wang",
title = "{TSVM}: Transfer Support Vector Machine for Predicting
{MPRA} Validated Regulatory Variants",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "472--479",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3374413",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3374413",
abstract = "Genome-wide association studies have shown that common
genetic variants associated with complex diseases are
mostly located in non-coding regions, which may not be
causal. In addition, the limited number of validated
non-coding functional variants makes \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ma:2024:PPP,
author = "Ke Ma and Jiawei Li and Mengyuan Zhao and Ibrahim
Zamit and Bin Lin and Fei Guo and Jijun Tang",
title = "{PPRTGI}: a Personalized {PageRank} Graph Neural
Network for {TF}-Target Gene Interaction Detection",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "480--491",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3374430",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3374430",
abstract = "Transcription factors (TFs) regulation is required for
the vast majority of biological processes in living
organisms. Some diseases may be caused by improper
transcriptional regulation. Identifying the target
genes of TFs is thus critical for \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2024:VHI,
author = "Weiling Li and Raunaq Malhotra and Steven Wu and
Manjari Jha and Allen Rodrigo and Mary Poss and Raj
Acharya",
title = "{ViPRA-Haplo}: {{\em De Novo\/}} Reconstruction of
Viral Populations Using Paired End Sequencing Data",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "492--500",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3374595",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3374595",
abstract = "We present ViPRA-Haplo, a {$<$ italic$>$ de}
{novo$<$}/{italic$>$} strain-specific assembly workflow
for reconstructing viral haplotypes in a viral
population from paired-end next generation sequencing
(NGS) data. The proposed Viral Path Reconstruction
Algorithm (ViPRA) \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cardona:2024:CON,
author = "Gabriel Cardona and Joan Carles Pons and Gerard Ribas
and Tom{\'a}s Mart{\'\i}nez Coronado",
title = "Comparison of Orchard Networks Using Their Extended $
\mu $-Representation",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "501--507",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3361390",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3361390",
abstract = "Phylogenetic networks generalize phylogenetic trees in
order to model reticulation events. Although the
comparison of phylogenetic trees is well studied, and
there are multiple ways to do it in an efficient way,
the situation is much different for \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Valdes-Jimenez:2024:PAD,
author = "Alejandro Vald{\'e}s-Jim{\'e}nez and Miguel
Reyes-Parada and Gabriel N{\'u}{\~n}ez-Vivanco and
Daniel Jim{\'e}nez-Gonz{\'a}lez",
title = "Parallel Algorithm for Discovering and Comparing
Three-Dimensional Proteins Patterns",
journal = j-TCBB,
volume = "21",
number = "3",
pages = "508--515",
month = may,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3367789",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Sep 26 07:01:14 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3367789",
abstract = "Identifying conserved (similar) three-dimensional
patterns among a set of proteins can be helpful for the
rational design of polypharmacological drugs. Some
available tools allow this identification from a
limited perspective, only considering the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhou:2024:EDL,
author = "Xiaokang Zhou and Carson K. Leung and Kevin I-Kai Wang
and Giancarlo Fortino",
title = "Editorial Deep Learning-Empowered Big Data Analytics
in Biomedical Applications and Digital Healthcare",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "516--520",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3371808",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3371808",
abstract = "Deep learning and big data analysis are among the most
important research topics in the fields of biomedical
applications and digital healthcare. With the fast
development of artificial intelligence (AI) and
Internets of Things (IoT) technologies, deep \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cao:2024:CGC,
author = "Kun Cao and Yangguang Cui and Liying Li and Junlong
Zhou and Shiyan Hu",
title = "{CPU-GPU} Cooperative {QoS} Optimization of
Personalized Digital Healthcare Using Machine Learning
and Swarm Intelligence",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "521--533",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3207509",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3207509",
abstract = "In recent decades, the rapid advances in information
technology have promoted a widespread deployment of
medical cyber-physical systems (MCPS), especially in
the area of digital healthcare. In digital healthcare,
medical edge devices empowered by CPU-GPU \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ganaie:2024:EDR,
author = "M. A. Ganaie and M. Tanveer",
title = "Ensemble Deep Random Vector Functional Link Network
Using Privileged Information for {Alzheimer}'s Disease
Diagnosis",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "534--545",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3170351",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3170351",
abstract = "Alzheimer's disease (AD) is a progressive brain
disorder. Machine learning models have been proposed
for the diagnosis of AD at early stage. Recently, deep
learning architectures have received quite a lot
attention. Most of the deep learning \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Malik:2024:GEE,
author = "Ashwani Kumar Malik and M. Tanveer",
title = "Graph Embedded Ensemble Deep Randomized Network for
Diagnosis of {Alzheimer}'s Disease",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "546--558",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3202707",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3202707",
abstract = "Randomized shallow/deep neural networks with closed
form solution avoid the shortcomings that exist in the
back propagation (BP) based trained neural networks.
Ensemble deep random vector functional link (edRVFL)
network utilize the strength of two \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Nan:2024:MCA,
author = "Fengtao Nan and Shunbao Li and Jiayu Wang and Yahui
Tang and Jun Qi and Menghui Zhou and Zhong Zhao and Yun
Yang and Po Yang",
title = "A Multi-Classification Accessment Framework for
Reproducible Evaluation of Multimodal Learning in
{Alzheimer}'s Disease",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "559--572",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3204619",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3204619",
abstract = "Multimodal learning is widely used in automated early
diagnosis of Alzheimer's disease. However, the current
studies are based on an assumption that different
modalities can provide more complementary information
to help classify the samples from \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Razzak:2024:CME,
author = "Imran Razzak and Saeeda Naz and Hamid Alinejad-Rokny
and Tu N. Nguyen and Fahmi Khalifa",
title = "A Cascaded Multiresolution Ensemble Deep Learning
Framework for Large Scale {Alzheimer}'s Disease
Detection Using Brain {MRIs}",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "573--581",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3219032",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3219032",
abstract = "Alzheimer's is progressive and irreversible type of
dementia, which causes degeneration and death of cells
and their connections in the brain. AD worsens over
time and greatly impacts patients' life and affects
their important mental \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ke:2024:DFL,
author = "Hengjin Ke and Dan Chen and Quanming Yao and Yunbo
Tang and Jia Wu and Jessica Monaghan and Paul Sowman
and David McAlpine",
title = "Deep Factor Learning for Accurate Brain Neuroimaging
Data Analysis on Discrimination for Structural {MRI}
and Functional {MRI}",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "582--595",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3252577",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3252577",
abstract = "Analysis of neuroimaging data (e.g., Magnetic
Resonance Imaging, structural and functional MRI) plays
an important role in monitoring brain dynamics and
probing brain structures. Neuroimaging data are
multi-featured and non-linear by nature, and it is a
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Deng:2024:MTL,
author = "Lizhen Deng and Yuxin Cao and Zhongyang Wang and
Xiaokang Wang and Yu Wang",
title = "A Multidimensional Tensor Low Rank Method for Magnetic
Resonance Image Denoising",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "596--606",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3272893",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3272893",
abstract = "In this paper, we present the Magnetic Resonance Image
(MRI) denoising method via nonlocal multidimensional
low rank tensor transformation constraint (NLRT). We
first design a nonlocal MRI denoising method by
non-local low rank tensor recovery framework.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yu:2024:CGE,
author = "Xiao Yu and Weimin Li and Jianjia Wang and Xing Wu and
Bin Sheng",
title = "Construction of Gene Expression Patterns to Identify
Critical Genes Under {SARS-CoV-$2$} Infection
Conditions",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "607--618",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3283534",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3283534",
abstract = "Severe Acute Respiratory Syndrome Coronavirus 2
(SARS-CoV-2) is a positive-stranded single-stranded RNA
virus with an envelope frequently altered by unstable
genetic material, making it extremely difficult for
vaccines, drugs, and diagnostics to work. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhang:2024:SHO,
author = "Yongting Zhang and Yonggang Gao and Huanhuan Wang and
Huaming Wu and Youbing Xia and Xiang Wu",
title = "A Secure High-Order Gene Interaction Detection
Algorithm Based on Deep Neural Network",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "619--630",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3214863",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3214863",
abstract = "Identifying high-order Single Nucleotide Polymorphism
(SNP) interactions of additive genetic model is crucial
for detecting complex disease gene-type and predicting
pathogenic genes of various disorders. We present a
novel framework for high-order gene \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Djenouri:2024:SPP,
author = "Youcef Djenouri and Asma Belhadi and Gautam Srivastava
and Jerry Chun-Wei Lin",
title = "A Secure Parallel Pattern Mining System for {Medical
Internet of Things}",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "631--643",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3233803",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3233803",
abstract = "In this paper, a new generic parallel pattern mining
framework called multi-objective Decomposition for
Parallel Pattern-Mining (MD-PPM) is developed to solve
challenges in the Internet of Medical Things through
big data exploration. MD-PPM discovers \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Sarkar:2024:HSB,
author = "Joy Lal Sarkar and {Ramasamy V} and Abhishek Majumder
and Bibudhendu Pati and Chhabi Rani Panigrahi and
Weizheng Wang and Nawab Muhammad Faseeh Qureshi and
Chunhua Su and Kapal Dev",
title = "{I-Health}: {SDN}-Based Fog Architecture for {IIoT}
Applications in Healthcare",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "644--651",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3193918",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3193918",
abstract = "The Industrial Internet of Things (IIoT) has been
introduced in an era of increasingly broad potentials
in the medical industry. In recent years, IIoT-based
healthcare applications have grown in popularity, with
the majority of them relying on Wireless \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Bhavani:2024:SCV,
author = "T. Bhavani and P. VamseeKrishna and Chinmay
Chakraborty and Priyanka Dwivedi",
title = "Stress Classification and Vital Signs Forecasting for
{IoT--Health} Monitoring",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "652--659",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3196151",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3196151",
abstract = "Health monitoring embedded with intelligence is the
demand of the day. In this era of a large population
with the emergence of a variety of diseases, the demand
for healthcare facilities is high. Yet there is
scarcity of medical experts, technicians for \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lv:2024:DLE,
author = "Zhihan Lv and Jinkang Guo and Haibin Lv",
title = "Deep Learning-Empowered Clinical Big Data Analytics in
Healthcare Digital Twins",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "660--669",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3252668",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3252668",
abstract = "With the rapid development of information technology,
great changes have taken place in the way of managing,
analyzing, and using data in all walks of life. Using
deep learning algorithm for data analysis in the field
of medicine can improve the accuracy \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhou:2024:SDR,
author = "Yichao Zhou and Zhisen Hu and Zuxing Xuan and Yangang
Wang and Xiyuan Hu",
title = "Synchronizing Detection and Removal of Smoke in
Endoscopic Images With Cyclic Consistency Adversarial
Nets",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "670--680",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3204673",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3204673",
abstract = "Smoke removal is an important and meaningful issue for
endoscopic surgery, which can enhance the visual
quality of endoscopic images. Because it is practically
impossible to construct a large training dataset of
pair-matched endoscopic images with/without \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xu:2024:SNS,
author = "Guoxia Xu and Hao Wang and Marius Pedersen and Meng
Zhao and Hu Zhu",
title = "{SSP-Net}: a {Siamese}-Based Structure-Preserving
Generative Adversarial Network for Unpaired Medical
Image Enhancement",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "681--691",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3256709",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3256709",
abstract = "Recently, unpaired medical image enhancement is one of
the important topics in medical research. Although deep
learning-based methods have achieved remarkable success
in medical image enhancement, such methods face the
challenge of low-quality training \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhou:2024:AUD,
author = "Hanchong Zhou and Henry Leung and Bhashyam Balaji",
title = "{AR-UNet}: a Deformable Image Registration Network
with Cyclic Training",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "692--700",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3284215",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3284215",
abstract = "Deformable image registration is a process to
determine the non-linear spatial correspondence among
deformed image pairs. Generative registration network
is a novel structure involving a generative
registration network and a discriminative network that
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Jiang:2024:SSL,
author = "Zijing Jiang and Linyan Wang and Yaqi Wang and
Gangyong Jia and Guodong Zeng and Jun Wang and Yunxiang
Li and Dechao Chen and Guiping Qian and Qun Jin",
title = "A Self-Supervised Learning Based Framework for Eyelid
Malignant Melanoma Diagnosis in Whole Slide Images",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "701--714",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3207352",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3207352",
abstract = "Eyelid malignant melanoma (MM) is a rare disease with
high mortality. Accurate diagnosis of such disease is
important but challenging. In clinical practice, the
diagnosis of MM is currently performed manually by
pathologists, which is subjective and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2024:MTI,
author = "Wenyan Wang and Yongtao Li and Kun Lu and Jun Zhang
and Peng Chen and Ke Yan and Bing Wang",
title = "Medical Tumor Image Classification Based on Few-Shot
Learning",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "715--724",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3282226",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3282226",
abstract = "As a high mortality disease, cancer seriously affects
people's life and well-being. Reliance on pathologists
to assess disease progression from pathological images
is inaccurate and burdensome. Computer aided diagnosis
(CAD) system can effectively \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lu:2024:MML,
author = "Liangfu Lu and Xudong Cui and Zhiyuan Tan and Yulei
Wu",
title = "{MedOptNet}: Meta-Learning Framework for Few-Shot
Medical Image Classification",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "725--736",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3284846",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3284846",
abstract = "In the medical research domain, limited data and high
annotation costs have made efficient classification
under few-shot conditions a popular research area. This
paper proposes a meta-learning framework, termed
MedOptNet, for few-shot medical image \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Xie:2024:RCA,
author = "Xia Xie and Yuanyishu Tian and Kaoru Ota and Mianxiong
Dong and Zhelong Liu and Hai Jin and Dezhong Yao",
title = "Reinforced Computer-Aided Framework for Diagnosing
Thyroid Cancer",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "737--747",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3251323",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3251323",
abstract = "Thyroid cancer is the most pervasive disease in the
endocrine system and is getting extensive attention.
The most prevalent method for an early check is
ultrasound examination. Traditional research mainly
concentrates on promoting the performance of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zeng:2024:PFF,
author = "Lirong Zeng and Mengxing Huang and Yuchun Li and Qiong
Chen and Hong-Ning Dai",
title = "Progressive Feature Fusion Attention Dense Network for
Speckle Noise Removal in {OCT} Images",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "748--756",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3205217",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3205217",
abstract = "Although deep learning for Big Data analytics has
achieved promising results in the field of optical
coherence tomography (OCT) image denoising, the low
recognition rate caused by complex noise distribution
and a large number of redundant features is \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Guan:2024:BDA,
author = "Peiyuan Guan and Keping Yu and Wei Wei and YanLin Tan
and Jia Wu",
title = "Big Data Analytics on Lung Cancer Diagnosis Framework
With Deep Learning",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "757--768",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3281638",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3281638",
abstract = "As the segment of diseased tissue in PET images is
time-consuming, laborious and low accuracy, this work
proposes an automated framework for PET image
screening, denoising and diseased tissue segmentation.
First, taking into account the characteristics of
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Rehman:2024:DLT,
author = "Amjad Rehman and Majid Harouni and Farzaneh Zogh and
Tanzila Saba and Mohsen Karimi and Faten S. Alamri and
Gwanggil Jeon",
title = "Detection of Lungs Tumors in {CT} Scan Images Using
Convolutional Neural Networks",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "769--777",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3315303",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3315303",
abstract = "Current human being's lifestyle has caused \&
exacerbated many diseases. One of these diseases is
cancer, and among all kinds of cancers like, brain
pulmonary; lung cancer is fatal. The cancers could be
detected early to save lives using \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhou:2024:HHN,
author = "Qingguo Zhou and Rui Zhao and Yilin Hu and Jinqiang
Wang and Rui Zhou",
title = "Hierarchical Hybrid Networks for Automatic Pulmonary
Blood Vessel Segmentation in Computed Tomography
Images",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "778--788",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3281828",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3281828",
abstract = "Pulmonary arterial hypertension (PAH) is considered
the third most common cardiovascular disease after
coronary heart disease and hypertension. The diagnosis
of PAH is mainly based on the comprehensive judgment of
computed tomography and other medical \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lu:2024:CCM,
author = "Fangfang Lu and Zhihao Zhang and Shuai Zhao and
Xiantian Lin and Zhengyu Zhang and Bei Jin and Weiyan
Gu and Jingjing Chen and Xiaoxin Wu",
title = "{CMM}: a {CNN-MLP} Model for {COVID-19} Lesion
Segmentation and Severity Grading",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "789--802",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3253901",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3253901",
abstract = "In this paper, a CNN-MLP model (CMM) is proposed for
COVID-19 lesion segmentation and severity grading in CT
images. The CMM starts by lung segmentation using UNet,
and then segmenting the lesion from the lung region
using a multi-scale deep supervised \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{He:2024:QEQ,
author = "Zaobo He and Zhipeng Cai",
title = "Quantifying the Effect of Quarantine Control and
Optimizing Its Cost in {COVID-19} Pandemic",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "803--813",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3215559",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3215559",
abstract = "The novel coronavirus has been spreading worldwide and
emerged as a public health crisis. As the rapid rise of
infected population count, a wide variety of stringent
non-pharmaceutical interventions have been taken by
cities and countries around the globe,. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ahmed:2024:AIB,
author = "Imran Ahmed and Abdellah Chehri and Gwanggil Jeon",
title = "Artificial Intelligence and Blockchain Enabled Smart
Healthcare System for Monitoring and Detection of
{COVID-19} in Biomedical Images",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "814--822",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3294333",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3294333",
abstract = "Millions of individuals around the world have been
impacted by the ongoing coronavirus outbreak, known as
the COVID-19 pandemic. Blockchain, Artificial
Intelligence (AI), and other cutting-edge digital and
innovative technologies have all offered \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wu:2024:CCC,
author = "Yirui Wu and Qiran Kong and Lilai Zhang and Aniello
Castiglione and Michele Nappi and Shaohua Wan",
title = "{CDT-CAD}: Context-Aware Deformable Transformers for
End-to-End Chest Abnormality Detection on {X}-Ray
Images",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "823--834",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3258455",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3258455",
abstract = "Deep learning methods have achieved great success in
medical image analysis domain. However, most of them
suffer from slow convergency and high computing cost,
which prevents their further widely usage in practical
scenarios. Moreover, it has been proved \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2024:ICF,
author = "Zheng Li and Xiaolong Xu and Xuefei Cao and Wentao Liu
and Yiwen Zhang and Dehua Chen and Haipeng Dai",
title = "Integrated {CNN} and Federated Learning for {COVID-19}
Detection on Chest {X}-Ray Images",
journal = j-TCBB,
volume = "21",
number = "4",
pages = "835--845",
month = jul,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2022.3184319",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Aug 22 12:10:22 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2022.3184319",
abstract = "Currently, Coronavirus Disease 2019 (COVID-19) is
still endangering world health and safety and deep
learning (DL) is expected to be the most powerful
method for efficient detection of COVID-19. However,
patients' privacy concerns prohibit data \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cai:2024:GEI,
author = "Zhipeng Cai and Alexander Zelikovsky",
title = "{Guest Editors}' Introduction to the Special Section
on Bioinformatics Research and Applications",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1141--1142",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3390374",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3390374",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Duan:2024:CEN,
author = "Junwen Duan and Shuyue Liu and Xincheng Liao and Feng
Gong and Hailin Yue and Jianxin Wang",
title = "{Chinese} {EMR} Named Entity Recognition Using Fused
Label Relations Based on Machine Reading Comprehension
Framework",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1143--1153",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3376591",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3376591",
abstract = "Chinese electronic medical record (EMR) presents
significant challenges for named entity recognition
(NER) due to their specialized nature, unique language
features, and diverse expressions. Traditionally, NER
is treated as a sequence labeling task, where
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Li:2024:TPC,
author = "Zeqian Li and Yijia Zhang and Peixuan Zhou",
title = "Temporal Protein Complex Identification Based on
Dynamic Heterogeneous Protein Information Network
Representation Learning",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1154--1164",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3351078",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3351078",
abstract = "Protein complexes, as the fundamental units of
cellular function and regulation, play a crucial role
in understanding the normal physiological functions of
cells. Existing methods for protein complex
identification attempt to introduce other biological
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yan:2024:GES,
author = "Da Yan and Catia Pesquita and Carsten G{\"o}rg and
Jake Y. Chen",
title = "Guest Editorial Selected Papers From {BIOKDD 2022}",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1165--1167",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3429784",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3429784",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Tanvir:2024:DPH,
author = "Farhan Tanvir and Khaled Mohammed Saifuddin and
Muhammad Ifte Khairul Islam and Esra Akbas",
title = "{DDI} Prediction With Heterogeneous Information
Network --- Meta-Path Based Approach",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1168--1179",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3417715",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3417715",
abstract = "Drug-drug interaction (DDI) indicates where a
particular drug's desired course of action is modified
when taken with other drug (s). DDIs may hamper,
enhance, or reduce the expected effect of either drug
or, in the worst possible scenario, cause an \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Raza:2024:ICD,
author = "Shaina Raza and Chen Ding",
title = "Improving Clinical Decision Making With a Two-Stage
Recommender System",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1180--1190",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2023.3318209",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2023.3318209",
abstract = "Clinical decision-making is complex and
time-intensive. To help in this effort, clinical
recommender systems (RS) have been designed to
facilitate healthcare practitioners with personalized
advice. However, designing an effective clinical RS
poses \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ji:2024:SSL,
author = "Cunmei Ji and Ning Yu and Yutian Wang and Jiancheng Ni
and Chunhou Zheng",
title = "{SGLMDA}: a Subgraph Learning-Based Method for
{miRNA}-Disease Association Prediction",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1191--1201",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3373772",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3373772",
abstract = "MicroRNAs (miRNA) are endogenous non-coding RNAs,
typically around 23 nucleotides in length. Many miRNAs
have been founded to play crucial roles in gene
regulation though post-transcriptional repression in
animals. Existing studies suggest that the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Duan:2024:BAD,
author = "Junwen Duan and Huai Guo and Han Jiang and Fei Guo and
Jianxin Wang",
title = "Boundary-Aware Dual Biaffine Model for Sequential
Sentence Classification in Biomedical Documents",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1202--1210",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3376566",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3376566",
abstract = "Assigning appropriate rhetorical roles, such as
&\#x201C;background,&\#x201D;
&\#x201C;intervention,&\#x201D; and
&\#x201C;outcome,&\#x201D; to sentences in biomedical
documents can streamline the process for physicians to
locate evidence and resources for \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Hsiao:2024:NMC,
author = "Yen-Che Hsiao and Abhishek Dutta",
title = "Network Modeling and Control of Dynamic Disease
Pathways, Review and Perspectives",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1211--1230",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3378155",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3378155",
abstract = "Dynamic disease pathways are a combination of complex
dynamical processes among bio-molecules in a cell that
leads to diseases. Network modeling of disease pathways
considers disease-related bio-molecules (e.g. DNA, RNA,
transcription factors, enzymes, \ldots{})",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2024:TTD,
author = "Qingsong Wang and Ruiquan Ge and Changmiao Wang and
Ahmed Elazab and Qiming Fang and Renfeng Zhang",
title = "{TDFFM}: Transformer and Deep Forest Fusion Model for
Predicting Coronavirus {3C}-Like Protease Cleavage
Sites",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1231--1241",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3378470",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3378470",
abstract = "COVID-19, caused by the highly contagious SARS-CoV-2
virus, is distinguished by its positive-sense,
single-stranded RNA genome. A thorough understanding of
SARS-CoV-2 pathogenesis is crucial for halting its
proliferation. Notably, the 3C-like protease of
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Palacio:2024:DPC,
author = "Ana Le{\'o}n Palacio and Alberto Garc{\'\i}a S. and
Jos{\'e} Fabi{\'a}n Reyes Rom{\'a}n and Mireia Costa
and Oscar Pastor",
title = "The {Delfos Platform}: a Conceptual Model-Based
Solution for the Enhancement of Precision Medicine",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1242--1253",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3377928",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3377928",
abstract = "The use in the clinical practice of the vast amount of
genomic data generated by current sequencing
technologies constitutes a bottleneck for the progress
of Precision Medicine (PM). Various problems inherent
to the genomics domain (i.e., dispersion, \ldots{})",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Cai:2024:FPP,
author = "Changfeng Cai and Jianghui Li and Yuanling Xia and
Weihua Li",
title = "{FluPMT}: Prediction of Predominant Strains of
Influenza {A} Viruses via Multi-Task Learning",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1254--1263",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3378468",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3378468",
abstract = "Seasonal influenza vaccines play a crucial role in
saving numerous lives annually. However, the constant
evolution of the influenza A virus necessitates
frequent vaccine updates to ensure its ongoing
effectiveness. The decision to develop a new vaccine
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Biswas:2024:FFG,
author = "Sumona Biswas and Shovan Barma",
title = "Feature Fusion {GAN} Based Virtual Staining on Plant
Microscopy Images",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1264--1273",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3380634",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3380634",
abstract = "Virtual staining of microscopy specimens using
GAN-based methods could resolve critical concerns of
manual staining process as displayed in recent studies
on histopathology images. However, most of these works
use basic-GAN framework ignoring microscopy \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2024:NMT,
author = "Wenya Yang and Sai Zou and Hongfeng Gao and Lei Wang
and Wei Ni",
title = "A Novel Method for Targeted Identification of
Essential Proteins by Integrating Chemical Reaction
Optimization and Naive {Bayes} Model",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1274--1286",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3382392",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3382392",
abstract = "Targeted identification of essential proteins is of
great significance for species identification, drug
manufacturing, and disease treatment. It is a challenge
to analyze the binding mechanism between essential
proteins and improve the identification \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Yang:2024:EKO,
author = "Sen Yang and Peng Cheng and Yang Liu and Dawei Feng
and Shengqi Wang",
title = "Exploring the Knowledge of an Outstanding Protein to
Protein Interaction Transformer",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1287--1298",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3381825",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3381825",
abstract = "Protein-to-protein interaction (PPI) prediction aims
to predict whether two given proteins interact or not.
Compared with traditional experimental methods of high
cost and low efficiency, the current deep learning
based approach makes it possible to \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chong:2024:DST,
author = "Xiaoya Chong and Howard Leung and Qing Li and Jianhua
Yao and Niyun Zhou",
title = "Deep Spatio-Temporal Network for Low-{SNR} Cryo-{EM}
Movie Frame Enhancement",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1299--1310",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3380410",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3380410",
abstract = "Cryo-EM in single particle analysis is known to have
low SNR and requires to utilize several frames of the
same particle sample to restore one high-quality image
for visualizing that particle. However, the low SNR of
cryo-EM movie and motion caused by \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Guo:2024:IDE,
author = "Yin Guo and Yanni Xiao and Limin Li",
title = "Identifying Differentially Expressed Genes in {RNA}
Sequencing Data With Small Labelled Samples",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1311--1321",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3382147",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3382147",
abstract = "RNA-seq, including bulk RNA-seq and single-cell
RNA-seq, is a next-generation sequencing-based RNA
profiling method capable of measuring gene expression
patterns with high resolution, and has gradually become
an essential tool for the analysis of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Hosseini:2024:MDR,
author = "Seyed Hamid Hosseini and Mahdi Imani",
title = "Modeling Defensive Response of Cells to Therapies:
Equilibrium Interventions for Regulatory Networks",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1322--1334",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3383814",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3383814",
abstract = "A major objective in genomics is to design
interventions that can shift undesirable behaviors of
such systems (i.e., those associated with cancers) into
desirable ones. Several intervention policies have been
developed in recent years, including dynamic \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2024:NEM,
author = "Huan Wang and Ziwen Cui and Yinguang Yang and Baijing
Wang and Lida Zhu and Wen Zhang",
title = "A Network Enhancement Method to Identify Spurious
Drug-Drug Interactions",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1335--1347",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3385796",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3385796",
abstract = "As medical safety and drug regulation gain heightened
attention, the detection of spurious drug-drug
interactions (DDI) has become key in healthcare.
Although current research using graph neural networks
(GNNs) to predict DDI has shown impressive results,
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liang:2024:MPM,
author = "Ying Liang and Xiya You and Zequn Zhang and Shi Qiu
and Suhui Li and Lianlian Fu",
title = "{MGFmiRNAloc}: Predicting {miRNA} Subcellular
Localization Using Molecular Graph Feature and
Convolutional Block Attention Module",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1348--1357",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3383438",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3383438",
abstract = "MiRNA has distinct physiological functions at various
cellular locations. However, few effective
computational methods for predicting the subcellular
location of miRNA exist, thereby leaving considerable
room for improvement. Accordingly, our study \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Ge:2024:HNH,
author = "Ruiquan Ge and Yixiao Xia and Minchao Jiang and
Gangyong Jia and Xiaoyang Jing and Ye Li and Yunpeng
Cai",
title = "{HybAVPnet}: a Novel Hybrid Network Architecture for
Antiviral Peptides Prediction",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1358--1365",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3385635",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3385635",
abstract = "Viruses pose a great threat to human production and
life, thus the research and development of antiviral
drugs is urgently needed. Antiviral peptides play an
important role in drug design and development. Compared
with the time-consuming and laborious wet \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Wang:2024:EEN,
author = "Xubin Wang and Yunhe Wang and Zhiqiang Ma and Ka-Chun
Wong and Xiangtao Li",
title = "Exhaustive Exploitation of Nature-Inspired Computation
for Cancer Screening in an Ensemble Manner",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1366--1379",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3385402",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3385402",
abstract = "Accurate screening of cancer types is crucial for
effective cancer detection and precise treatment
selection. However, the association between gene
expression profiles and tumors is often limited to a
small number of biomarker genes. While computational
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Hu:2024:END,
author = "Xiaowen Hu and Ying Jiang and Lei Deng",
title = "Exploring {ncRNA}-Drug Sensitivity Associations via
Graph Contrastive Learning",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1380--1389",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3385423",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3385423",
abstract = "Increasing evidence has shown that noncoding RNAs
(ncRNAs) can affect drug efficiency by modulating drug
sensitivity genes. Exploring the association between
ncRNAs and drug sensitivity is essential for drug
discovery and disease prevention. However, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Mondal:2024:MTL,
author = "Sankar Mondal and Pradipta Maji",
title = "Multi-Task Learning and Sparse Discriminant Canonical
Correlation Analysis for Identification of
Diagnosis-Specific Genotype--Phenotype Association",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1390--1402",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3386406",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3386406",
abstract = "The primary objective of imaging genetics research is
to investigate the complex genotype-phenotype
association for the disease under study. For example,
to understand the impact of genetic variations over the
brain functions and structure, the genotypic \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Liu:2024:TAI,
author = "Dian Liu and Zi Liu and Yunpeng Xia and Zhikang Wang
and Jiangning Song and Dong-Jun Yu",
title = "{TransC-ac4C}: Identification of {N4-Acetylcytidine}
{(ac4C)} Sites in {mRNA} Using Deep Learning",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1403--1412",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3386972",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3386972",
abstract = "N4-acetylcytidine (ac4C) is a post-transcriptional
modification in mRNA that is critical in mRNA
translation in terms of stability and regulation. In
the past few years, numerous approaches employing
convolutional neural networks (CNN) and Transformer
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lan:2024:LPC,
author = "Wei Lan and Chunling Li and Qingfeng Chen and Ning Yu
and Yi Pan and Yu Zheng and Yi-Ping Phoebe Chen",
title = "{LGCDA}: Predicting {CircRNA-Disease} Association
Based on Fusion of Local and Global Features",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1413--1422",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3387913",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3387913",
abstract = "CircRNA has been shown to be involved in the
occurrence of many diseases. Several computational
frameworks have been proposed to identify
circRNA-disease associations. Despite the existing
computational methods have obtained considerable
successes, these \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Chen:2024:PKN,
author = "Mingshuai Chen and Quan Zou and Ren Qi and Yijie
Ding",
title = "{PseU-KeMRF}: a Novel Method for Identifying {RNA}
Pseudouridine Sites",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1423--1435",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3389094",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3389094",
abstract = "Pseudouridine is a type of abundant RNA modification
that is seen in many different animals and is crucial
for a variety of biological functions. Accurately
identifying pseudouridine sites within the RNA sequence
is vital for the subsequent study of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Alkhanbouli:2024:ACA,
author = "Razan Alkhanbouli and Amira Al-Aamri and Maher Maalouf
and Kamal Taha and Andreas Henschel and Dirar Homouz",
title = "Analysis of Cancer-Associated Mutations of {POLB}
Using Machine Learning and Bioinformatics",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1436--1444",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3395777",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3395777",
abstract = "DNA damage is a critical factor in the onset and
progression of cancer. When DNA is damaged, the number
of genetic mutations increases, making it necessary to
activate DNA repair mechanisms. A crucial factor in the
base excision repair process, which \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Lu:2024:RPC,
author = "Pengli Lu and Yuehao Wang",
title = "{RDGAN}: Prediction of {circRNA}-Disease Associations
via Resistance Distance and Graph Attention Network",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1445--1457",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3402248",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3402248",
abstract = "As a series of single-stranded RNAs, circRNAs have
been implicated in numerous diseases and can serve as
valuable biomarkers for disease therapy and prevention.
However, traditional biological experiments demand
significant time and effort. Therefore, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
@Article{Zhao:2024:DTB,
author = "Lingling Zhao and Yan Zhu and Naifeng Wen and Chunyu
Wang and Junjie Wang and Yongfeng Yuan",
title = "Drug-Target Binding Affinity Prediction in a
Continuous Latent Space Using Variational
Autoencoders",
journal = j-TCBB,
volume = "21",
number = "5",
pages = "1458--1467",
month = sep # "\slash " # oct,
year = "2024",
CODEN = "ITCBCY",
DOI = "https://doi.org/10.1109/TCBB.2024.3402661",
ISSN = "1545-5963 (print), 1557-9964 (electronic)",
ISSN-L = "1545-5963",
bibdate = "Thu Oct 24 08:15:46 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
URL = "https://dl.acm.org/doi/10.1109/TCBB.2024.3402661",
abstract = "Accurate prediction of Drug-Target binding Affinity
(DTA) is a daunting yet pivotal task in the sphere of
drug discovery. Over the years, a plethora of deep
learning-based DTA models have emerged, rendering
promising results in predicting the binding \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "IEEE/ACM Trans. Comput. Biol. Bioinform.",
fjournal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
journal-URL = "https://dl.acm.org/loi/tcbb",
}
