%%% -*-BibTeX-*-
%%% ====================================================================
%%%  BibTeX-file{
%%%     author          = "Christopher Hugh Bryant",
%%%     version         = "1.05",
%%%     date            = "25 November 2011",
%%%     time            = "15:50:10 MDT",
%%%     filename        = "bryant-chris.bib",
%%%     address         = "The Robert Gordon University
%%%                        School of Computing
%%%                        St Andrew St, Aberdeen
%%%                        AB25 1HG
%%%                        Scotland, UK",
%%%     telephone       = "+441224 262737",
%%%     FAX             = "+441224 262727",
%%%     URL             = "http://www.scms.rgu.ac.uk/staff/chb",
%%%     checksum        = "64798 681 3744 35436",
%%%     email           = "chb at scms.rgu.ac.uk (Internet)",
%%%     codetable       = "ISO/ASCII",
%%%     keywords        = "bibliography, BibTeX",
%%%     license         = "public domain",
%%%     supported       = "yes",
%%%     docstring       = "This is a bibliography of publications of
%%%                        Christopher Hugh Bryant.  The companion LaTeX file
%%%                        bryant-christopher-h.ltx can be used to typeset
%%%                        this bibliography.
%%%
%%%                        At version 1.05, the year coverage looked
%%%                        like this:
%%%
%%%                             1994 (   1)    1997 (   5)    2000 (   6)
%%%                             1995 (   2)    1998 (   1)    2001 (   3)
%%%                             1996 (   2)    1999 (   1)
%%%
%%%                             Article:          7
%%%                             Booklet:          1
%%%                             InProceedings:    9
%%%                             Proceedings:      4
%%%
%%%                             Total entries:   21
%%%
%%%                        This file is available as part of the BibNet
%%%                        Project.  The master copy is available for
%%%                        public access on ftp.math.utah.edu in the
%%%                        directory tree /pub/bibnet/authors.  It is
%%%                        mirrored to netlib.bell-labs.com in the directory
%%%                        tree /netlib/bibnet/authors, from which it is
%%%                        available via anonymous ftp and the Netlib
%%%                        service.
%%%
%%%                        The checksum field above contains a CRC-16
%%%                        checksum as the first value, followed by the
%%%                        equivalent of the standard UNIX wc (word
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%%%                        Solovay's checksum utility.",
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%%% ====================================================================

%%% ====================================================================
%%% Publisher abbreviations:

@String{pub-MORGAN-KAUFMANN     = "Morgan Kaufmann Publishers"}

@String{pub-MORGAN-KAUFMANN:adr = "San Francisco, CA, USA"}

@String{pub-SV                  = "Springer-Verlag"}

@String{pub-SV:adr              = "Berlin, Germany~/ Heidelberg, Germany~/
                                  London, UK~/ etc."}

%%% ====================================================================
%%% Series abbreviations:

@String{ser-LNAI                = "Lecture Notes in Artificial Intelligence"}

@String{ser-LNCS                = "Lecture Notes in Computer Science"}

%%% ====================================================================
%%% Bibliography entries:

@Article{Bryant:1994:RES,
  author =       "C. H. Bryant and A. E. Adam and D. R. Taylor and R. C.
                 Rowe",
  title =        "Review of Expert Systems for Chromatography",
  journal =      "Analytica Chimica Acta",
  volume =       "297",
  number =       "3",
  pages =        "317--347",
  year =         "1994",
  CODEN =        "ACACAM",
  ISSN =         "0003-2670",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  URL =          "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_aca_review.ps.gz",
  abstract =     "Expert systems for chromatography are reviewed. A
                 taxonomy is proposed that allows present (and future)
                 expert systems in this area to be classified and
                 facilitates an understanding of their
                 inter-relationship. All the systems are described
                 focusing on the reasons for their development, what
                 their purpose was and how they were to be used. The
                 engineering methods, knowledge representations, tools
                 and architectures used for the systems are compared and
                 contrasted in a discussion covering all the stages of
                 the development life cycle of expert systems. The
                 review reveals that too often developers of expert
                 systems for chromatography do not justify their
                 decisions on engineering matters and that the
                 literature suggests that many ideas advocated by
                 knowledge engineers are not being used.",
}

@InProceedings{Bryant:1995:DCA,
  author =       "C. H. Bryant and A. E. Adam and D. R. Taylor and G. V.
                 Conroy and R. C. Rowe",
  booktitle =    "Data Mining",
  title =        "{DataMariner}, a Commercially Available Data Mining
                 Package, and its Application to a Chemistry Domain",
  publisher =    "UNICOM",
  address =      "London, UK",
  year =         "1995",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
}

@InProceedings{Bryant:1995:DKH,
  author =       "C. H. Bryant and A. E. Adam and D. R. Taylor and G. V.
                 Conroy and R. C. Rowe",
  booktitle =    "Knowledge Discovery in Databases",
  title =        "Discovering Knowledge Hidden in a Chemical Database
                 Using a Commercially Available {Data Mining} Tool",
  number =       "Digest 1995/021(B)",
  publisher =    "????",
  address =      "Savoy Place, London, WC2R OBL, UK",
  year =         "1995",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  series =       "IEE Computing and Control Division",
}

@Article{Bryant:1996:TES,
  author =       "C. H. Bryant and A. E. Adam and D. R. Taylor and R. C.
                 Rowe",
  title =        "Towards an Expert System for Enantioseparations:
                 Induction of Rules Using Machine Learning",
  journal =      "Chemometrics and Intelligent Laboratory Systems",
  volume =       "34",
  number =       "1",
  pages =        "21--40",
  year =         "1996",
  CODEN =        "CILSEN",
  ISSN =         "0169-7439",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  URL =          "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_DataMariner.ps.gz",
  abstract =     "A commercially available machine induction tool was
                 used in an attempt to automate the acquisition of the
                 knowledge needed for an expert system for
                 enantioseparations by High Performance Liquid
                 Chromatography using Pirkle-type chiral stationary
                 phases (CSPs). Various rule-sets were induced that
                 recommended particular CSP chiral selectors based on
                 the structural features of an enantiomer pair. The
                 results suggest that the accuracy of the optimal
                 rule-set is 63\% + or - 3\% which is more than ten
                 times greater than the accuracy that would have
                 resulted from a random choice.",
}

@InProceedings{McCluskey:1996:VFS,
  author =       "T. L. McCluskey and J. M. Porteous and M. M. West and
                 C. H. Bryant",
  booktitle =    "Proceedings of the BCS-FACS Northern Formal Methods
                 Workshop, Ilkley, UK",
  title =        "The Validation of Formal Specifications of
                 Requirements",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  month =        sep,
  year =         "1996",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  series =       "Electronic Workshops in Computing Series",
  URL =          "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_north_fm_ws.ps.gz",
}

@Booklet{Bryant:1997:CGR,
  author =       "C. H. Bryant",
  title =        "Computer Generation of Rules for an Expert System for
                 Enantioseparations",
  howpublished = "Invited presentation given at Chrial Technology and
                 Enantioseparations '97",
  address =      "Cambridge, UK",
  month =        apr,
  year =         "1997",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
}

@InProceedings{Bryant:1997:DMI,
  author =       "C. H. Bryant",
  title =        "{Data Mining} via {ILP}: The Application of {Progol}
                 to a Database of Enantioseparations",
  crossref =     "Lavrac:1997:ILP",
  pages =        "85--92",
  year =         "1997",
  bibdate =      "Thu Apr 4 13:44:03 MST 2002",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  series =       "Lecture Notes in Artificial Intelligence",
  URL =          "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_ilp97.ps.gz",
  abstract =     "As far as this author is aware, this is the first
                 paper to describe the application of Progol to
                 enantioseparations. A scheme is proposed for data
                 mining a relational database of published
                 enantioseparations using Progol. The application of the
                 scheme is described and a preliminary assessment of the
                 usefulness of the resulting generalisations is made
                 using their accuracy, size, ease of interpretation and
                 chemical justification.",
}

@Article{Bryant:1997:UIL,
  author =       "C. H. Bryant and A. E. Adam and D. R. Taylor and R. C.
                 Rowe",
  title =        "Using {Inductive Logic Programming} to Discover
                 Knowledge Hidden in Chemical Data",
  journal =      "Chemometrics and Intelligent Laboratory Systems",
  volume =       "36",
  number =       "2",
  pages =        "111--123",
  year =         "1997",
  CODEN =        "CILSEN",
  ISSN =         "0169-7439",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  URL =          "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_golem.ps.gz",
  abstract =     "This paper demonstrates how general purpose tools from
                 the field of Inductive Logic Programming (ILP) can be
                 applied to analytical chemistry. As far as these
                 authors are aware, this is the first published work to
                 describe the application of the ILP tool Golem to
                 separation science. An outline of the theory of ILP is
                 given, together with a description of Golem and
                 previous applications of ILP. The advantages of ILP
                 over classical machine induction techniques, such as
                 the Top-Down-Induction-of-Decision-Tree family, are
                 explained. A case-study is then presented in which
                 Golem is used to induce rules which predict, with a
                 high accuracy (82\%), whether each of a series of
                 attempted separations succeed or fail. The separation
                 data was obtained from published work on the attempted
                 separation of a series of 3-substituted phthalide
                 enantiomer pairs on
                 (R)-N-(3,5-dinitrobenzoyl)-phenylglycine.",
}

@InProceedings{West:1997:TGP,
  author =       "M. M. West and C. H. Bryant and T. L. McCluskey",
  booktitle =    "The preliminary Proceedings of the Seventh
                 International Workshop on Logic Program Synthesis and
                 Transformation",
  title =        "Transforming General Program Proofs: {A} Meta
                 Interpreter which Expands Negative Literals",
  publisher =    "????",
  address =      "Leuven, Belgium",
  year =         "1997",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  URL =          "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_lopstr97.ps.gz",
}

@Article{Bryant:1998:KDD,
  author =       "C. H. Bryant and R. C. Rowe",
  title =        "{Knowledge Discovery} in {Databases}: Application to
                 Chromatography",
  journal =      "Trends in Analytical Chemistry",
  volume =       "17",
  pages =        "18--24",
  month =        "1",
  year =         "1998",
  CODEN =        "TTAEDJ",
  ISSN =         "0165-9936",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  URL =          "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_TRAC.ps.gz",
  abstract =     "This paper reviews emerging computer techniques for
                 discovering knowledge from databases and their
                 application to various sets of separation data. The
                 data-sets include the separation of a diverse range of
                 analytes using either liquid, gas or ion
                 chromatography. The main conclusion is that the new
                 techniques should help to close the gap between the
                 rate at which chromatographic data is gathered and
                 stored electronically and the rate at which it can be
                 analysed and understood.",
}

@InProceedings{Bryant:1999:CAL,
  author =       "C. H. Bryant and S. H. Muggleton and C. D. Page and M.
                 J. E. Sternberg",
  editor =       "S. Colton",
  booktitle =    "Proceedings of AISB'99 Symposium on AI and Scientific
                 Creativity",
  title =        "Combining {Active Learning} with {Inductive Logic
                 Programming} to close the loop in {Machine Learning}",
  publisher =    "The Society for the Study of Artificial Intelligence
                 and Simulation of Behaviour (AISB)",
  address =      "",
  pages =        "59--64",
  year =         "1999",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  URL =          "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_aisb99.ps.gz;
                 http://www.cogs.susx.ac.uk/aisb/",
  abstract =     "Machine Learning (ML) systems that produce
                 human-comprehensible hypotheses from data are typically
                 open loop, with no direct link between the ML system
                 and the collection of data. This paper describes the
                 alternative, {\it Closed Loop Machine Learning}. This
                 is related to the area of Active Learning in which the
                 ML system actively selects experiments to discriminate
                 between contending hypotheses. In Closed Loop Machine
                 Learning the system not only selects but also carries
                 out the experiments in the learning domain. ASE-Progol,
                 a Closed Loop Machine Learning system, is proposed.
                 ASE-Progol will use the ILP system Progol to form the
                 initial hypothesis set. It will then devise experiments
                 to select between competing hypotheses, direct a robot
                 to perform the experiments, and finally analyse the
                 experimental results. ASE-Progol will then revise its
                 hypotheses and repeat the cycle until a unique
                 hypothesis remains. This will be, to our knowledge, the
                 first attempt to use a robot to carry out experiments
                 selected by Active Learning within a real world
                 application.",
}

@InProceedings{Muggleton:2000:LCL,
  author =       "S. H. Muggleton and C. H. Bryant and A. Srinivasan",
  title =        "Learning {Chomsky}-like Grammars for Biological
                 Sequence Families",
  crossref =     "Langley:2000:PSI",
  pages =        "631--638",
  year =         "2000",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  URL =          "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_icml2k.ps.gz",
  abstract =     "This paper presents a new method of measuring
                 performance when positives are rare and investigates
                 whether Chomsky-like grammar representations are useful
                 for learning accurate comprehensible predictors of
                 members of biological sequence families. The
                 positive-only learning framework of the Inductive Logic
                 Programming (ILP) system CProgol is used to generate a
                 grammar for recognising a class of proteins known as
                 human neuropeptide precursors (NPPs). As far as these
                 authors are aware, this is both the first biological
                 grammar learnt using ILP and the first real-world
                 scientific application of the positive-only learning
                 framework of CProgol. Performance is measured using
                 both predictive accuracy and a new cost function, {\em
                 Relative Advantage\/} ($RA$). The $RA$ results show
                 that searching for NPPs by using our best NPP predictor
                 as a filter is more than 100 times more efficient than
                 randomly selecting proteins for synthesis and testing
                 them for biological activity. The highest $RA$ was
                 achieved by a model which includes grammar-derived
                 features. This $RA$ is significantly higher than the
                 best $RA$ achieved without the use of the
                 grammar-derived features.",
}

@InProceedings{Muggleton:2000:MPW,
  author =       "S. H. Muggleton and C. H. Bryant and A. Srinivasan",
  title =        "Measuring Performance when Positives are Rare:
                 Relative Advantage versus Predictive Accuracy --- a
                 Biological Case-study",
  crossref =     "LopezdeMantaras:2000:MLE",
  pages =        "300--312",
  year =         "2000",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  URL =          "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_ecml2k.ps.gz;
                 http://www.springer.de/comp/lncs/index.html",
  abstract =     "This paper presents a new method of measuring
                 performance when positives are rare and investigates
                 whether Chomsky-like grammar representations are useful
                 for learning accurate comprehensible predictors of
                 members of biological sequence families. The
                 positive-only learning framework of the Inductive Logic
                 Programming (ILP) system CProgol is used to generate a
                 grammar for recognising a class of proteins known as
                 human neuropeptide precursors (NPPs). Performance is
                 measured using both predictive accuracy and a new cost
                 function, {\em Relative Advantage\/} ($RA$). The $RA$
                 results show that searching for NPPs by using our best
                 NPP predictor as a filter is more than 100 times more
                 efficient than randomly selecting proteins for
                 synthesis and testing them for biological activity.
                 Predictive accuracy is not a good measure of
                 performance for this domain because it does not
                 discriminate well between NPP recognition models:
                 despite covering varying numbers of (the rare)
                 positives, all the models are awarded a similar (high)
                 score by predictive accuracy because they all exclude
                 most of the abundant negatives.",
}

@InProceedings{Muggleton:2000:TCU,
  author =       "S. H. Muggleton and C. H. Bryant",
  title =        "Theory Completion using Inverse Entailment",
  crossref =     "Cussens:2000:ILP",
  pages =        "130--146",
  year =         "2000",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  URL =          "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_ilp2k.ps.gz;
                 http://www.springer.de/comp/lncs/index.html",
  abstract =     "The main real-world applications of Inductive Logic
                 Programming (ILP) to date involve the ``Observation
                 Predicate Learning'' (OPL) assumption, in which both
                 the examples and hypotheses define the same predicate.
                 However, in both scientific discovery and language
                 learning potential applications exist in which OPL does
                 not hold. OPL is ingrained within the theory and
                 performance testing of Machine Learning. A general ILP
                 technique called ``Theory Completion using Inverse
                 Entailment'' (TCIE) is introduced which is applicable
                 to non-OPL applications. TCIE is based on inverse
                 entailment and is closely allied to abductive
                 inference. The implementation of TCIE within Progol5.0
                 is described. The implementation uses contra-positives
                 in a similar way to Stickel's Prolog Technology Theorem
                 Prover. Progol5.0 is tested on two different data-sets.
                 The first dataset involves a grammar which translates
                 numbers to their representation in English. The second
                 dataset involves hypothesising the function of unknown
                 genes within a network of metabolic pathways. On both
                 datasets near complete recovery of performance is
                 achieved after relearning when randomly chosen portions
                 of background knowledge are removed. Progol5.0's
                 running times for experiments in this paper were
                 typically under 6 seconds on a standard laptop PC.",
}

@Article{Bryant:2001:CIL,
  author =       "C. H. Bryant and S. H. Muggleton and S. G. Oliver and
                 D. B. Kell and P. Reiser and R. D. King",
  title =        "{Combining Inductive Logic} Programming, {Active
                 Learning} and Robotics to Discover the Function of
                 Genes",
  journal =      "Electronic Transactions on Artificial Intelligence",
  volume =       "5",
  number =       "B",
  pages =        "1--36",
  year =         "2001",
  CODEN =        "????",
  ISSN =         "????",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  URL =          "http://www.ep.liu.se/ej/etai/2001/001/",
  abstract =     "The paper is addressed to AI workers with an interest
                 in biomolecular genetics and also to biomolecular
                 geneticists interested in what AI tools may do for
                 them. The authors are engaged in a collaborative
                 enterprise aimed at partially automating some aspects
                 of scientific work. These aspects include the processes
                 of forming hypotheses, devising trials to discriminate
                 between these competing hypotheses, physically
                 performing these trials and then using the results of
                 these trials to converge upon an accurate hypothesis.
                 As a potential component of the reasoning carried out
                 by an ``artificial scientist'' this paper describes
                 ASE-Progol, an Active Learning system which uses
                 Inductive Logic Programming to construct hypothesised
                 first-order theories and uses a CART-like algorithm to
                 select trials for eliminating ILP derived hypotheses.
                 In simulated yeast growth tests ASE-Progol was used to
                 rediscover how genes participate in the aromatic amino
                 acid pathway of {\em Saccharomyces cerevisiae}. The
                 cost of the chemicals consumed in converging upon a
                 hypothesis with an accuracy of around $88\%$ was
                 reduced by five orders of magnitude when trials were
                 selected by ASE-Progol rather than being sampled at
                 random. While the naive strategy of always choosing the
                 cheapest trial from the set of candidate trials led to
                 lower cumulative costs than ASE-Progol, both the naive
                 strategy and the random strategy took significantly
                 longer to converge upon a final hypothesis than
                 ASE-Progol. For example to reach an accuracy of $80\%$,
                 ASE-Progol required 4 days while random sampling
                 required 6 days and the naive strategy required 10
                 days.",
}

@Article{Muggleton:2001:GRU,
  author =       "S. H. Muggleton and C. H. Bryant and A. Srinivasan and
                 A. Whittaker and S. Topp and C. Rawlings",
  title =        "Are grammatical representations useful for learning
                 from biological sequence data? --- a case study",
  journal =      "Journal of Computational Biology",
  volume =       "8",
  number =       "5",
  pages =        "493--522",
  month =        oct,
  year =         "2001",
  CODEN =        "JCOBEM",
  ISSN =         "1066-5277",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  note =         "\copyright Mary Ann Liebert.",
  URL =          "http://www.liebertpub.com/",
  abstract =     "This paper investigates whether Chomsky-like grammar
                 representations are useful for learning cost-effective,
                 comprehensible predictors of members of biological
                 sequence families. The Inductive Logic Programming
                 (ILP) Bayesian approach to learning from positive
                 examples is used to generate a grammar for recognising
                 a class of proteins known as human neuropeptide
                 precursors (NPPs). Collectively, five of the co-authors
                 of this paper, have extensive expertise on NPPs and
                 general bioinformatics methods. Their motivation for
                 generating a NPP grammar was that none of the existing
                 bioinformatics methods could provide sufficient
                 cost-savings during the search for new NPPs. Prior to
                 this project experienced specialists at SmithKline
                 Beecham had tried for many months to hand-code such a
                 grammar but without success. Our best predictor makes
                 the search for novel NPPs {\bf more than 100 times more
                 efficient} than randomly selecting proteins for
                 synthesis and testing them for biological activity. As
                 far as these authors are aware, this is both the first
                 biological grammar learnt using ILP and the first
                 real-world scientific application of the ILP Bayesian
                 approach to learning from positive examples. A group of
                 features is derived from this grammar. Other groups of
                 features of NPPs are derived using other learning
                 strategies. Amalgams of these groups are formed. A
                 recognition model is generated for each amalgam using
                 C4.5 and C4.5rules and its performance is measured
                 using both predictive accuracy and a new cost function,
                 {\em Relative Advantage\/} ($RA$). The highest $RA$ was
                 achieved by a model which includes grammar-derived
                 features. This $RA$ is significantly higher than the
                 best $RA$ achieved without the use of the
                 grammar-derived features. Predictive accuracy is not a
                 good measure of performance for this domain because it
                 does not discriminate well between NPP recognition
                 models: despite covering varying numbers of (the rare)
                 positives, all the models are awarded a similar (high)
                 score by predictive accuracy because they all exclude
                 most of the abundant negatives.",
  finaldraft =   "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_jcb.ps.gz",
}

@Article{Reiser:2001:DLM,
  author =       "P. Reiser and R. D. King and D. B. Kell and S. H.
                 Muggleton and C. H. Bryant and S. G. Oliver",
  title =        "Developing a Logical Model of Yeast Metabolism",
  journal =      "Electronic Transactions on Artificial Intelligence",
  volume =       "5",
  number =       "B",
  pages =        "223--244",
  year =         "2001",
  CODEN =        "????",
  ISSN =         "????",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  URL =          "http://www.ep.liu.se/ej/etai/2001/013/",
  abstract =     "With the completion of the sequencing of genomes of
                 increasing numbers of organisms, the focus of biology
                 is moving to determining the role of these genes
                 (functional genomics). To this end it is useful to view
                 the cell as a biochemical machine: it consumes simple
                 molecules to manufacture more complex ones by chaining
                 together biochemical reactions into long sequences
                 referred to as {\em metabolic pathways}. Such metabolic
                 pathways are not linear but often intersect to form
                 complex networks. Genes play a fundamental role in
                 these networks by providing the information to
                 synthesise the enzymes that catalyse biochemical
                 reactions. Although developing a complete model of
                 metabolism is of fundamental importance to biology and
                 medicine, the size and complexity of the network has
                 proven beyond the capacity of human reasoning. This
                 paper presents the first results of the Robot Scientist
                 research programme that aims to automatically discover
                 the function of genes in the metabolism of the yeast
                 {\em Saccharomyces cerevisiae}. Results include: (1)
                 the first logical model of metabolism; (2) a method to
                 predict phenotype by deductive inference; and (3) a
                 method to infer reactions and gene function by
                 abductive inference. We describe the {\em in vivo\/}
                 experimental set-up which will allow these {\em in
                 silico\/} predictions to be automatically tested by a
                 laboratory robot.",
}

%%% ====================================================================
%%% Cross-referenced entries must come last:

@Proceedings{Lavrac:1997:ILP,
  editor =       "Nada Lavrac and Saso Dzeroski",
  booktitle =    "Inductive logic programming: 7th international
                 workshop, {ILP}-97, Prague, Czech Republic, September
                 17--20, 1997: proceedings",
  title =        "Inductive logic programming: 7th international
                 workshop, {ILP}-97, Prague, Czech Republic, September
                 17--20, 1997: proceedings",
  volume =       "1297",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "viii + 308",
  year =         "1997",
  CODEN =        "LNCSD9",
  ISBN =         "3-540-63514-9 (softcover)",
  ISBN-13 =      "978-3-540-63514-7 (softcover)",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "QA76.63.I52 1997",
  bibdate =      "Mon Nov 24 11:33:24 1997",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  series =       ser-LNAI # " and " # ser-LNCS,
  acknowledgement = ack-nhfb,
  annote =       "Revised versions of papers presented at the
                 workshop.",
  keywords =     "Logic programming --- Congresses.",
}

@Proceedings{Cussens:2000:ILP,
  editor =       "James Cussens and Alan Frisch",
  booktitle =    "Inductive logic programming: 10th International
                 Conference, {ILP} 2000, London, {UK}, July 2000:
                 proceedings",
  title =        "Inductive logic programming: 10th International
                 Conference, {ILP} 2000, London, {UK}, July 2000:
                 proceedings",
  volume =       "1866",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "x + 264",
  year =         "2000",
  ISBN =         "3-540-67795-X (softcover)",
  ISBN-13 =      "978-3-540-67795-6 (softcover)",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "QA267.A1 L43 no.1866",
  bibdate =      "Mon Oct 16 18:31:56 MDT 2000",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  series =       ser-LNCS # " and " # ser-LNAI,
  acknowledgement = ack-nhfb,
  keywords =     "logic programming -- congresses",
}

@Proceedings{Langley:2000:PSI,
  editor =       "Pat Langley",
  booktitle =    "Proceedings of the Seventeenth International
                 Conference on Machine Learning (ICML-2000), June
                 29--July 2, 2000, Stanford University",
  title =        "Proceedings of the Seventeenth International
                 Conference on Machine Learning ({ICML}-2000), June
                 29--July 2, 2000, Stanford University",
  publisher =    pub-MORGAN-KAUFMANN,
  address =      pub-MORGAN-KAUFMANN:adr,
  pages =        "xiv + 1219",
  year =         "2000",
  ISBN =         "1-55860-707-2",
  ISBN-13 =      "978-1-55860-707-1",
  LCCN =         "Q325.5 .I57 2000",
  bibdate =      "Thu Apr 04 13:57:19 2002",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  acknowledgement = ack-nhfb,
}

@Proceedings{LopezdeMantaras:2000:MLE,
  editor =       "Ramon {Lopez de Mantaras} and Enric Plaza",
  booktitle =    "Machine learning: {ECML} 2000: 11th European
                 Conference on Machine Learning, Barcelona, Catalonia,
                 Spain, May 31--June 2, 2000",
  title =        "Machine learning: {ECML} 2000: 11th European
                 Conference on Machine Learning, Barcelona, Catalonia,
                 Spain, May 31--June 2, 2000",
  volume =       "1810",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "xii + 460",
  year =         "2000",
  ISBN =         "3-540-67602-3",
  ISBN-13 =      "978-3-540-67602-7",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "QA267.A1 L43 no.1810",
  bibdate =      "Thu Apr 04 14:00:45 2002",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
  series =       ser-LNCS # " and " # ser-LNAI,
  acknowledgement = ack-nhfb,
  keywords =     "machine learning -- congresses; machine learning --
                 industrial applications -- congresses",
}