@article{Finn+Muggleton+Page+Srinivasan/98/Discovery,
	AUTHOR = "P. Finn and S.H. Muggleton and D. Page and A. Srinivasan",
	TITLE = "Pharmacophore Discovery using the {I}nductive {L}ogic
		{P}rogramming system {Progol}",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/finnpharm.pdf",
	JOURNAL = "Machine Learning",
	VOLUME = "30",
	PAGES = "241--271",
	YEAR = "1998"}
@article{bratmug:ilpapp,
	AUTHOR = "I. Bratko and S.H. Muggleton",
	TITLE = "Applications of {I}nductive {L}ogic {P}rogramming",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/cacm.pdf",
	YEAR = 1995,
	JOURNAL = "Communications of the ACM",
	VOLUME = 38,
	NUMBER = 11,
	PAGES = "65--70"}
@article{cootes:mi18,
	TITLE = "Automatic determination of protein fold signatures from
	  structured superposition",
	AUTHOR = "A.P. Cootes and S.H. Muggleton and R.B. Greaves and M.J. Sternberg",
	YEAR = 2001,
	JOURNAL = "Electronic Transactions in Artificial Intelligence",
	VOLUME = "6-B2",
	NUMBER = "026",
        MONTH = "November",
	PAGES = "245-274",
        URL = "http://www.ida.liu.se/ext/epa/cis/2001/026/tcover.html" }

@article{kmuggs:drugs,
	AUTHOR = "R.D. King and S.H. Muggleton and R. Lewis and M.J.E. Sternberg",
	TITLE = "Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase",
	JOURNAL = "Proceedings of the National Academy of Sciences",
	VOLUME = "89",
	NUMBER = "23",
	YEAR = 1992,
	PAGES = "11322--11326",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/pnas92.pdf"}
@article{kmuggs:muta,
	AUTHOR = "R.D. King and S.H. Muggleton and A. Srinivasan and M.J.E. Sternberg",
	TITLE = "Structure-activity relationships derived by machine
		learning: the use of atoms and their bond connectives
		to predict mutagenicity by inductive logic programming",
	JOURNAL = "Proceedings of the National Academy of Sciences",
	VOLUME = "93",
	PAGES = "438--442",
	YEAR = 1996,
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/pnas96.pdf"}
@article{kmuggs:robot,
	AUTHOR = "R.D. King and K.E. Whelan and F.M. Jones and P.K.G. Reiser and C.H. Bryant and S.H. Muggleton and D.B. Kell and S.G. Oliver",
	TITLE = "Functional genomic hypothesis generation and experimentation by a robot scientist",
	JOURNAL = "Nature",
	VOLUME = "427",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/Oliver_Jan15_hi.pdf",
	YEAR = 2004,
	PAGES = "247--252"}
@article{mugg:ilp,
	TITLE = "Inductive {L}ogic {P}rogramming",
	AUTHOR = "S.H. Muggleton",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ilp.pdf",
	YEAR = "1991",
	JOURNAL = "New Generation Computing",
	VOLUME = 8,
	NUMBER = 4,
	PAGES = "295--318"}
@article{muggks:proteins,
	AUTHOR = "S.H. Muggleton and R.D. King and M.J.E. Sternberg",
	TITLE = "Protein secondary structure prediction using logic-based
		machine learning",
	JOURNAL = "Protein Engineering",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/prot92.pdf",
	VOLUME = "5",
	NUMBER = "7",
	PAGES = "647--657",
	YEAR = 1992 }
@article{mugg:predinv,
	AUTHOR = "S.H. Muggleton",
	TITLE = "Predicate Invention and Utilisation",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/util.pdf",
	YEAR = "1994",
	JOURNAL = "Journal of Experimental and Theoretical Artificial Intelligence",
	PUBLISHER = "Taylor \& Francis",
	VOLUME = 6,
	NUMBER = 1,
	PAGES = "127--130"}
@article{mugg:sigart,
	AUTHOR = "S.H. Muggleton",
	TITLE = "Inductive Logic Programming: derivations, successes
			and shortcomings",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/sigart.pdf",
	JOURNAL = "SIGART Bulletin",
	PAGES = "5-11",
	VOLUME = "5",
	NUMBER = "1",
	YEAR = 1994 }
@article{mugg:bttj,
	AUTHOR = "S.H. Muggleton and D. Michie",
	TITLE = "Machine intelligibility and the duality principle",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/bttj.pdf",
	JOURNAL = "British Telecom Technology Journal",
	PAGES = "15--23",
	VOLUME = "14",
	NUMBER = "4",
	YEAR = 1996}
@article{srinmugg:aijmuta,
        AUTHOR = "A. Srinivasan and S.H. Muggleton and R.D. King and M.J.E. Sternberg",
        TITLE = "Theories for mutagenicity: a study of first-order
		and feature based induction",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ash_aij95.pdf",
	YEAR = "1996",
	JOURNAL = "Artificial Intelligence",
	VOLUME = "85",
	NUMBER = "1,2",
	PAGES = "277--299"}
@article{mugg:der,
	AUTHOR = "S.H. Muggleton and L. De Raedt",
	TITLE = "Inductive Logic Programming: Theory and Methods",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/lpj.pdf",
	YEAR = "1994",
	JOURNAL = "Journal of Logic Programming",
	VOLUME = "19,20",
	PAGES = "629--679"}
@article{mugg:progol,
	AUTHOR = "S.H. Muggleton",
	TITLE = "Inverse entailment and {P}rogol",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/InvEnt.pdf",
	YEAR = "1995",
	JOURNAL = "New Generation Computing",
	VOLUME = "13",
	PAGES = "245--286"}
@article{mugg:slp,
        AUTHOR = "S.H. Muggleton",
        TITLE = "Stochastic Logic Programs",
	ABSTRACT = "One way to represent a machine learning algorithm's
	  {\it bias} 
	  over the hypothesis and instance space is as a pair of probability
	  distributions.  This approach has been taken both within Bayesian
	  learning schemes and the framework of U-learnability. However, it
	  is not obvious how an Inductive Logic Programming (ILP) system should best
	  be provided with a probability distribution. This paper extends the
	  results of a previous paper by the author which
	  introduced {\it stochastic logic programs} as a means of providing a structured
	  definition of such a probability distribution.  Stochastic logic programs
	  are a generalisation of stochastic grammars.
	  A stochastic logic program consists of a set of labelled clauses
	  $p:C$ where $p$ is from the interval $[0,1]$ and
	  $C$ is a range-restricted definite clause.
	  A stochastic logic program $P$ has a distributional semantics, that is
	  one which assigns a probability distribution to the atoms
	  of each predicate in the Herbrand base of
	  the clauses in $P$. These probabilities are assigned to atoms according
	  to an SLD-resolution strategy which employs a stochastic selection rule.
	  It is shown that the probabilities can be
	  computed directly for {\it fail-free} logic programs and
	  by normalisation for arbitrary logic programs.
	  The stochastic proof strategy can be used to provide three distinct functions:
	  1) a method of sampling from the Herbrand base which can be used
	  to provide selected targets or example sets for ILP experiments,
	  2) a measure of the information content of examples or hypotheses;
	  this can be used to guide the search in an ILP system
	  and 3) a simple method for conditioning a given stochastic
	  logic program on samples of data. Functions 1) and 3)
	  are used to measure the generality of hypotheses
	  in the ILP system Progol4.2. This supports an implementation of
	  a Bayesian technique for learning from positive examples only.  ",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/slp.pdf",
        YEAR = 2001,
        JOURNAL = "Journal of Logic Programming",
        NOTE = "Accepted subject to revision"}
@article{mugg:cacm2,
	TITLE = "Scientific Knowledge Discovery using {I}nductive {L}ogic
		{P}rogramming",
	AUTHOR = "S.H. Muggleton",
	ABSTRACT = "This paper is an overview of scientific knowledge
	  discovery tasks carried out using Inductive Logic Programming
	  (ILP). The results reviewed have been published in some of the
	  top general science journals, and as such are among the strongest
	  examples of semi-automated scientific discovery in the Artificial
	  Intelligence literature.  Space restrictions do not permit this
	  paper to cover other discovery areas of ILP. These include the
	  discovery of linguistic features in natural language data and the
	  discovery of patterns in traffic data.",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/cacm2.pdf",
	MONTH = "November",
	YEAR = "1999",
	JOURNAL = "Communications of the ACM",
	VOLUME = "42",
	NUMBER = "11",
	PAGES = "42-46" }
@article{stern:rschem,
	TITLE = "Modelling the structure and function of enzymes by
		machine learning",
	AUTHOR = "M.J.E. Sternberg and R. Lewis and R.D. King and S.H. Muggleton",
	YEAR = 1992,
	JOURNAL = "Proceedings of the Royal Society of Chemistry:
		Faraday Discussions",
	VOLUME = 93,
	PAGES = "269--280"}
@article{stern:roysoc,
	TITLE = "Application of Machine Learning to Structural
		Molecular Biology",
	AUTHOR = "M.J.E. Sternberg and R.D. King and R. Lewis and S.H. Muggleton",
	YEAR = "1994",
	JOURNAL = "Philosophical Transactions of the Royal Society B",
	VOLUME = "344",
	PAGES = "365--371"
	}
@article{sternmugg:campy,
	TITLE = "Gene Function Hypotheses for the Campylobacter jejuni Glycome
		Generated by a Logic-Based Approach",
	AUTHOR = "M.J.E. Sternberg and A. Tamaddoni-Nezhad and V.I.. Lesk
	and and E. Kay and P.G. Hitchen and A. Cootes and L.B. Alphen and
	M.P. Lamoureux and H.C. Jarrell and C.J. Rawlings and E.C. Soo and
	C.M. Szymanski and A. Dell and B.W. Wren and S.H. Muggleton",
	YEAR = "2013",
	VOLUME = "425",
	NUMBER = "1",
	PAGES = "186-197",
	JOURNAL = "Journal of Moleular Biology",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/campy.pdf",
	}
@article{mugg:aij99,
        AUTHOR = "S.H. Muggleton",
        TITLE = "Inductive Logic Programming: issues, results and
the
		{LLL} challenge",
	ABSTRACT = "Inductive Logic Programming (ILP) is the area of AI which
deals
	with the induction of hypothesised predicate definitions from examples and
	background knowledge. Logic programs are used as a single representation
	for examples, background knowledge and hypotheses.
	ILP is differentiated from most other forms of Machine Learning (ML)
	both by its use of an expressive representation language
	and its ability to make use of logically encoded background knowledge.
	This has allowed successful applications of ILP
	in areas such as molecular biology and natural language which
	both have rich sources of background knowledge and both benefit
	from the use of an expressive concept representation languages. For instance,
	the ILP system Progol has recently been used to generate comprehensible
	descriptions of the 23 most populated fold classes of proteins,
	where no such descriptions had previously been formulated manually. 
	In the natural language area ILP has not only been shown to have higher
	accuracies than various other ML approaches in learning the
	past tense of English but also shown to be
	capable of learning accurate
	grammars which translate sentences into deductive database queries.
	The area of Learning Language in Logic (LLL) is producing a number of
	challenges to existing ILP theory and implementations. In particular,
	language applications of ILP require revision and extension of a
	hierarchically defined set of predicates in which the examples are typically
	only provided for predicates at the top of the hierarchy. New predicates often
	need to be invented, and complex recursion is usually involved.
	Advances in ILP theory and implementation
	related to the challenges of LLL are already producing beneficial advances in
	other sequence-oriented applications of ILP. In addition LLL
	is starting to develop its own character as a sub-discipline
	of AI involving the confluence of computational linguistics, machine learning
	and logic programming.",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/aij99.pdf",
	JOURNAL = "Artificial Intelligence",
	VOLUME = "114",
	NUMBER = "1--2",
	MONTH = "December",
	YEAR = "1999",
        PAGES = "283--296"}
@article{mugg:mljfolds,
        AUTHOR = "M. Turcotte and S.H. Muggleton and M.J.E. Sternberg",
        TITLE = "The Effect of Relational Background Knowledge on
	        Learning of Protein Three-Dimensional Fold Signatures",
	ABSTRACT = "As a form of Machine Learning the study of Inductive Logic
	Programming (ILP) is motivated by a central belief: relational description
	languages are better (in terms of accuracy and understandability) than
	propositional ones for certain real-world applications.
	This claim is investigated here for a particular application
	in structural molecular biology,
	that of constructing readable descriptions of the major protein folds.
	To the authors' knowledge Machine Learning has not previously been
	applied systematically to this task.
	In this application domain the domain expert (thord author) identified
	a natural divide between essentially propositional features and more
	structurally-orientated relational ones.
	The following null hypotheses are tested: 1) for a given ILP
	system (Progol) provision of relational background knowledge does
	not increase predictive accuracy, 2) a good propositional
	learning system (C5.0) without relational background knowledge will outperform
	Progol with relational background knowledge, 3) relational background
	knowledge does not produce improved explanatory insight. Null hypotheses
	1) and 2) are both refuted on cross-validation results carried
	out over 20 of the most populated protein folds. Hypothesis 3
	is refuted by demonstration of various insightful rules discovered only in the
	relationally-oriented learned rules.",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ml2000.pdf",
	JOURNAL = "Machine Learning",
	MONTH = "April-May",
	VOLUME = "1,2",
	PAGES = "81--96",
	YEAR = "2001" }
@article{turc:jmbfolds,
        AUTHOR = "M. Turcotte and S.H. Muggleton and M.J.E. Sternberg",
        TITLE = "Automated Discovery of Structural Signatures of Protein
		Fold and Function",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/foldsjmb.pdf",
	JOURNAL = "Journal of Molecular Biology",
	VOLUME = "306",
	PAGES = "591--605",
	YEAR = "2001" }
@article{mugg:statilp,
        AUTHOR = "S.H. Muggleton",
        TITLE = "Statistical Aspects of Logic-Based Machine Learning",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/statilp.pdf",
	JOURNAL = "ACM Transactions on Computational Logic",
	YEAR = "2001",
	NOTE = "Under revision"}
@article{turcotte:genfold,
        AUTHOR = "M. Turcotte and S.H. Muggleton and M.J. Sternberg",
        TITLE = "Generating protein three-dimensional fold signatures using
		inductive logic programming",
	JOURNAL = "Computational Chemistry",
	VOLUME = "26",
	PAGES = "57--64",
	YEAR = "2001" }
@article{bryant:jcb,
	TITLE = "Are grammatical representations useful for learning
        from biological sequence data? - a case study",
	AUTHOR = "S.H. Muggleton and C.H. Bryant and A. Srinivasan and
	A. Whittaker and S. Topp and C. Rawlings",
	YEAR = 2001,
	JOURNAL = "Journal of Computational Biology",
	VOLUME = 8,
        NUMBER = 5,
	PAGES = "493-521",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/bryantgram.pdf",
        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."}
@article{mugg:mi17:slplearn,
	TITLE = "Learning Stochastic Logic Programs",
	AUTHOR = "S.H. Muggleton",
	YEAR = 2000,
	JOURNAL = "Electronic Transactions in Artificial Intelligence",
	VOLUME = 4,
	NUMBER = "041",
        URL = "http://www.ida.liu.se/ext/epa/cis/2000/041/tcover.html" }
@article{bryant:mi18,
	TITLE = "Combining Inductive Logic Programming, Active Learning and
		Robotics to Discover the Function of Genes",
	AUTHOR = "C.H. Bryant and S.H. Muggleton and S.G. Oliver and D.B. Kell
		and P. Reiser and R.D. King",
	YEAR = 2001,
	JOURNAL = "Electronic Transactions in Artificial Intelligence",
	VOLUME = "5-B1",
	NUMBER = "012",
        MONTH = "November",
	PAGES = "1--36",
        URL = "http://www.ida.liu.se/ext/epa/cis/2001/012/tcover.html" }
@article{reiser:mi18,
	TITLE = "Developing a Logical Model of Yeast Metabolism",
	AUTHOR = "P.G.K. Reiser and R.D. King and D.B. Kell and S.H. Muggleton
		and C.H. Bryant and S.G. Oliver",
	YEAR = 2001,
	JOURNAL = "Electronic Transactions in Artificial Intelligence",
	VOLUME = "5-B2",
	NUMBER = "024",
        MONTH = "November",
	PAGES = "223--244",
        URL = "http://www.ida.liu.se/ext/epa/cis/2001/024/tcover.html" }
@article{mugg:mi19:slplearn,
	TITLE = "Learning Structure and Parameters of Stochastic Logic Programs",
	AUTHOR = "S.H. Muggleton",
	YEAR = 2002,
	JOURNAL = "Electronic Transactions in Artificial Intelligence",
	VOLUME = 6,
        URL = "http://www.ida.liu.se/ext/etai/received/machi/mi19.html" }
@article{angmugg:mi19:metabolism,
	TITLE = "Machine Learning Metabolic Pathway descriptions using a
		Probabilistic Relational Representation",
	AUTHOR = "N. Angelopoulos and S.H. Muggleton",
	YEAR = 2002,
	JOURNAL = "Electronic Transactions in Artificial Intelligence",
	VOLUME = 6,
        URL = "http://www.ida.liu.se/ext/etai/received/machi/mi19.html" }
@article{watmugg:mi19:stochact,
	TITLE = "First-order Stochastic Action Languages",
	AUTHOR = "H. Watanabe and S.H. Muggleton",
	YEAR = 2002,
	JOURNAL = "Electronic Transactions in Artificial Intelligence",
	VOLUME = 6,
        URL = "http://www.ida.liu.se/ext/etai/received/machi/mi19.html" }
@article{cootes:jmbfolds,
        AUTHOR = "A. Cootes and S.H. Muggleton and M.J.E. Sternberg",
        TITLE = "The automatic discovery of structural principles describing
		protein fold space",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/cootesfoldjmb.pdf",
	JOURNAL = "Journal of Molecular Biology",
	VOLUME = 330,
	NUMBER = 4,
	PAGES = "839--850",
	YEAR = "2003" }
@article{cootes:jmbnetworks,
        AUTHOR = "A. Cootes and S.H. Muggleton and M.J.E. Sternberg",
        TITLE = "The Identification of Similarities between
Biological Networks: Application to the
Metabolome and Interactome",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/cootesjmbnets.pdf",
	JOURNAL = "Journal of Molecular Biology",
	VOLUME = 369,
	PAGES = "1126--1139",
	YEAR = "2003" }
@article{sternmugg:qsar,
        AUTHOR = "M.J.E. Sternberg and S.H. Muggleton",
        TITLE = "Structure Activity Relationships ({SAR}) and Pharmacophore
		Discovery Using Inductive Logic Programming ({ILP})",
	JOURNAL = "QSAR and Combinatorial Science",
	VOLUME = "22",
	NUMBER = "5",
	PAGES = "527--532",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/qsarilp.pdf",
	YEAR = "2003" }
@article{mugg:nature,
        AUTHOR = "S.H. Muggleton",
        TITLE = "Exceeding Human Limits",
	JOURNAL = "Nature",
	VOLUME = "440",
	NUMBER = "7083",
	PAGES = "409--410",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/NatureCommentaryFinal.pdf",
	YEAR = "2006" }
@article{coltmugg:mlj,
        AUTHOR = "S. Colton and S.H. Muggleton",
        TITLE = "Mathematical Applications of Inductive Logic Programming",
	JOURNAL = "Machine Learning",
	VOLUME = "64",
	NUMBERS = "1-3",
	PAGES = "25--64",
	NOTE = " DOI: 10.1007/s10994-006-8259-x",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/mathsilp.pdf",
	YEAR = "2006" }
@article{alimugg:mljmetabduce,
        AUTHOR = "A. Tamaddoni-Nezhad and R. Chaleil and A. Kakas and
		S.H. Muggleton",
        TITLE = "Application of abductive {ILP} to learning metabolic network
		inhibition from temporal data",
	JOURNAL = "Machine Learning",
	VOLUME = "64",
	NUMBERS = "1-3",
	PAGES = "209-230",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/mljmetabduce.pdf",
	YEAR = "2006" }
@article{alimugg:ieeemetabduce,
        AUTHOR = "A. Tamaddoni-Nezhad and R. Chaleil and A. Kakas and
		M.J.E. Sternberg and J. Nicholson and S.H. Muggleton",
        TITLE = "Modeling the Effects of Toxins in Metabolic Networks",
	JOURNAL = "IEEE Engineering in Medicine and Biology",
	VOLUME = "26",
	NUMBERS = "2",
	PAGES = "37-46",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ieeemetabduce.pdf",
	YEAR = "2007" }
@article{cannon:svilpapp,
        AUTHOR = "E.O. Cannon and A. Amini and A. Bender and
		M. J. E. Sternberg and S.H. Muggleton and
		R.C. Glen and J.B.O. Mitchell",
        TITLE = "Support vector inductive logic programming outperforms the
		naive {B}ayes classifier and inductive logic programming for
		the classification of bioactive chemical compounds",
	JOURNAL = "Journal of Computer Aided Molecular Design",
	VOLUME = "21",
	NUMBERS = "5",
	PAGES = "269-280",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/cannonsvilp.pdf",
	YEAR = "2007" }
@article{amini:svilpapp,
        AUTHOR = "A. Amini and S.H. Muggleton, H. Lodhi and M.J.E. Sternberg",
        TITLE = "A Novel Logic-Based Approach for Quantitative Toxicology
		Prediction",
	JOURNAL = "Journal of Chemical Information and Modelling",
	VOLUME = "47",
	NUMBER = "3",
	PAGES = "998-1006",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/aminisvilpapp.pdf",
	YEAR = "2007" }
@article{cootes:networks,
        AUTHOR = "A.P. Cootes and S.H. Muggleton and M.J.E. Sternberg",
        TITLE = "The Identification of Similarities between Biological
		Networks: Application to the Metabolome and Interactome
		Prediction",
	JOURNAL = "Journal of Molecular Biology",
	VOLUME = "369",
	NUMBER = "4",
	PAGES = "1126--1139",
	NOTE = "DOI: 10.1016/j.jmb.2007.03.013",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/cootesnet.pdf",
	YEAR = "2007" }
@article{shrimpton:docksvilp,
        AUTHOR = "A. Amini and P.J. Shrimpton and S.H. Muggleton and
		M.J.E. Sternberg",
        TITLE = "A general approach for developing system-specific
		functions to score protein-ligand docked complexes using
		support vector inductive logic programming",
	JOURNAL = "Proteins",
	VOLUME = "69",
	NUMBER = "4",
	PAGES = "823-831",
	NOTE = "DOI: 10.1002/prot.21782",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/svilp_proteins.pdf",
	YEAR = "2007" }
@article{mugali:qgga,
        AUTHOR = "S.H. Muggleton and A. Tamaddoni-Nezhad",
        TITLE = "{QG/GA}: A Stochastic Search for {P}rogol",
	JOURNAL = "Machine Learning",
	VOLUME = "70",
	NUMBER = "2--3",
	PAGES = "123--133",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/qgga_mlj1.pdf",
	YEAR = "2007" }
@article{mugkaz:scaffolds,
        AUTHOR = "K. Tsunoyama and A. Amini and M.J.E. Sternberg and
		S.H. Muggleton",
        TITLE = "Scaffold Hopping in Drug Discovery Using Inductive Logic
		Programming",
	JOURNAL = "Journal of Chemical Information and Modelling",
	VOLUME = "48",
	NUMBER = "5",
	PAGES = "949--957",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/Scaffolds.pdf",
	YEAR = "2008" }
@article{chenmugg:probexs,
        AUTHOR = "J. Chen and S.H. Muggleton and J. Santos",
        TITLE = "Learning probabilistic logic models from probabilistic
		examples",
	JOURNAL = "Machine Learning",
	VOLUME = "73",
	NUMBER = "1",
	PAGES = "55--85",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/probexs.pdf",
	YEAR = "2008" }
@article{dietmugg:strucml,
        AUTHOR = "T. Dietterich and P. Domingos and L. Getoor and
		S.H. Muggleton and P. Tadepalli",
        TITLE = " Structured machine learning: the next ten years",
	JOURNAL = "Machine Learning",
	VOLUME = "73",
	NUMBER = "1",
	PAGES = "3--23",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/strucml.pdf",
	YEAR = "2008" }
@article{bang:bayesnets,
        AUTHOR = "J-W. Bang and D.J. Crockford and E. Holmes and F. Pazos and
		M.J.E. Sternberg and S.H. Muggleton and J.K. Nicholson",
        TITLE = "Integrative top-down system metabolic modeling in
		experimental disease states via data-driven Bayesian methods",
	JOURNAL = "Journal of Proteome Research",
	VOLUME = "7",
	NUMBER = "2",
	PAGES = "497--503",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/BN-Metalog_JPR_08.pdf",
	YEAR = "2008" }
@article{alimugg:lattice,
        AUTHOR = "A. Tamaddoni-Nezhad and S.H. Muggleton",
        TITLE = "The lattice structure and refinement operators for the
		hypothesis space bounded by a bottom clause ",
	JOURNAL = "Machine Learning",
	VOLUME = "76",
	NUMBER = "1",
	PAGES = "37--72",
	NOTE = "10.1007/s10994-009-5117-7",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/lattref.pdf",
	YEAR = "2009" }
@article{lodhi:multisvilp,
        AUTHOR = "H. Lodhi and S.H. Muggleton and M.J.E. Sternberg",
        TITLE = "Multi-class protein fold recognition using large margin
		logic based divide and conquer learning",
	JOURNAL = "SIGKDD Exploration",
	VOLUME = "11",
	NUMBER = "2",
	PAGES = "117-122",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/multisvilp.pdf",
	YEAR = "2010" }
@article{lodhi:dssmulti,
        AUTHOR = "H. Lodhi and S.H. Muggleton and M.J.E. Sternberg",
        TITLE = "Multi-class mode of action classification of toxic compounds
		using logic based kernel methods",
	JOURNAL = "Molecular Informatics",
	PAGES = "655--664",
	YEAR = "2010" }
@article{mugg:ILPturns20,
        AUTHOR = "S.H. Muggleton and L. De Raedt and D. Poole and I. Bratko
		and P. Flach and K. Inoue",
        TITLE = "{ILP} turns 20: biography and future challenges",
	JOURNAL = "Machine Learning",
	VOLUME = "86",
	NUMBER = "1",
	PAGES = "3--23",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ILPturns20.pdf",
	YEAR = "2011" }
@article{lesk:wibl,
        AUTHOR = "V. Lesk and J. Taubert and C. Rawlings and S. Dunbarand S.H. Muggleton",
        TITLE = "{WIBL}: Workbench for Integrative Biological Learning",
	JOURNAL = "Journal of Integrative Bioinformatics",
	VOLUME = "8",
	NUMBER = "2",
	YEAR = "2011",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/wibl2011.pdf",
	DOI = "10.2390/biecoll-jib-2011-156"}
@article{mugg:ecoPlosOne,
        AUTHOR = "D.A. Bohan and G. Caron-Lormier and S.H. Muggleton and A. Raybould and A. Tamaddoni-Nezhad",
        TITLE = "Automated Discovery of Food Webs from Ecological Data using Logic-based Machine Learning",
	JOURNAL = "PloS ONE",
	VOLUME = "6",
	NUMBER = "12",
	YEAR = "2011",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ecoilp_plosone.pdf",
	DOI = "10.1371/journal.pone.0029028"}
@article{reynoldsmugs:inddex,
        AUTHOR = "C.R. Reynolds and S.H. Muggleton and M.J.E. Sternberg",
        TITLE = "Assessment of a rule-based virtual screening technology (INDDEx) on a benchmark data set",
	JOURNAL = "The Journal of Physical Chemistry B",
	VOLUME = "116",
	NUMBER = "23",
	YEAR = "2012",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/inddex.pdf"}
@article{santosmugg:hexose,
        AUTHOR = "J.C.A. Santos and H. Nassif and C.D. Page, S.H. Muggleton M.J.E. Sternberg ",
        TITLE = "Automated identification of protein-ligand interaction features using Inductive Logic Programming: A hexose binding case study",
	JOURNAL = "BMC Bioinformatics",
	VOLUME = "13",
	NUMBER = "162",
	YEAR = "2012",
	DOI = "10.1186/1471-2105-13-162",
	URL = "http://www.biomedcentral.com/1471-2105/13/162/abstract"}
@article{mugg:turingai,
        AUTHOR = "S. H. Muggleton",
        TITLE = "Alan {T}uring and the development of {A}rtificial {I}ntelligence",
	JOURNAL = "AI Communications",
	YEAR = "2014",
	VOLUME = "27",
	NUMBER = "1",
	PAGES= "3--10",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/TuringAI_1.pdf"}
@article{mugg:metalearn,
	AUTHOR = "S.H. Muggleton and D. Lin and N. Pahlavi and
		A. Tamaddoni-Nezhad",
	TITLE = "Meta-interpretive Learning: application to
		Grammatical Inference",
	YEAR = 2014,
	JOURNAL = "Machine Learning",
	VOLUME = "94",
	PAGES = "25--49",
	DOI = "10.1007/s10994-013-5358-3",
	URL = "https://link.springer.com/article/10.1007/s10994-013-5358-3" }
@article{alireza:aecr49,
        TITLE = {Construction and Validation of Food-webs using Logic-based
		Machine Learning and Text-mining},
        AUTHOR = {A. Tamaddoni-Nezhad and G. Afroozi Milani and A.
		Raybould and S. Muggleton and D.Bohan},
        YEAR = 2013,
        JOURNAL = {Advances in Ecological Research},
        VOLUME = 49,
	PAGES = "225--289"}
@article{mugetal:indprogreal,
	AUTHOR = "S. Gulwani and J. Hernandez-Orallo and E. Kitzelmann and
		S.H. Muggleton and U. Schmid and B. Zorn",
	TITLE = "Inductive Programming Meets the Real World",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/indprogreal.pdf",
	YEAR = 2015,
	JOURNAL = "Communications of the ACM",
	VOLUME = "58",
	NUMBER = "11",
	PAGES = "90--99"}
@article{mugg:metagolD:mlj,
	AUTHOR = "S.H. Muggleton and D. Lin and A. Tamaddoni-Nezhad",
	TITLE = "Meta-Interpretive Learning of Higher-Order Dyadic Datalog:
		Predicate Invention revisited",
	URL = "https://link.springer.com/article/10.1007/s10994-014-5471-y",
	YEAR = 2015,
	JOURNAL = "Machine Learning",
	VOLUME = "100",
	NUMBER = "1",
	PAGES = "49--73"}
@article{reynolds:virtual,
	AUTHOR = "C.R. Reynolds and S.H. Muggleton and M.J.E. Sternberg",
	TITLE = "Incorporating Virtual Reactions into a Logic-Based Ligand-Based Virtual Screening Method to Discover New Leads",
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/reynvirtual.pdf",
	YEAR = 2015,
	JOURNAL = "Molecular Informatics",
	NOTE = "DOI: 10.1002/minf.201400162" }
@article{mugg:logvismlj,
	AUTHOR = "S.H. Muggleton and W-Z. Dai and C. Sammut and
		A. Tamaddoni-Nezhad and J. Wen and Z-H. Zhou",
	TITLE = "Meta-Interpretive Learning from noisy images",
	URL = "http://link.springer.com/article/10.1007/s10994-018-5710-8",
	YEAR = 2018,
	JOURNAL = "Machine Learning",
	VOLUME = "107",
	ISSUE = "7",
	PAGES = "1097--1118" }
@article{mugg:compmlj,
	AUTHOR = "S.H. Muggleton and U. Schmid and C. Zeller and
		A. Tamaddoni-Nezhad and T. Besold",
	TITLE = "Ultra-Strong Machine Learning - Comprehensibility of
		Programs Learned with {ILP}",
	URL = "http://link.springer.com/article/10.1007/s10994-018-5707-3",
	YEAR = 2018,
	JOURNAL = "Machine Learning",
	VOLUME = "107",
	ISSUE = "7",
	PAGES = "1119--1140" }
@article{cropmugg:metaopt,
	AUTHOR = "A. Cropper and S.H. Muggleton",
	TITLE = "Learning efficient logic programs",
	YEAR = 2019,
	JOURNAL = "Machine Learning",
	VOLUME = "108",
	ISSUE = "7",
	PAGES = "1063--1083",
	URL = "http://link.springer.com/article/10.1007/s10994-018-5712-6"}
@article{mugghoq:newgengamestrat,
	AUTHOR = "S.H. Muggleton and C. Hocquette",
	TITLE = "Machine Discovery of Comprehensible Strategies
		for Simple Games Using Meta-Interpretive Learning",
	JOURNAL = "New Generation Computing",
	YEAR = 2019,
	VOLUME = "37",
	PAGES = "203--217",
	URL = "https://doi.org/10.1007/s00354-019-00054-2"}
@article{cropmug:learnholp,
	AUTHOR = "A. Cropper and R. Morel and S.H. Muggleton",
	TITLE = "Learning higher-order logic programs",
	JOURNAL = "Machine Learning",
	VOLUME = "109",
	PAGES = "1289--1322",
	YEAR = 2020,
	URL = "https://link.springer.com/article/10.1007/s10994-019-05862-7"}
@article{lunmug:mljharmexp,
	AUTHOR = "L. Ai and S.H. Muggleton and C. Hocquette and M. Gromowski
		and U. Schmid",
	TITLE = "Beneficial and Harmful Explanatory Machine Learning",
	JOURNAL = "Machine Learning",
	VOLUME = "110",
	PAGES = "695-721",
	YEAR = 2021,
	URL = "https://link.springer.com/article/10.1007/s10994-020-05941-0"}
@article{lunmug:bmlp,
	AUTHOR = "L. Ai and S.H. Muggleton and S-S. Liang and G. Baldwin",
	TITLE = "Boolean matrix logic programming for active learning
		of gene functions in genome-scale metabolic network models",
	JOURNAL = "Machine Learning",
	VOLUME = "114,254",
	YEAR = 2025,
	URL = "https://doi.org/10.1007/s10994-025-06868-0"}
@article{patmug:topprog,
	AUTHOR = "S. Patsantzis and S.H. Muggleton",
	TITLE = "Top Program Construction and Reduction for polynomial
		time Meta-Interpretive Learning",
	JOURNAL = "Machine Learning",
	VOLUME = 110,
	PAGES = "755-778",
	YEAR = 2021,
	URL = "https://link.springer.com/article/10.1007/s10994-020-05945-w" }
@article{cropmug:ilp30mlj,
	AUTHOR = "A. Cropper and S. Duman\u{c}i\'{c} and Richard Evans and
		S.H. Muggleton",
	TITLE = "{I}nductive {L}ogic {P}rogramming at 30",
	JOURNAL = "Machine Learning",
	VOLUME = "111",
	PAGES = "147--172",
	YEAR = 2021,
	URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ilp30.pdf" }
@article{patmug:metaspec,
	AUTHOR = "S. Patsantzis and S.H. Muggleton",
	TITLE = "Meta-Interpretive Learning as Metarule Specialisation",
	JOURNAL = "Machine Learning",
	VOLUME = "111",
	PAGES = "3703--3731",
	YEAR = 2022,
	URL = "https://link.springer.com/article/10.1007/s10994-022-06156-1"}
@article{mugg:hypalg1ex,
	AUTHOR = "S.H. Muggleton",
	TITLE = "Hypothesising an Algorithm from One Example: the Role of Specificity",
	JOURNAL = "Philosophical Transaction of the Royal Society A",
	YEAR = 2023,
	VOLUME = "381:20220046",
	URL = "https://royalsocietypublishing.org/doi/10.1098/rsta.2022.0046"}
@article{lunmug:SeqTeach,
	AUTHOR = "L. Ai and J. Langer and S.H.  Muggleton and
		U. Schmid",
	TITLE = "Explanatory machine learning for sequential human teaching",
	JOURNAL = "Machine Learning",
	YEAR = 2023,
	VOLUME = 112,
	PAGES = "3591--3632",
	URL = "https://link.springer.com/article/10.1007/s10994-023-06351-8" }