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- L. Ai, J. Langer, S.H.
Muggleton, and U. Schmid.
Explanatory
machine learning for sequential human teaching.
Machine Learning, 112:3591-3632, 2023.
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- S.H. Muggleton.
Hypothesising
an algorithm from one example: the role of specificity.
Philosophical Transaction of the Royal Society A, 381:20220046,
2023.
- [3]
- S. Patsantzis and S.H. Muggleton.
Meta-interpretive
learning as metarule specialisation.
Machine Learning, 111:3703-3731, 2022.
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- L. Ai, S.H. Muggleton,
C. Hocquette, M. Gromowski, and U. Schmid.
Beneficial
and harmful explanatory machine learning.
Machine Learning, 110:695-721, 2021.
- [5]
- A. Cropper,
S. Dumancic, Richard Evans, and S.H. Muggleton.
Inductive Logic
Programming at 30.
Machine Learning, 111:147-172, 2021.
- [6]
- S. Patsantzis and S.H. Muggleton.
Top
program construction and reduction for polynomial time meta-interpretive
learning.
Machine Learning, 110:755-778, 2021.
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- A. Cropper,
R. Morel, and S.H. Muggleton.
Learning
higher-order logic programs.
Machine Learning, 109:1289-1322, 2020.
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- A. Cropper
and S.H. Muggleton.
Learning
efficient logic programs.
Machine Learning, 108:1063-1083, 2019.
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- S.H. Muggleton and C. Hocquette.
Machine discovery of
comprehensible strategies for simple games using meta-interpretive
learning.
New Generation Computing, 37:203-217, 2019.
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- S.H. Muggleton,
W-Z. Dai, C. Sammut, A. Tamaddoni-Nezhad, J. Wen, and Z-H. Zhou.
Meta-interpretive
learning from noisy images.
Machine Learning, 107:1097-1118, 2018.
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- S.H. Muggleton,
U. Schmid, C. Zeller, A. Tamaddoni-Nezhad, and T. Besold.
Ultra-strong
machine learning - comprehensibility of programs learned with ILP.
Machine Learning, 107:1119-1140, 2018.
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- S. Gulwani,
J. Hernandez-Orallo, E. Kitzelmann, S.H. Muggleton, U. Schmid, and B. Zorn.
Inductive
programming meets the real world.
Communications of the ACM, 58(11):90-99, 2015.
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- S.H. Muggleton,
D. Lin, and A. Tamaddoni-Nezhad.
Meta-interpretive
learning of higher-order dyadic datalog: Predicate invention revisited.
Machine Learning, 100(1):49-73, 2015.
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- C.R. Reynolds,
S.H. Muggleton, and M.J.E. Sternberg.
Incorporating
virtual reactions into a logic-based ligand-based virtual screening method to
discover new leads.
Molecular Informatics, 2015.
DOI: 10.1002/minf.201400162.
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- S. H. Muggleton.
Alan Turing and
the development of Artificial Intelligence.
AI Communications, 27(1):3-10, 2014.
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- S.H. Muggleton,
D. Lin, N. Pahlavi, and A. Tamaddoni-Nezhad.
Meta-interpretive
learning: application to grammatical inference.
Machine Learning, 94:25-49, 2014.
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- M.J.E. Sternberg,
A. Tamaddoni-Nezhad, V.I.. Lesk, , E. Kay, P.G. Hitchen, A. Cootes, L.B.
Alphen, M.P. Lamoureux, H.C. Jarrell, C.J. Rawlings, E.C. Soo, C.M.
Szymanski, A. Dell, B.W. Wren, and S.H. Muggleton.
Gene function
hypotheses for the campylobacter jejuni glycome generated by a logic-based
approach.
Journal of Moleular Biology, 425(1):186-197, 2013.
- [18]
- A. Tamaddoni-Nezhad, G. Afroozi Milani, A. Raybould,
S. Muggleton, and D.Bohan.
Construction and validation of food-webs using logic-based machine learning and
text-mining.
Advances in Ecological Research, 49:225-289, 2013.
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- C.R. Reynolds,
S.H. Muggleton, and M.J.E. Sternberg.
Assessment of a
rule-based virtual screening technology (inddex) on a benchmark data set.
The Journal of Physical Chemistry B, 116(23), 2012.
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- J.C.A. Santos,
H. Nassif, and S.H. Muggleton M.J.E. Sternberg C.D. Page.
Automated
identification of protein-ligand interaction features using inductive logic
programming: A hexose binding case study.
BMC Bioinformatics, 13(162), 2012.
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- D.A. Bohan,
G. Caron-Lormier, S.H. Muggleton, A. Raybould, and A. Tamaddoni-Nezhad.
Automated
discovery of food webs from ecological data using logic-based machine
learning.
PloS ONE, 6(12), 2011.
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- V. Lesk, J. Taubert,
C. Rawlings, and S. Dunbarand S.H. Muggleton.
WIBL: Workbench
for integrative biological learning.
Journal of Integrative Bioinformatics, 8(2), 2011.
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- S.H. Muggleton,
L. De Raedt, D. Poole, I. Bratko, P. Flach, and K. Inoue.
ILP turns 20:
biography and future challenges.
Machine Learning, 86(1):3-23, 2011.
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- H. Lodhi, S.H.
Muggleton, and M.J.E. Sternberg.
Multi-class mode of action classification of toxic compounds using logic based
kernel methods.
Molecular Informatics, pages 655-664, 2010.
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- H. Lodhi, S.H.
Muggleton, and M.J.E. Sternberg.
Multi-class
protein fold recognition using large margin logic based divide and conquer
learning.
SIGKDD Exploration, 11(2):117-122, 2010.
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- A. Tamaddoni-Nezhad and S.H. Muggleton.
The lattice
structure and refinement operators for the hypothesis space bounded by a
bottom clause.
Machine Learning, 76(1):37-72, 2009.
10.1007/s10994-009-5117-7.
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- J-W. Bang, D.J.
Crockford, E. Holmes, F. Pazos, M.J.E. Sternberg, S.H. Muggleton, and J.K.
Nicholson.
Integrative
top-down system metabolic modeling in experimental disease states via
data-driven bayesian methods.
Journal of Proteome Research, 7(2):497-503, 2008.
- [28]
- J. Chen, S.H.
Muggleton, and J. Santos.
Learning
probabilistic logic models from probabilistic examples.
Machine Learning, 73(1):55-85, 2008.
- [29]
- T. Dietterich,
P. Domingos, L. Getoor, S.H. Muggleton, and P. Tadepalli.
Structured machine
learning: the next ten years.
Machine Learning, 73(1):3-23, 2008.
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- K. Tsunoyama,
A. Amini, M.J.E. Sternberg, and S.H. Muggleton.
Scaffold hopping
in drug discovery using inductive logic programming.
Journal of Chemical Information and Modelling, 48(5):949-957,
2008.
- [31]
- A. Amini, H. Lodhi
S.H. Muggleton, and M.J.E. Sternberg.
A novel
logic-based approach for quantitative toxicology prediction.
Journal of Chemical Information and Modelling, 47(3):998-1006,
2007.
- [32]
- A. Amini, P.J.
Shrimpton, S.H. Muggleton, and M.J.E. Sternberg.
A general
approach for developing system-specific functions to score protein-ligand
docked complexes using support vector inductive logic programming.
Proteins, 69(4):823-831, 2007.
DOI: 10.1002/prot.21782.
- [33]
- E.O. Cannon,
A. Amini, A. Bender, M. J. E. Sternberg, S.H. Muggleton, R.C. Glen, and
J.B.O. Mitchell.
Support vector
inductive logic programming outperforms the naive Bayes classifier and
inductive logic programming for the classification of bioactive chemical
compounds.
Journal of Computer Aided Molecular Design, 21:269-280, 2007.
- [34]
- A.P. Cootes, S.H.
Muggleton, and M.J.E. Sternberg.
The identification
of similarities between biological networks: Application to the metabolome
and interactome prediction.
Journal of Molecular Biology, 369(4):1126-1139, 2007.
DOI: 10.1016/j.jmb.2007.03.013.
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- S.H.
Muggleton and A. Tamaddoni-Nezhad.
QG/GA: A
stochastic search for Progol.
Machine Learning, 70(2-3):123-133, 2007.
- [36]
- A. Tamaddoni-Nezhad, R. Chaleil, A. Kakas, M.J.E. Sternberg,
J. Nicholson, and S.H. Muggleton.
Modeling the
effects of toxins in metabolic networks.
IEEE Engineering in Medicine and Biology, 26:37-46, 2007.
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- S. Colton and
S.H. Muggleton.
Mathematical
applications of inductive logic programming.
Machine Learning, 64:25-64, 2006.
DOI: 10.1007/s10994-006-8259-x.
- [38]
- S.H. Muggleton.
Exceeding
human limits.
Nature, 440(7083):409-410, 2006.
- [39]
- A. Tamaddoni-Nezhad, R. Chaleil, A. Kakas, and S.H. Muggleton.
Application of
abductive ILP to learning metabolic network inhibition from temporal
data.
Machine Learning, 64:209-230, 2006.
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- R.D. King, K.E. Whelan,
F.M. Jones, P.K.G. Reiser, C.H. Bryant, S.H. Muggleton, D.B. Kell, and S.G.
Oliver.
Functional
genomic hypothesis generation and experimentation by a robot scientist.
Nature, 427:247-252, 2004.
- [41]
- A. Cootes, S.H.
Muggleton, and M.J.E. Sternberg.
The automatic
discovery of structural principles describing protein fold space.
Journal of Molecular Biology, 330(4):839-850, 2003.
- [42]
- M.J.E.
Sternberg and S.H. Muggleton.
Structure activity
relationships (SAR) and pharmacophore discovery using inductive logic
programming (ILP).
QSAR and Combinatorial Science, 22(5):527-532, 2003.
- [43]
- N. Angelopoulos and S.H. Muggleton.
Machine
learning metabolic pathway descriptions using a probabilistic relational
representation.
Electronic Transactions in Artificial Intelligence, 6, 2002.
- [44]
- S.H. Muggleton.
Learning
structure and parameters of stochastic logic programs.
Electronic Transactions in Artificial Intelligence, 6, 2002.
- [45]
- H. Watanabe and S.H. Muggleton.
First-order
stochastic action languages.
Electronic Transactions in Artificial Intelligence, 6, 2002.
- [46]
- C.H. Bryant, S.H.
Muggleton, S.G. Oliver, D.B. Kell, P. Reiser, and R.D. King.
Combining
inductive logic programming, active learning and robotics to discover the
function of genes.
Electronic Transactions in Artificial Intelligence,
5-B1(012):1-36, November 2001.
- [47]
- A.P. Cootes, S.H.
Muggleton, R.B. Greaves, and M.J. Sternberg.
Automatic
determination of protein fold signatures from structured superposition.
Electronic Transactions in Artificial Intelligence,
6-B2(026):245-274, November 2001.
- [48]
- S.H. Muggleton.
Statistical aspects
of logic-based machine learning.
ACM Transactions on Computational Logic, 2001.
Under revision.
- [49]
- S.H. Muggleton.
Stochastic logic
programs.
Journal of Logic Programming, 2001.
Accepted subject to revision.
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- S.H. Muggleton, C.H.
Bryant, A. Srinivasan, A. Whittaker, S. Topp, and C. Rawlings.
Are grammatical
representations useful for learning from biological sequence data? - a case
study.
Journal of Computational Biology, 8(5):493-521, 2001.
- [51]
- P.G.K. Reiser, R.D.
King, D.B. Kell, S.H. Muggleton, C.H. Bryant, and S.G. Oliver.
Developing a
logical model of yeast metabolism.
Electronic Transactions in Artificial Intelligence,
5-B2(024):223-244, November 2001.
- [52]
- M. Turcotte,
S.H. Muggleton, and M.J. Sternberg.
Generating protein three-dimensional fold signatures using inductive logic
programming.
Computational Chemistry, 26:57-64, 2001.
- [53]
- M. Turcotte, S.H.
Muggleton, and M.J.E. Sternberg.
Automated discovery
of structural signatures of protein fold and function.
Journal of Molecular Biology, 306:591-605, 2001.
- [54]
- M. Turcotte, S.H.
Muggleton, and M.J.E. Sternberg.
The effect of
relational background knowledge on learning of protein three-dimensional fold
signatures.
Machine Learning, 1,2:81-96, April-May 2001.
- [55]
- S.H. Muggleton.
Learning
stochastic logic programs.
Electronic Transactions in Artificial Intelligence, 4(041),
2000.
- [56]
- S.H. Muggleton.
Inductive logic
programming: issues, results and the LLL challenge.
Artificial Intelligence, 114(1-2):283-296, December 1999.
- [57]
- S.H. Muggleton.
Scientific knowledge
discovery using Inductive Logic Programming.
Communications of the ACM, 42(11):42-46, November 1999.
- [58]
- P. Finn, S.H. Muggleton, D. Page, and A. Srinivasan.
Pharmacophore
discovery using the Inductive Logic Programming system Progol.
Machine Learning, 30:241-271, 1998.
- [59]
- R.D. King, S.H. Muggleton,
A. Srinivasan, and M.J.E. Sternberg.
Structure-activity
relationships derived by machine learning: the use of atoms and their bond
connectives to predict mutagenicity by inductive logic programming.
Proceedings of the National Academy of Sciences, 93:438-442,
1996.
- [60]
- S.H. Muggleton and
D. Michie.
Machine intelligibility
and the duality principle.
British Telecom Technology Journal, 14(4):15-23, 1996.
- [61]
- A. Srinivasan,
S.H. Muggleton, R.D. King, and M.J.E. Sternberg.
Theories for
mutagenicity: a study of first-order and feature based induction.
Artificial Intelligence, 85(1,2):277-299, 1996.
- [62]
- I. Bratko and
S.H. Muggleton.
Applications of
Inductive Logic Programming.
Communications of the ACM, 38(11):65-70, 1995.
- [63]
- S.H. Muggleton.
Inverse entailment
and Progol.
New Generation Computing, 13:245-286, 1995.
- [64]
- S.H. Muggleton.
Inductive logic
programming: derivations, successes and shortcomings.
SIGART Bulletin, 5(1):5-11, 1994.
- [65]
- S.H. Muggleton.
Predicate invention and
utilisation.
Journal of Experimental and Theoretical Artificial Intelligence,
6(1):127-130, 1994.
- [66]
- S.H. Muggleton and
L. De Raedt.
Inductive logic
programming: Theory and methods.
Journal of Logic Programming, 19,20:629-679, 1994.
- [67]
- M.J.E. Sternberg,
R.D. King, R. Lewis, and S.H. Muggleton.
Application of machine learning to structural molecular biology.
Philosophical Transactions of the Royal Society B, 344:365-371,
1994.
- [68]
- R.D. King, S.H.
Muggleton, R. Lewis, and M.J.E. Sternberg.
Drug design by
machine learning: The use of inductive logic programming to model the
structure-activity relationships of trimethoprim analogues binding to
dihydrofolate reductase.
Proceedings of the National Academy of Sciences,
89(23):11322-11326, 1992.
- [69]
- S.H. Muggleton,
R.D. King, and M.J.E. Sternberg.
Protein secondary
structure prediction using logic-based machine learning.
Protein Engineering, 5(7):647-657, 1992.
- [70]
- M.J.E. Sternberg,
R. Lewis, R.D. King, and S.H. Muggleton.
Modelling the structure and function of enzymes by machine learning.
Proceedings of the Royal Society of Chemistry: Faraday
Discussions, 93:269-280, 1992.
- [71]
- S.H. Muggleton.
Inductive Logic
Programming.
New Generation Computing, 8(4):295-318, 1991.