[1]
L. Ai, J. Langer, S.H. Muggleton, and U. Schmid. Explanatory machine learning for sequential human teaching. Machine Learning, 112:3591-3632, 2023.

[2]
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.

[4]
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.

[7]
A. Cropper, R. Morel, and S.H. Muggleton. Learning higher-order logic programs. Machine Learning, 109:1289-1322, 2020.

[8]
A. Cropper and S.H. Muggleton. Learning efficient logic programs. Machine Learning, 108:1063-1083, 2019.

[9]
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.

[10]
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.

[11]
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.

[12]
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.

[13]
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.

[14]
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.

[15]
S. H. Muggleton. Alan Turing and the development of Artificial Intelligence. AI Communications, 27(1):3-10, 2014.

[16]
S.H. Muggleton, D. Lin, N. Pahlavi, and A. Tamaddoni-Nezhad. Meta-interpretive learning: application to grammatical inference. Machine Learning, 94:25-49, 2014.

[17]
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.

[19]
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.

[20]
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.

[21]
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.

[22]
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.

[23]
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.

[24]
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.

[25]
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.

[26]
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.

[27]
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.

[30]
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.

[35]
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.

[37]
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.

[40]
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.

[50]
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.