(1) King, R.D. (1987) An inductive learning approach to the problem of predicting a protein's secondary structure from its amino acid sequence. In: Progress in Machine Learning (eds. I. Bratko and N. L. Lavrac) Sigma Press. Wimslow England 230-250. 

(2)  King, R.D. (1990) Experiments in machine learning and protein folding. In: A.A.A.I. Spring Symposium on AI and Molecular Biology , Stanford, California. 

(3) King, R.D. & Sternberg, M.J.E. (1990) Machine learning approach for the prediction of protein secondary structure. J. Mol. Biol. , 216, 441-457.

(4) King, R.D. (1991) PROMIS: Experiments in machine learning and protein folding. In: Machine Intelligence 12 (eds. J.E. Hayes, D. Michie, & E. Tyugu). Oxford University Press, Oxford. 291-310.

(5) Schulze-Kremer, S. & King, R.D. (1991) IPSA - Inductive protein structure analysis. In: A.A.A.I. Workshop on A.I. Approaches to Classification and Pattern Recognition in Molecular Biology. Anaheim, California.

(6) Muggleton, S., King, R.D., & Sternberg, M.J.E. (1992) Using logic for protein structure prediction. In: Proceedings of the 25th Hawaii International Conference on System Sciences. IEEE Computer Society Press, Los Alamitos.

(7) Schulze-Kremer, S. & King, R.D. (1992) IPSA - Inductive protein structure analysis. Protein Engineering 5, 377-390.

(8) Muggleton, S., King, R.D., & Sternberg, M.J.E. (1992) Protein secondary structure prediction using logic. Protein Engineering. 5. 647-657.

(9) Sternberg, M.J.E., Lewis, R.A., King, R.D., & Muggleton, S. (1992) Modelling the structure and function of enzymes by machine learning. The Royal Society of Chemistry: Faraday Discussion No: 93. 269-280.

(10) King, R.D., Muggleton, S., Lewis R.A., & Sternberg, M.J.E. (1992) Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proc. Nat. Acad. Sci. U.S.A. 89, 11322-11326.

(11) Sutherland, A., Henery, R., Molina, R., Taylor, C.C., & King, R.D. (1992) Statistical methods in learning. In: IPMU'92-Advanced methods in Artificial Intelligence (eds. Bouchon-Meunier, Valverde, & Yager) Lecture Notes in Computer Science 682, Springer Verlag, Berlin, 173-182.

(12) King, R.D., Muggleton, S., Srinivasan, A., Feng, C., Lewis, R.A., & Sternberg, M.J.E. (1993) Drug design using inductive logic programming. Proceedings of the 26th Hawaii International Conference on System Sciences. IEEE Computer Society Press, Los Alamitos, 646-655.

(13) Feng, C., Sutherland, A., King, R.D., Muggleton, S. & Henery, R. (1993) Comparing machine learning classifiers to statistics and neural networks In: Proceedings of International Conference on Artificial Intelligence and Statistics. Society of A.I. and Statistics and IASC. Florida.

(14) King, R.D., Hirst, J.D., & Sternberg, M.J.E. (1993) New approaches to QSAR: Neural networks and machine learning. Perspectives in Drug Discovery and Design. 1. 279-290.

(15) King, R.D., Henery, R. & Sutherland, A. (1994) A Comparative study of classification algorithms: Statistical, machine learning, and neural network Machine Intelligence 13. (eds. K. Furukawa, D. Michie, & S. Muggleton) Oxford University Press, Oxford. 311-359.

(16) Sternberg, M.J.E., Lewis, R.A., King, R.D., & Muggleton, S. (1994) Machine learning and biomolecular modelling. Machine Intelligence 13. Oxford University Press, Oxford. 193-212.

(17) Bratko, I., & King, R.D. (1994) Applications of inductive logic programming. SIGART Bulletin. 5. 43-49.

(18) Sternberg, M.J.E., King, R.D., Lewis, R.A., & Muggleton, S. (1994) Application of machine learning to structural molecular biology. Phil. Trans. R. Soc. Lond. B. 344. 365-371.

(19) King, R.D., Clark, D.A., Shirazi, J., & Sternberg, M.J.E. (1994) Inductive logic programming used to discover topological constraints in protein structures. In: Second International Conference on Intelligent Systems for Molecular Biology. A.A.A.I. Press, Menlo Park. 219-226.

(20) Hirst, J.D., King, R.D., & Sternberg, M.J.E. (1994) Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines. Journal of Computer-Aided Molecular Design. 8, 405-420.

(21) Hirst, J.D., King, R.D., & Sternberg, M.J.E. (1994) Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines. Journal of Computer-Aided Molecular Design. 8, 421-432.

(22) King, R.D., Clark, D.A., Shirazi, J., & Sternberg, M.J.E. (1994) On the use of machine learning to identify topological rules in the packing of beta-strands. Protein Engineering. 7, 1295-1303.

(23) Srinivasan, A., Muggleton, S., King, R.D., & Sternberg, M.J.E. (1994) Mutagenesis: ILP experiments in a non-determinate biological domain. In: Proceedings of the Inductive Logic Programming 1994 Workshop.

(24) King, R.D., Sternberg, M.J.E., Srinivasan, A., and Muggleton, S.H. (1995) Knowledge discovery in a database of mutagenic chemicals. In: Proceedings of the MLnet Familiarization Workshop: Statistics, Machine Learning, and Knowledge Discovery. (eds. Y. Kodratoff, R. Nakaeizadeh, C. Taylor).

(25) King, R.D., Hirst, J.D., Sternberg, M.J.E. (1995) A comparison of artificial intelligence methods for modelling pharmaceutical QSARs. Applied Artificial Intelligence. 9, 213-234.

(26) King, R.D., Feng, C., & Sutherland, A. (1995) StatLog: Comparison of classification algorithms on large real-world problems. Applied Artificial Intelligence. 9, 289-333.postscript

(27) King, R.D., Srinivasan, A., & Sternberg, M.J.E. (1995) Relating chemical activity to structure: an examination of ILP successes. New Gen. Computing. 13, 411-433

(28) King, R.D., Muggleton, S.H., Srinivasan, A., & Sternberg, M.J.E. (1996) Structure activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity using inductive logic programming. Proc. Nat. Acad. Sci. U.S.A. 93, 438-442.

(29) King, R.D., Clark, D.A., Shirazi, J., & Sternberg, M.J.E. (1996) Discovery of protein structural constraints in a deductive database using inductive logic programming. In: Machine Intelligence 14. (eds. K. Furukawa, D. Michie, & S. Muggleton) Oxford University Press, Oxford. 271-298.

(30) King, R.D. & Angus, C,G. (1996) PM: Protein Music. CABIOS. 12, 251-252.

(31) Srinivasan, A., Sternberg, M.J.E., & King, R.D. (1996) Theories for mutagenicity: a study of first-order and feature based induction. A.I. Journal. 85, 277-299.

(32) King, R.D. & Sternberg, M.J.E. (1996) Identification and application of the concepts important for accurate and reliable protein secondary structure prediction Protein Science. 5, 2298-2310.

(33) King, R.D. & Srinivasan, A., (1996) Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming Environmental Health Perspectives. 104 (supplement 5) 1031-1040.

(34) Srinivasan, A. & King, R.D. (1997) Feature construction with Inductive Logic Programming: a study of quantitative predictions of biological activity aided by structural attributes In: Proceedings of 6th Inductive Logic Programming Workshop. , Lecture Notes in Artificial Intelligence 1314, Springer Verlag, Berlin.

(35) Srinivasan, A. King, R.D., Muggleton, S.H. & Sternberg, M.J.E. (1997) Carcinogenesis predictions using ILP In: Proceedings of 7th Inductive Logic Programming Workshop. , Lecture Notes in Artificial Intelligence 1297, Springer Verlag, Berlin, 273-287.

(36) King, R.D., Saqi, M., Sayle, R., & Sternberg, M.J.E. (1997) DSC: public domain protein secondary structure prediction CABIOS. 13, 473-474.postscript

(37) Srinivasan, A., King, R.D., Muggleton, S.H. & Sternberg, M.J.E. (1997) The predictive toxicology evaluation challenge In: Fifteenth International Joint Conference on Artificial Intelligence. , Morgan Kaufmann, San Francisco, 4-9.postscript

(38) King, R.D., & Srinivasan, A. (1997) The discovery of indicator variables for QSAR using inductive logic programming Journal of Computer-Aided Molecular Design. 11, 571-580.

(39) Dehaspe, L., Toivonen, H., & King, R.D. (1998) Finding frequent substructures in chemical compounds. In: The Fourth International Conference on Knowledge Discovery and Data Mining. R. Agrawal, P. Stolorez, & G. Piatetsky (eds.) AAAI Press, Menlo Park. 30-36.
Voted best paper in applications category (247 papers submitted).

(40) Dehaspe, L., Toivonen, H., & King. R.D. (1988) Finding frequent substructures in chemical compounds. In: Proceedings of the 8th Belgian-Dutch conference on machine learning (BENELEARN-98) F.Verdenius, W. van den Broek (eds.): p. 21-29. Technical report 352, ATO-DLO, Wageningen (Holland).

(41) Muggleton, S., Srinivasan, A., King, R.D., & Sternberg, M.J.E. (1998) Biochemical knowledge discovery using inductive logic programming In: Discovery Science 98 326-341.postscript

(42) Srinivasan, A. & King, R. D. (1999) Using Inductive Logic Programming to construct Structure-Activity Relationships In: Predictive toxicology of chemical: Experiences and impact of AI tools (Papers from the 1999 AAAI Spring Symposium). G.C. Gini & A.R. Katrizky (eds.) AAAI Press, Menlo Park. 64-73.postscript

(43) Dehaspe, L., Toivonen, H. & King, R. D. (1999) Finding frequent substructures in chemical compounds. In: Predictive toxicology of chemical: Experiences and impact of AI tools (Papers from the 1999 AAAI Spring Symposium). G.C. Gini & A.R. Katrizky (eds.) AAAI Press, Menlo Park. 78-81.

(44) Srinivasan, A., & King, R.D. (1999) Feature construction with inductive logic programming: a study of quantitative predictions of biological activity aided by structural attributes. Knowledge Discovery and Data Mining Journal 3, 37-57.

(45) Srinivasan, A., King, R.D., & Bristol, D. (1999) An assessment of submissions made to the predictive toxicology challenge. In: Sixteenth International Joint Conference on Artificial Intelligence. (ed. T. Dean) Morgan Kaufmann, San Francisco CA. pp 270-275.postscript

(46) Srinivasan, A., King, R.D., & Bristol, D. (1999) An assessment of ILP-assisted models for toxicology and the PTE-3 experiment. In: Proceedings of 9th Inductive Logic Programming Workshop. (eds. S. Dzeroski and P.A. Flach) Berlin, Springer 291-302.postscript

(47) King, R.D., Ouali, M., Strong, A.T., Aly, A., Elmaghraby, A., Kantardzic, M., & Page, D. (2000) Is it better to combine predictions? Protein Engineering 13, 15-19.postscript

(48) Kell, D., & King, R.D. (2000) On the optimization of classes for the assignment of unidentified reading frames in functional genomics programmes: the need for machine learning. Trends in Biotechnology 18, 93-98.postscript

(49) Sternberg, M.J.E., King, R.D., Srinivasan, A., & Muggleton, S.H. (2000) Drug design by machine learning. In: Machine Intelligence 15 (eds. K. Furukawa, D. Michie, S. Muggleton) Oxford University Press, Oxford. pp 328-338.

(50) Ouali, M., & King, R.D. (2000) Cascaded multiple classifiers for secondary structure prediction. Prot. Sci 9, 1162-1176.postscript

(51) King, R.D., Page, D., & Ouali, M. (2000) Combining Legacy Prediction Systems in Bioinformatics. In: The Fith International Workshop on Multistrategy Learning (MSL 2000) R.S Michalski & P.B. Brazdil (eds.) pp 77-90.postscript

(52) King, R.D., Karwath, A., Clare, A., & Dehaspe, L. (2000) Genome scale prediction of protein functional class from sequence using data mining. In: The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. R. Ramakrishnan, S. Stolfo, R. Bayardo, & I Parsa (eds.) The Association for Computing Machinery, New York, USA. pp. 384-389.pdf

(53) King, R.D., Karwath, A., Clare, A., & Dehaspe, L. (2000) Accurate prediction of protein functional class in the M. tuberculosis and E. coli genomes using data mining.  Yeast (Comparative and Functional Genomics) 17 283-293postscript

(54) Alsberg, B.K., Marchand-Geneste, N., & King, R.D., (2001) A new 3D molecular structure representation based on quantum topology with application to structure-property relationships. Chemometrics and Intelligent Laboratory Systems 54, 75-91

(55) King, R.D., Srinivasan, A., & Dehaspe, L. (2001) Warmr: A Data Mining Tool for Chemical Data.Journal of Computer-Aided Molecular Design. 15, 173-181. postscript

(56) Helma, C., King, R.D., Kramer, S., & Srinivasan, A. (2001) The predictive toxicology challenge 2000-2001. Bioinformatics. 17, 107-108.

(57) King, R.D., Karwath, A., Clare, A., & Dehaspe, L. (2001) The Utility of Different Representations of Protein Sequence for Predicting Functional Class.Bioinformatics 17, 445-454.pdf

(58) Clare, A.J. & King, R.D. (2001) Knowledge discovery in multi-label phenotype data. In: The 5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'01) (Eds. L. De Raedt, A. Siebes) Lecture Notes in A.I. 2168 Sringer-Verlag, Heidelberg.postscript

(59) Karwath, A. & King, R.D.(2001) An automated ILP server in the field of bioinformatics . In: The Eleventh International Conference on Inductive Logic Programming (ILP'01). (Eds. C. Rouveirol, M. Sebag) Lecture Notes in A.I. 2157 Sringer-Verlag, Heidelberg.postscript

(60) Alsberg, B.K., Marchand-Geneste, N., & King, R.D. (2001) Modeling quantitative structure-property relationships in calculated reaction pathways using a three-dimensional quantum topological representation. Analytica Chimica Acta (in press).

(61) Clare, A.J. & King, R.D.(2001) Machine learning of functional class from phenotype data. Bioinformatics (in press) postscript

(62) Bryant, C.H., Muggleton, S.H., Oliver, S.G., Kell, D.B., Reiser, P., & King, R.D. (2001) Combining inductive logic programming, active learning, and robotics to discover the function of genes. Electronic Transactionsi in Artificial Intelligence (in press). postscript

(63) King R. D., Marchand-Geneste, N., & Alsberg, B.K. (2001) A quantum mechanics based representation of molecules for machine inference. Electronic Transactionsi in Artificial Intelligence (in press).postscript

(64) Reiser, P.K., King, R.D., Kell, D.B., Muggleton, S.H., Bryant, C.H. & Oliver, S.G (2001) Developing a logical model of yeast metabolism. Electronic Transactionsi in Artificial Intelligence (in press).pdf

(65) Marchand-Geneste, N., Watson, K.A., Alsberg, B., King, R.D. (2001) A new approach to pharmacophore mapping and QSAR analysis using Inductive Logic Programming. Application to thermolysin inhibitors and glycogen phosphorylase b inhibitors. J. Med. Chem. (in press).postscript
 
 

(1) Sternberg, M.J.E., Hirst, J.D., Lewis, R.A., King, R.D., Srinivasan, A., & Muggleton, S. (1994) Application of machine learning to protein structure prediction and drug design. In: Advances in Molecular Bioinformatics. (ed. S. Schulze-Kremer) IOS Press, Amsterdam.

(2) King, R.D. (1996) Secondary structure prediction In: Protein Structure Prediction - A Practical Approach. (ed. M.J.E. Sternberg) Oxford University Press Oxford.

(3) King, R.D. (1998) Applications of machine learning in drug design In: Structure-Based Drug Design: Experimental and Computational Approaches P.W. Codding (ed.) Kluwer Academic Publishers, Dordrecht.

(4) King, R.D., Sternberg M.J.E., Muggleton, S.H. & Srinivasan, A.(1998) Recent developments in applying machine learning to drug design In: Structure-Based Drug Design: Experimental and Computational Approaches P.W. Codding (ed.) Kluwer Academic Publishers, Dordrecht.

(5) Garrett, S.M., Coghill, G.M., Srinivasan, A. & King, R. D. (2001) On learning qualitative models from qualitative and real-valued data. In: Computational Discovery of Communicable Knowledge (ed. S. Dzeroski) (in press).

(6) King, R.D., Karwath, A., Clare A., Dehaspe, L. (2001) Logic and the Automatic Acquisition of Scientific Knowledge. In: Computational Discovery of Communicable Knowledge (ed. S. Dzeroski) (in press).