Machine learning for systems biology

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3625, Issue , 2005, Pages 416-423

  Muggleton, S H ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGY; GENES; MATHEMATICAL MODELS; TOXIC MATERIALS; VECTORS;

BIOCHEMICAL PATHWAYS; SYSTEMS BIOLOGY;

LEARNING SYSTEMS;

EID: 26944473632     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11536314_27     Document Type: Conference Paper

References (15)

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2. S. Goto, Y. Okuno, M. Hattori, T. Nishioka, and M. Kanehisa. Ligand: database of chemical compounds and reactions in biological pathways. Nucleic Acids Research, 30:402-404, 2002.
3. T.R. Hughes, M.J. Marton, A.R. Jones, C.J. Roberts, R. Stoughton, C.D. Armour, H.A.Bennett, E. Coffey, H. Dai, Y.D. He, M.J. Kidd, A.M. King, M.R. Meyer, D. Slade, P.Y. Lum, S.B. Stepaniants, D.D. Shoemaker, D. Gachotte, K. Chakraburtty, J. Simon, M. Bard, and S.H. Friend. Functional discovery via a compendium of expression profiles. Cell, 102(1):109-126, 2000.
4. R.D. King, S.H. Muggleton, A. Srinivasan, and M. 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.
5. 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.
6. H. Kitano. Computational systems biology. Nature, 420:206-210, 2002.
7. H. Kitano. Systems biology: a brief overview. Science, 295:1662-1664, 2002.
8. S.H. Muggleton and C.H. Bryant. Theory completion using inverse entailment. In Proc. of the 10th International Workshop on Inductive Logic Programming (ILP-00), pages 130-146, Berlin, 2000. Springer-Verlag.
9. S.H. Muggleton, H. Lodhi, A. Amini, and M.J.E. Sternberg. Support Vector Inductive Logic Programming. In D. Holmes and L.C. Jain, editors, Recent Advances in Machine Learning. Springer-Verlag, 2005. To appear.
10. J.K. Nicholson, J. Connelly, J.C. Lindon, and E. Holmes. Metabonomics: a platform for studying drug toxicity and gene function. Nature Drug Discovery, 1:153-161, 2002.
11. 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.
12. A.M. Richard and C.R. Williams. Distributed structure-searchable toxicity (DSSTox) public database network: A proposal. Mutation Research, 499:27-52, 2000.
13. A. Srinivasan, S.H. Muggleton, R. King, and M. Sternberg. Theories for mutagenicity: a study of first-order and feature based induction. Artificial Intelligence, 85(1,2):277-299, 1996.
14. A. Tamaddoni-Nezhad, A. Kakas, S.H. Muggleton, and F. Pazos. Modelling inhibition in metabolic pathways through abduction and induction. In Proceedings of the 14th International Conference on Inductive Logic Programming. Springer-Verlag, 2004.
15. A. Tamaddoni-Nezhad, S. Muggleton, and J. Bang. A Bayesian model for metabolic pathways. In International Joint Conference on Artificial Intelligence (IJCAI03) Workshop on Learning Statistical Models from Relational Data, pages 50-57. IJCAI, 2003.