Fragment generation and support vector machines for inducing SARs

References

• Klopman , G. 1984 . “Artificial intelligence approach to structure-activity studies: computer automated structure evaluation of biological activity of organic molecules” . J. Am. Chem. Soc. , 106 : 7315 – 7321 .
   Web of Science ®Google Scholar
• Klopman , G. 1992 . “MultiCASE: a hierarchical computer automated structure evaluation program” . Quant. Struct.-Act. Relat. , 11 : 176 – 184 .
   Web of Science ®Google Scholar
• Malacarne , D. , Pesenti , R. , Paolucci , M. and Parodi , S. 1993 . “Relationship between molecular connectivity and carcinogenic activity: a confirmation with a new software program based on graph theory” . Environ. Health Perspect. , 101 : 332 – 342 .
   PubMed Web of Science ®Google Scholar
• Malacarne , D. , Taningher , M. , Pesenti , R. , Paolucci , M. , Perrotta , A. and Parodi , S. 1995 . “Molecular fragments associated with non-genotoxic carcinogens, as detected using a software program based on graph theory: their usefulness to predict carcinogenicity” . Chem.-Biol. Interact. , 97 : 75 – 100 .
   PubMed Web of Science ®Google Scholar
• De Raedt , L. and Kramer , S. "The levelwise version space algorithm and its application to molecular fragment finding" . Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01) . Edited by: Nebel , B. pp. 853 – 859 . San Francisco , CA : Morgan Kaufmann .
   Google Scholar
• Kramer , S. and De Raedt , L. "Feature construction with version spaces for biochemical applications" . Proceedings of the Eighteenth International Conference on Machine Learning (ICML-2001) . Edited by: Brodley , C. and Danyluk , A. pp. 258 – 265 . San Mateo , CA : Morgan Kaufmann .
   Google Scholar
• Agrawal , R. , Mannila , H. , Srikant , R. , Toivonen , H. and Verkamo , A.I. 1996 . “Fast discovery of association rules” . In Advances in Knowledge Discovery and Data Mining , Edited by: Fayyad , U. , Piatetsky-Shapiro , G. , Smyth , P. and Uthurusamy , R. 307 – 328 . Menlo Park , CA : AAAI Press .
   Google Scholar
• Mannila , H. and Toivonen , H. 1997 . “Levelwise search and borders of theories in knowledge discovery” . Data Mining and Knowledge Discovery , 1 : 241 – 258 .
   Web of Science ®Google Scholar
• Vapnik , V. 1982 . Estimation of Dependencies Based on Empirical Data , Berlin : Springer .
   Google Scholar
• Vapnik , V. 1995 . The Nature of Statistical Learning Theory , Berlin : Springer .
   Google Scholar
• Joachims , T. "Transductive inference for text classification using support vector machines" . Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99) . Edited by: Bratko , I. and Dzeroski , S. pp. 200 – 209 . San Mateo , CA : Morgan Kaufmann .
   Google Scholar
• Brown , M. , Grundy , W. , Lin , D. , Cristianini , N. , Sugnet , C. , Furey , T. , Ares , M. and Haussler , D. 1999 . “Knowledge-based analysis of microarray gene expression data using support vector machines” . Proc. Natl Acad. Sci. , 97 : 262 – 267 .
   Web of Science ®Google Scholar
• Furey , T. , Cristianini , N. , Duffy , N. , Bednarski , D. , Schummer , M. and Haussler , D. 2000 . “Support vector machine classification and validation of cancer tissue samples using microarray expression” . Bioinformatics , 16 : 906 – 914 .
   PubMed Web of Science ®Google Scholar
• Jaakkola , T. , Diekhans , M. and Haussler , D. 2000 . “A discrimitive framework for detecting remote protein homologies” . J. Comput. Biol. , 7 : 95 – 114 .
   PubMed Web of Science ®Google Scholar
• Burbidge , R. , Trotter , M. , Holden , S. and Buxton , B. Drug design by machine learning: support vector machines for pharmaceutical data analysis . Presentation at the symposium "Artificial Intelligence in Bioinformatics" . UK : University of Birmingham . [ www http://www.rdg.ac.uk/∼sas99acm/aisb00/abstracts/burbidge/index.html]
   Google Scholar
• Minsky , M.L. and Papert , S.A. 1969 . Perceptrons , Cambridge , MA : MIT Press .
   Google Scholar
• Schölkopf , B. , Burges , C. and Smola , A. 1999 . Advances in Kernel Methods: Support Vector Learning , Cambridge , MA : MIT Press .
   Google Scholar
• Cristianini , N. and Shawe-Taylor , J. 2000 . An Introduction to Support Vector Machines and other Kernel-based Learning Methods , Cambridge , , UK : Cambridge University Press .
   Google Scholar
• Perrotta , A. , Malacarne , D. , Taningher , M. , Pesenti , R. , Paolucci , M. and Parodi , S. 1996 . “A computerized connectivity approach for analyzing the structural basis of mutagenicity in Salmonella and its relationship with rodent carcinogenicity” . Environ. Mol. Mutagen. , 28 : 31 – 50 .
   PubMed Web of Science ®Google Scholar
• Mitchell , T.M. 1997 . Machine Learning , New York , NY : McGraw-Hill .
   Google Scholar
• Helma , C. , King , R.D. , Kramer , S. and Srinivasan , A. 2001 . “The predictive toxicology challenge 2000-2001” . Bioinformatics , 17 : 107 – 108 . [www http://www.informatik.uni-freiburg.de/∼ml/ptc/]
   Web of Science ®Google Scholar
• Platt , J. 1999 . “Fast training of support vector machines using sequential minimal optimization” . In Advances in Kernel Methods: Support Vector Learning , Edited by: Schölkopf , B. , Burges , C. and Smola , A. 185 – 208 . Cambridge , MA : MIT Press .
   Google Scholar
• Witten , I. and Frank , E. 1999 . Data Mining , San Francisco , CA : Morgan Kaufmann . [www http://www.cs.waikato.ac.nz/∼ml/weka/index.html]
   Google Scholar
• Kohavi , R. "A study of cross-validation and bootstrap for accuracy estimation and model selection" . Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI-95) . Edited by: Mellish , C.S. pp. 1137 – 1143 . San Mateo , CA : Morgan Kaufmann .
   Google Scholar
• Kramer , S. "Structural regression trees" . Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96) . Edited by: Clancey , W. and Weld , D. pp. 812 – 819 . Menlo Park , CA : AAAI Press .
   Google Scholar
• Kramer , S. 2000 . “Relational learning vs. propositionalization: investigations in inductive logic programming and propositional machine learning” . AI Commun. , 13 : 275 – 276 .
   Web of Science ®Google Scholar
• Kramer , S. and Widmer , G. 2001 . “Inducing classification and regression trees in first-order logic” . In Relational Data Mining , Edited by: Dzeroski , S. and Lavrac , N. 140 – 159 . Berlin : Springer .
   Google Scholar
• Dietterich , T.G. and Bakiri , G. 1995 . “Solving multiclass learning problems via error-correcting output codes” . J. Artif. Intell. Res. , 2 : 263 – 286 .
   Web of Science ®Google Scholar
• Hastie , T. and Tibshirani , R. 1998 . “Classification by pairwise coupling” . Ann. Stat. , 26 : 451 – 471 .
   Web of Science ®Google Scholar
• Drucker , H. , Burges , C.J.C. , Kaufman , L. , Smola , A. and Vapnik , V. 1997 . “Support vector regression machines” . In Advances in Neural Information Processing Systems , Edited by: Mozer , M. , Jordan , M. and Petsche , T. 155 – 161 . Cambridge , MA : MIT Press .
   Google Scholar