Multi-assay-based structure-activity relationship models: Improving structure-activity relationship models by incorporating activity information from related targets

Journal of Chemical Information and Modeling

Volumn 49, Issue 11, 2009, Pages 2444-2456

  Ning, Xia ^a   Rangwala, Huzefa ^b   Karypis, George ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

^b George Mason University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ITERATIVE METHODS; LIGANDS; MULTI-TASK LEARNING; PROTEINS; SEMI-SUPERVISED LEARNING; STRUCTURES (BUILT OBJECTS);

ACTIVITY INFORMATIONS; CLASSIFIER ENSEMBLES; COMPREHENSIVE EVALUATION; ITERATIVE OPTIMIZATION; MACHINE LEARNING TECHNIQUES; PRIMARY SEQUENCES; RECEIVER OPERATING CHARACTERISTICS; STRUCTURE ACTIVITY RELATIONSHIPS;

LEARNING SYSTEMS;

ARTICLE; CHEMICAL MODEL; STRUCTURE ACTIVITY RELATION;

MODELS, CHEMICAL; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 72949114936     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci900182q     Document Type: Article

References (53)

1. Hansch, C.; Maolney, P. P.; Fujita, T.; Muir, R. M. Nature 1962, 194, 178-180.
2. Hansch, C.; Muir, R. M.; Fujita, T.; Maloney, C. F.; Streich, M. J. Am. Chem. Soc. 1963, 85, 2817-11824
3. Bravi, G.; Green, E. G. D.; Hann, V.; Mike, M. Modelling Structure-Activity Relationship. In Virtual Screening for Bioactive Molecules, Volume 10; Bohm, H., Schneider, G., Eds.; Wiley-VCH: New York, 2000.
4. Agrafiotis, D.; Bandyopadhyay, D.; Wegner, J.; van Vlijmen, H. J. Chem. Inf. Model. 2007, 47, 1279-1293.
5. Warmuth, M. K.; Liao, J.; Ratsch, G.; Mathieson, M.; Putta, S.; Lemmen, C. J. Chem. Inf. Comput. Sci. 2003, 43, 667-673.
6. Frye, S. Chemistry and Biology 1999, R3-R7.
7. Caron, P. R.; Mullican, M. D.; Mashal, R. D.; Wilson, K. P.; Su, M. S.; Murcko, M. A. Curr. Opin. Chem. Biol. 2001, 5, 464-470
8. Klabunde, T. Br. J. Pharmacol. 2007, 152, 5-7.
9. Bock, J.; Gough, D. J. Chem. Inf. Model. 2005, 45, 1402-1414.
10. Lapinsh, M.; Prusis, P.; Uhlen, S.; Wikberg, J. E. S. Bioinformatics 2005, 21, 4289-4296.
11. Lindstróm, A.; Pettersson, F.; Almqvist, F.; Berglund, A.; Kihlberg, J.; Linusson, A. J. Chem. Inf. Model. 2006, 46, 1154-1167.
12. Jacob, L.; Hoffmann, B.; Stoven, V.; Vert, J.-P. BMC Bioinformatics 2008, 9, 363.
13. Strómbergsson, H.; Daniluk, P.; Kryshtafovych, A.; Fidelis, K.; Wikberg, J. E. S.; Kleywegt, G. J.; Hvidsten, T. R. J. Chem. Inf. Model 2008, 48, 2278-2288.
14. Deng, Z.; Chuaqui, C.; Singh, J. J. Med. Chem. 2004, 47, 337-344.
15. Weill, N.; Rognan, D. J. Chem. Inf. Model. 2009, 49, 1049-1062.
16. Geppert, H.; Humrich, J.; Stumpfe, D.; Gartner, T.; Bajorath, J. J. Chem. Inf. Model. 2009, 49, 767-779.
17. Erhan, D.; L'Heureux, P.-J.; Yue, S. Y.; Bengio, Y. J. Chem. Inf. Model. 2006, 46, 626-635.
18. Semi-Supervised Learning; Chapelle, O., Schólkopf, B., Zien, A., Eds.; MIT Press: Cambridge, MA, 2006.
19. Thurn, S. Is learning the n-th thing any easier than learning the first? In Advances in Neural Information Processing Systems; MIT Press: Cambridge, MA, 1996.
20. Caruana, R. A. Multitask learning: A knowledge-based source of inductive bias. In Proc. Int. Conf. Mach. Learn.; Morgan Kaufmann: MA, 1993.
21. Evgeniou, T.; Micchelli, C. A.; Pontil, M. J. Mach. Learn. Res. 2005, 6, 615-637.
22. Bonilla, E.; Agakov, F.;Williams, C. Kernel Multi-task Learning using Task-specific Features. In Proc. Int. Conf. Artificial Intelligence and Statistics; Omnipress: San Juan, Puerto Rico, 2007.
23. Swanson, R.; Tsai, J. J. Bacteriol. 2003, 185, 3990-3993.
24. Kuncheva, L. I.; Whitaker, C. J. Mach. Learn. 2003, 51, 181-207.
25. Shipp, C. A.; Kuncheva, L. I. Inf. Fusion 2002, 3, 135-148.
26. P. Willett, J.; Downs, G. M. J. Chem. Inf. Comput. Sci. 1998, 38, 983-997.
27. Vapnik, V. Statistical Learning Theory; John Wiley: New York, 1998.
28. Yu, H.; Han, J.; Chang, K. PEBL: Positive example based learning for Web page classification using SVM. In Proc. Int. Conf. Know. Disc. Data Mining; ACM: New York, NY, USA, 2002.
29. Liu, B.; Dai, Y.; Li, X.; Lee, W.; Yu, P. Building text classifiers using positive and unlabeled examples. In Proc. Int. Conf. Data Mining; IEEE Computer Society Press: Florida, USA, 2003.
30. Wang, C.; Ding, C.; Meraz, R. F.; Holbrook, S. R. Bioinformatics 2006, 22, 2590-2596.
31. Elkan, C.; Noto, K. Learning classifiers from only positive and unlabeled data. In Proc. Int. Conf. Know. Disc. Data Mining; ACM: New York, 2008.
32. Davies, E. K.; Briant, C. Combinatorial Chemistry Library Design Using Pharmacophore Diversity. In Molecular Diversity and Combinatorial Chemistry: Libraries and Drug Discovery, Volume 118; American Chemical Society: Washington, DC, 1996, 309-316.
33. Smith, T. F.; Waterman, M. S. J. Mol. Biol. 1981, 147, 195-197.
34. Altschul, S. F.; Madden, T. L.; Schaffer, A. A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D. J. Nucleic Acids Res. 1997, 25, 3389-3402.
35. Heger, A.; Holm, L. Bioinformatics 2001, 17, 272-279.
36. Rangwala, H.; Karypis, G. Bioinformatics 2005, 21, 4239-4247.
37. Zhu, X. "Semi-Supervised Learning Literature Survey", Technical Report, Computer Sciences, University of Wisconsin: Madison, WI, 2005.
38. Zhu, X.; Ghahramani, Z. "Learning from labeled and unlabeled data with label propagation", Technical Report CMU-CALD-02-107, School of Computer Science, Carnegie Mellon University: Pittsburgh, PA, 2002.
39. Martin, Y. C.; Kofron, J. L.; Traphagen, L. M. J. Med. Chem. 2002, 45, 4350-4358.
40. Weston, J.; Elisseeff, A.; Zhou, D.; Leslie, C.; Noble, W. S. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 6559-6563.
41. Tetko, I. V.; Tanchuk, V. Y. J. Chem. Inf. Comput. Sci. 2002, 42, 1136-1145.
42. Tetko, I. V.; Jaroszewicz, I.; Platts, J. A.; Kuduk-Jaworska, J. J. Inorg. Biochem. 2008, 102, 1424-1437.
43. Varnek, A.; Gaudin, C.; Marcou, G.; Baskin, I.; Pandey, A. K.; Tetko, I. V. J. Chem. Inf. Model. 2009, 49, 133-144.
44. Lanckriet, G. R.; Deng, M.; Cristianini, N.; Jordan, M. I.; Noble, W. S. Kernel-based data fusion and its application to protein function prediction in yeast. In Proc. Pac. Symp. Biocomput. World Scientific Publishing: Berkeley, CA, 2004.
45. Sonnenburg, S.; Ratsch, G.; Schafer, C. A General and Efficient Multiple Kernel Learning Algorithm. In Proc. Neural Inf. Proc. Sys.; British Columbia, Canada, 2005.
46. Tsang, I. W.; Kwok, J. T. IEEE Trans. Neural Networks 2006, 17, 48-58.
47. Rangwala, H.; Karypis, G. fRMSDAlign: Protein Sequence Alignment Using Predicted Local Structure Information for Pairs with Low Sequence Identity. In Proc. Asia-Pacific Bioinformatics Conf. Volume 6; Brazma, A., Miyano, S., Akutsu, T., Eds.; Imperial College Press: Kyoto, Japan, 2008.
48. Saigo, H.; Vert, J.-P.; Ueda, N.; Akutsu, T. Bioinformatics 2004, 20, 1682-1689.
49. Austin, C. P.; Brady, L. S.; Insel, T. R.; Collins, F. S. Science 2004, 306, 1138-1139.
50. Wale, N.; Watson, I. A.; Karypis, G. Knowledge and Information Systems 2008, 14, 347-375.
51. Karypis, G. AFGEN2.0, Technical Report 08-020, Department of Computer Science & Engineering, University of Minnesota: Minneapolis, MN; 2007 www.cs.umn.edu/, karypis.
52. Joachims, T. Making Large-Scale SVM Learning Practical 1999, 169-184.
53. Fawcett, T. ROC Graphs: Notes and Practical Considerations for Researchers, HP Lab Technical Report HPL-2003-4; Hewlett Packard: Palo Alto, CA, 2003.