Classifying large chemical data sets: Using a regularized potential function method

Journal of Chemical Information and Modeling

Volumn 51, Issue 1, 2011, Pages 4-14

  Mussa, Hamse Y ^a   Hawizy, Lezan ^a   Nigsch, Florian ^b   Glen, Robert C ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b NOVARTIS INSTITUTES FOR BIOMEDICAL RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PATTERN RECOGNITION; SUPPORT VECTOR MACHINES;

BINARY KERNEL DISCRIMINATIONS; GENERALIZATION ABILITY; KERNEL BASED CLASSIFIERS; KERNEL BASED METHODS; KERNEL-BASED PERCEPTRON; REGULARIZATION NETWORKS; REGULARIZATION SCHEMES; THEORETICAL GUARANTEES;

CLASSIFICATION (OF INFORMATION);

ARTICLE; CHEMISTRY; CLASSIFICATION; DECISION MAKING; DISCRIMINANT ANALYSIS; INFORMATION SCIENCE; METHODOLOGY; NONLINEAR SYSTEM;

CHEMISTRY; CLASSIFICATION; DECISION MAKING; DISCRIMINANT ANALYSIS; INFORMATICS; NONLINEAR DYNAMICS;

EID: 79952592181     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci100022u     Document Type: Article

References (46)

1. Nigsch, F.; Bender, A.; Jenkins, J. L.; Mitchell, J. Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance Statistics. J. Chem. Inf. Model. 2008, 48, 2313-2325.
2. Bender, A.; Mussa, H. Y.; Glen, R. C.; Reiling, S. Molecular similarity searching using atom environments, information-based feature selection, and a naive Bayesian classifier. J. Chem. Inf. Comput. Sci. 2004, 44, 170-178.
3. Kotani, T.; Higashiura, K. Rapid evaluation of molecular shape similarity index using pairwise calculation of the nearest atomic distances. J. Chem. Inf. Comput. Sci. 2002, 42, 58-63. (Pubitemid 35359023)
4. Rueda, M.; Bottegoni, G.; Abagyan, R. Consistent Improvement of Cross-Docking Results Using Binding Site Ensembles Generated with Elastic Network Normal Modes. J. Chem. Inf. Comput. Sci. 2009, 49, 716-725.
5. Aizerman, M.; Braverman, E. M; Rozonoer, L. Theoretical Foundations of the Potential Function Method in Pattern Recognition. Avtom. Telemekh. 1964, 25, 917-936.
6. Vapnik, V. N. The Nature of Statistical Learning Theory, 2nd ed.; Springer-Verlag: Berlin, Germany, 1995.
7. Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5-32.
8. Mitchell, T. M. Machine Learning, 2nd ed.; McGraw-Hill International: New York, 1997.
9. Evgeniou, T.; Pontil, M.; Poggio, T. Regularization Networks and Support Vector Machines. Adv. Comp. Math. 2000, 13, 1-50.
10. Bender, A.; Jenkins, J. L.; Glick, M.; Deng, Z.; Nettles, H. J.; Davies, W. J. "Bayes Affinity Fingerprints" Improve Retrieval Rates in Virtual Screening and Define Orthogonal Bioactivity Space: When Are Multitarget Drugs a Feasible Concept. J. Chem. Inf. Model. 2006, 46, 2445-2456. (Pubitemid 46008117)
11. Harper, G.; Bradshaw, J.; Gittins, J. C.; Green, D. V. S.; Leach, A. R. Prediction of biological activity for high-throughput screening using binary kernel discrimination. J. Chem. Inf. Comput. Sci. 2001, 41, 1295-1300.
12. Wilton, D. J.; Harrison, R. F.; Willett, P.; Dalaney, J.; Lawson, K.; Mullier, G. Virtual Screening Using Binary Kernel Discrimination: Analysis of Pesticide Data. J. Chem. Inf. Model. 2006, 46, 471-477.
13. Schurmann, J. Classification: A Unified View of Statistical and Neural Approaches, 1st ed.; John Wiley and Sons: New York, 1996.
14. Haykin, S. Neural Networks: A Comprehensive Foundation, 2nd ed.; Prentice Hall: Upper Saddle River, NJ, 1999.
15. Bishop, C. M. Pattern Recognition and Machine Learning, 1st ed.; Springer-Verlag: New York, 2006.
16. Tresp, V. Scaling kernel-based systems to large data sets. Data Min. Knowl. Discov. 2001, 5, 197-211. (Pubitemid 33750366)
17. Aronszajn, N. Theory of reproducing kernels. Trans. Amer. Math. Soc. 1950, 68, 337-404.
18. Wahba, G. Spline models for observational data (CBMS-NSF Regional Conference Series in Applied Mathematics), 1st ed.; SIAM: Philadelphia, PA, 1990.
19. Saitoh, S. Theory of Reproducing Kernels and its Applications (Pitman research notes in mathematics series), 1st ed.; Longman Scientific and Technical: Harlow, Essex, U. K., 1988; Vol. 189.
20. Abe, S. Support Vector machines for pattern classification, 1st ed.; Springer-Verlag: New York, 2005.
21. Young, T.; Calvert, T. W. Classification, Estimation and Pattern Recognition, 1st ed.; American Elsevier Pub. Co.: New York, 1974.
22. Meisel, W. S. Computer-Oriented Approaches to Pattern Recognition, 1st ed.; Academic Press: New York, 1972.
23. Freund, Y.; Schapire, R. E. Large margin classification using the perceptron algorithm. Machine Learning 1999, 37, 277-296. (Pubitemid 32210619)
24. Braverman, E. M. On the potential function method. Avtom. Telemekh. 1965, 26, 2205-2213.
25. Aizerman, M.; Braverman, E. M.; Rozonoer, L. The probability problem of pattern recognition learning and method of potential functions. Avtom. Telemekh. 1964, 25, 1307-1323.
26. Braverman, E. M.; Pyatnitski, E. S. Estimation of the rate of convergence of algorithms based on the potential function method. Avtom. Telemekh. 1966, 27, 95-112.
27. Fukunaga, K. Introduction to Statistical Pattern Recognition, 2nd ed.; Academic Press: London, U. K., 1990.
28. Shawe-Taylor, J.; Cristianini, N. Kernel Methods for Pattern Analysis, 1st ed.; Cambridge University Press: Cambridge, U. K., 2004.
29. Ikeda, K. Convergence Theorem for Kemel Perceptron: In Computational Intelligence for the E-Age; Proceedings of the 9th International Conference on Neural Information, Singapore, November 18-22, 2002; Wang, L, Rajapakse, J. C., Fukushima, K., Lee, S, Yao, X., Eds; Wiley-IEEE Press: Hoboken, NJ, 2002; Vol. 1, pp 163-166.
30. Cortes, C.; Vapnik, V. N. Support vector networks. Mach. Learn. 1995, 20, 273-297.
31. Hofmann, T.; Schölkopf, B.; Smola, A. J. Kernel methods in machine learning. Ann. Stat. 2008, 36, 1171-1220.
32. Burges, C. J. Simplified support Vector decision rules; Proceedings of the 13th International Conference on Machine Learning, Bari, Italy, July 3-6, 1996; Saitta, L., Ed.; Morgan Kaufmann: Burlington, MA, 1996; pp 71-77.
33. Burges, C. J. C. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 1998, 2, 121-167. (Pubitemid 128695475)
34. Girosi, F. An equivalence between sparse approximation and support vector machines. Neural Comput. 1998, 10, 1455-1480. (Pubitemid 128464285)
35. Smola, A. J.; Scholkopf, B.; Muller, K. R. The connection between regularization operators and support vector kernels. Neural Networks 1998, 11, 637-649. (Pubitemid 28400264)
36. van Rijsbergen, C. J. Information Retrieval, 2nd ed.; Butterworths: London, U. K., 1979.
37. Duda, R. O.; Hart, P. E. Pattern Classification and Scene Analysis, 1st ed.; Wiley: New York, 1973.
38. Wilcoxon, F. Individual comparisons by ranking methods. Biometrics Bulletin 1945, 1, 80-83.
39. Joachims, T.; Finley, T.; Yu, C. J. Cutting-Plane Training of Structural SVMs. Mach. Learn. 2009, 77, 27-59.
40. Olah, M.; Mracec, M.; Ostopovici, L.; Rad, R.; Bora, A.; Hadaruga, N.; Olah, I.; Banda, M.; Simon, Z.; Oprea, T. I. WOMBAT: World of Molecular Bioactivity. In Cheminformatics in Drug Discovery; Oprea, T. I., Ed.; Wiley-VCH: Weinheim, Germany, 2004; pp 223-239.
41. Molecular Operating Environment (MOE); Chemical Computing Group: Montreal, Quebec, Canada; http://www.chemComp.com. Accessed January 10, 2010.
42. Kearns, M. A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split. Neural Comput. 1997, 9, 1143-1161. (Pubitemid 127462796)
43. Ripley, B. D. Pattern Pattern Recognition and Neural Networks, 1st ed.; Cambridge University Press: Cambridge, U. K., 1996; pp 66-89.
44. Joachims, T. Making large-Scale SVM Learning Practical. In Advances in Kernel Methods - Support Vector Learning; Schölkopf, B., Burges, C., Smola, A. J., Eds.; MIT-Press: Cambridge, MA, 1999; pp 169-184.
45. University of California Irvine Machine Learning Repository; University of California, Irvine: Irvine, CA; http://archive.ics.uci.edu/ml/. Accessed March 4, 2008.
46. R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2008; http://www.R-project.org. Accessed April-July, 2010.