A robust boosting regression tree with applications in quantitative structure-activity relationship studies of organic compounds

Journal of Chemical Information and Modeling

Volumn 51, Issue 4, 2011, Pages 816-828

  Jiao, Jian ^a   Tan, Shi Miao ^a   Luo, Rui Ming ^a   Zhou, Yan Ping ^a  

^a CENTRAL CHINA NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

FORESTRY; STRUCTURES (BUILT OBJECTS);

ANTI-HIV ACTIVITY; GENERALIZATION ABILITY; QSAR STUDIES; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP STUDIES; REGRESSION TREES; ROBUST BOOSTING; TRAINING SETS; WEIGHTED MEDIAN;

COMPUTATIONAL CHEMISTRY;

ANTI HUMAN IMMUNODEFICIENCY VIRUS AGENT; ENZYME INHIBITOR; FLAVONE DERIVATIVE;

ALGORITHM; ARTICLE; CHEMICAL STRUCTURE; CHEMISTRY; COMPARATIVE STUDY; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REGRESSION ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; ANTI-HIV AGENTS; ENZYME INHIBITORS; FLAVONES; MODELS, MOLECULAR; MODELS, STATISTICAL; MOLECULAR STRUCTURE; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; REGRESSION ANALYSIS;

EID: 79955366049     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci100429u     Document Type: Article

References (36)

1. Breiman, L.; Friedman, J. H.; Olshen, R. J.; Stone, C. J. In Classification and Regression Trees; Bickel, P. J., Cleveland, W. S., Dudley, R. M., Eds.; Wadsworth Internal Group: Belmont, CA, 1984.
2. Daszykowski, M.; Walczak, B.; Xu, Q. S.; Daeyaert, F.; de Jonge, M. R.; Heeres, J.; Koymans, L. M. H.; Lewi, P. J.; Vinkers, H. M.; Janssen, P. A.; Massart, D. L. Classification and Regression Trees-Studies of HIV Reverse Transcriptase Inhibitors J. Chem. Inf. Comput. Sci. 2004, 44, 716-726
3. Gleeson, M. P.; Waters, N. J.; Paine, S. W.; Davis, A. M. In Silico Human and Rat Vss Quantitative Structure-Activity Relationship Models J. Med. Chem. 2006, 49, 1953-1963
4. Zhou, Y. P.; Tang., L. J.; Jiao., J.; Song, D. D.; Jiang, J. H.; Yu, R. Q. Modified Particle Swarm Optimization Algorithm for Adaptively Configuring Globally Optimal Classification and Regression Trees J. Chem. Inf. Model. 2009, 49, 1144-1153
5. Tan, S. M.; Jiao, J.; Zhu, X. L.; Zhou, Y. P.; Song, D. D.; Gong, H.; Yu, R. Q. QSAR Studies of a Diverse Series of Antimicrobial Agents against Candida albicans by Classification and Regression Trees Chemom. Intell. Lab. Syst. 2010, 103, 184-190
6. Svetnik, V.; Wang, T.; Tong, C.; Liaw, A.; Sheridan, R. P.; Song, Q. H. Boosting: An Ensemble Learning Tool for Compound Classification and QSAR Modeling J. Chem. Inf. Model. 2005, 45, 786-799 (Pubitemid 40795182)
7. Delisle, R. K.; Dioxon, S. L. Induction of Decision Trees via Evolutionary Programming J. Chem. Inf. Comput. Sci. 2004, 44, 862-870
8. Buontempo, F. V.; Wang, X. Z.; Mwense, M.; Horan, N.; Young, A.; Osborn, D. Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data J. Chem. Inf. Model. 2005, 45, 904-912 (Pubitemid 41156690)
9. Izrailev, S.; Agrafiotis, D. A Novel Method for Building Regression Tree Models for QSAR Based on Artificial Ant Colony Systems J. Chem. Inf. Comput. Sci. 2001, 41, 176-180 (Pubitemid 33645687)
10. Dietterich, T. G. Ensemble Learning. In The Handbook of Brain Theory and Neural Networks, 2 nd ed.; Arbib, M. A., Ed.; The MIT Press: Cambridge, 2002.
11. Hawkins, D. M.; Musser, B. J. One Tree or a Forest? Alternative Dendrographic Models Comput. Sci. Stat. 1999, 30, 534-542
12. Tong, W.; Hong, H.; Fang, H.; Xie, Q.; Perkins, R. Decision Forest: Combining the Predictions of Multiple Independent Decision Tree Models J. Chem. Inf. Comput. Sci. 2003, 43, 525-531
13. van Rhee, A. M. Use of Recursion Forests in the Sequential Screening Process: Consensus Selection by Multiple Recursion Trees J. Chem. Inf. Comput. Sci. 2003, 43, 941-948
14. Meyer, D.; Leisch, F.; Hornik, K. The Support Vector Machine under Test Neurocomputing. 2003, 55, 169-186 (Pubitemid 37336675)
15. He, P.; Xu, C. J.; Liang, Y. Z.; Fang, K. T. Improving the Classification Accuracy in Chemistry via Boosting Technique Chemom. Intell. Lab. Syst. 2004, 70, 39-46 (Pubitemid 38102217)
16. Culp, M.; Johnson, K.; Michailidis, G. The Ensemble Bridge Algorithm: A New Modeling Tool for Drug Discovery Problems J. Chem. Inf. Model. 2010, 50, 309-316
17. Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J. C.; Sheridan, R. P.; Feuston, B. P. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling J. Chem. Inf. Comput. Sci. 2003, 43, 1947-1958
18. Deconinck, E.; Zhang, M. H.; Coomans, D.; Heyden, Y. V. Evaluation of Boosted Regression Trees (BRTs) and Two-Step BRT Procedures to Model and Predict Blood-Brain Barrier Passage J. Chemom. 2007, 21, 280-291
19. Schapire, R. E. The Strength of Weak Learnability Mach. Learn. 1990, 5, 197-227
20. Zhou, Y. P.; Jiang, J. H.; Lin, W. Q.; Zou, H. Y.; Wu, H. L.; Shen, G. L.; Yu, R. Q. Boosting Support Vector Regression in QSAR Studies of Bioactivities of Chemical Compounds Eur. J. Pharm. Sci. 2006, 28, 344-353
21. Zhang, M. H.; Xu, Q. S.; Massart, D. L. Boosting Partial Least Square Anal. Chem. 2005, 77, 1423-1431
22. Zhou, Y. P.; Cai, C. B.; Huan, S.; Jiang, J. H.; Wu, H. L.; Shen, G. L.; Yu, R. Q. QSAR Study of Angiotensin II Antagonists Using Robust Boosting Partial Least Squares Regression Anal. Chim. Acta 2007, 593, 68-74 (Pubitemid 46776622)
23. Dietterich, T. G. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization Mach. Learn. 2000, 40, 139-157
24. Grove, A. J.; Schuurmans, D. Boosting in the Limit: Maximizing the Margin of Learned Ensembles. Proceedings of the 15th National Conference on Artificial Intelligence; Madison, WI, July 1998; pp 692 - 299.
25. Friedman, J.; Hastie, T.; Tibshirani, R. Additive Logistic Regression: A Statistical View of Boosting Ann. Statist. 2000, 28, 337-407 (Pubitemid 33227445)
26. Freund, Y.; Schapire, R. Experiments with a New Boosting Algorithm. Proceeding of the 13th International Conference on Machine Learning; 1996; pp 148-156
27. The Mathematics of Generalization; Wolpert, D. H., Ed.; Addison-Wesley: Reading, 1995.
28. Drucker, H. Improving Regressors Using Boosting Techniques. Proceedings of the 14th National Conference on Machine Learning; Nashville, TN, July 8-12, 1997; pp 107 - 115.
29. Bhhatarai, B.; Garg, R. From SAR to Comparative QSAR: Role of Hydrophobicity in the Design of 4-Hydroxy-5,6-dihydropyran-2-ones HIV-1 Protease Inhibitors Bioorg. Med. Chem. 2005, 13, 4078-4084 (Pubitemid 40725040)
30. Leonard, J. T.; Roy, K. QSAR by LFER Model of HIV Protease Inhibitor Mannitol Derivatives Using FA-MLR, PCRA, and RT Techniques Bioorg. Med. Chem. 2006, 14, 1039-1046 (Pubitemid 43059795)
31. Roy, P. P.; Leonard, J. T.; Roy, K. Exploring the Impact of Size of Training Sets for the Development of Predictive QSAR Models Chemom. Intell. Lab. Syst. 2008, 90, 31-42 (Pubitemid 350213175)
32. Freitas, M. P. MIA-QSAR Modelling of Anti-HIV-1 Activities of Some 2-Amino-6-Arylsulfonylbenzonitriles and Their Thio and Sulfinyl Congeners Org. Biomol. Chem. 2006, 4, 1154-1159 (Pubitemid 43383363)
33. Thakur, A.; Vishwakarma, S.; Thakur, M. QSAR Study of Flavonoid Derivatives as P56lck Tyrosinkinase Inhibitors Bioorg. Med. Chem. 2004, 12, 1209-1214 (Pubitemid 38251311)
34. Centner, V.; de Noord, O. E.; Massart, D. L. Detection of Nonlinearity in Multivariate Calibration Anal. Chim. Acta 1998, 376, 153-168 (Pubitemid 28532668)
35. Centnera, V.; Massart, D. L.; de Noord, O. X. Detection of Inhomogeneities in Sets of NIR Spectra Anal. Chim. Acta 1996, 330, 1-17 (Pubitemid 26274007)
36. Cook, R. D. Detection of Influential Observations in Linear Regression Technometrics 1977, 19, 15-18