Evaluation of mutual information, genetic algorithm and SVR for feature selection in QSAR regression

Current Drug Discovery Technologies

Volumn 8, Issue 2, 2011, Pages 107-111

  Fang, Jianwen ^a   Tai, David ^a  

^a UNIVERSITY OF KANSAS   (United States)

Author keywords

Feature selection; Quantitative structure activity relationship; Regression; Support vector machine

Indexed keywords

ALGORITHM; ARTICLE; EVALUATION; GENETIC ALGORITHM; INTERMETHOD COMPARISON; MUTUAL INFORMATION; PARAMETER; PERFORMANCE; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SUPPORT VECTOR MACHINE; SUPPORT VECTOR MACHINE REGRESSION BASED RECURSIVE FEATURE ELIMINATION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER-AIDED DESIGN; DRUG DESIGN; MOLECULAR STRUCTURE; PHARMACEUTICAL PREPARATIONS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; REGRESSION ANALYSIS; TECHNOLOGY, PHARMACEUTICAL;

EID: 79956086847     PISSN: 15701638     EISSN: 18756220     Source Type: Journal    
DOI: 10.2174/157016311795563839     Document Type: Article

References (28)

1. Hansch C, Streich M, Geiger F, Muir RM, Maloney PP, Fujita T. Correlation of biological activity of plant growth regulators and chloromycetin derivatives with hammett constants and partition coefficients. J Am Chem Soc 1963; 85(18): 2817-24.
2. Xue L, Bajorath J. Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Comb Chem High Throughput Screen 2000; 3(5): 363-72.
3. Kohavi R, John GH. Wrappers for feature subset selection. Artif Intell 1997; 97(1-2): 273-324. (Pubitemid 127401107)
4. Guyon I, Elisseeff A. An introduction to variable and Feature selection. J machine learning res 2003; 3: 1157-82.
5. Hoffman BT, Kopajtic T, Katz JL, Newman AH. 2D QSAR modeling and preliminary database searching for dopamine transporter inhibitors using genetic algorithm variable selection of Molconn Z descriptors. J Med Chem 2000; 43 (22): 4151-9.
6. Wegner JK, Zell A. Prediction of aqueous solubility and partition coefficient optimized by a genetic algorithm based descriptor selection method. J Chem Inf Comp Sci 2003; 43(3): 1077-1084.
7. Yasri A, Hartsough D. Toward an optimal procedure for variable selection and QSAR model building. J Chem Inf Comp Sci 2001; 41(5): 1218-27.
8. Weston J, Elisseeff A, Schölkopf B, Tipping M. Use of the Zero- Norm with Linear Models and Kernel Methods. J Machine Learning Res 2003; 3: 1439-61.
9. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn 2002; 46 (1-3): 389-422. (Pubitemid 34129977)
10. Bi J, Bennett K, Embrechts M, Breneman C, Song MJ. Dimensionality reduction via sparse support vector machines. J Mach Learn Res 2003; 3: 1229-43.
11. Rakotomamonjy A. Variable selection using SVM-based criteria. J Machine Learning Res 2003; 3: 1357-70.
12. Rusinko A, Farmen MW, Lambert CG, Brown PL, Young SS. Analysis of a large structure/biological activity data set using recursive partitioning. J Chem Inf Comp Sci 1999; 39(6): 1017-26. (Pubitemid 129579834)
13. Godden JW, Furr JR, Bajorath J. Recursive median partitioning for virtual screening of large databases. J Chem Inf Comp Sci 2003; 43(1): 182-8.
14. Rivals I, Personnaz L. MLPs (Monolayer Polynomials and Multi- Layer Perceptrons) for Nonlinear Modeling. J Machine Learning Res 2003; 3: 1383-98.
15. Stoppiglia H, Dreyfus G, Dubois R, Oussar Y. Ranking a random feature for variable and feature selection. J Machine Learning Res 2003; 3: 1399-414.
16. Rogati M, Yang Y. High-performing feature selection for text classification. In Proceedings of the eleventh international conference on Information and knowledge management, ACM: McLean, Virginia, USA, 2002; 659-661.
17. Judson R, Elloumi F, Setzer RW, Li Z, Shah I. A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model. BMC Bioinformatics 2008; 9: 241.
18. Kubiny H. Variable selection in QSAR studies. I. an evolutionary lgorithm. QSAR & Combinatorial Science 1994; 13(3): 285-94;
19. 19] Roy PP, Roy K. On some aspects of variable selection for partial least squares regression models. QSAR & Combinatorial Science 2008; 27 (3): 302-13.
20. Soltani S, Abolhasani H, Zarghi A, Jouyban A. QSAR analysis of diaryl COX-2 inhibitors: Comparison of feature selection and traintest data selection methods. Eur J Med Chem 2010; 45(7): 2753-60.
21. Bohm M, Sturzebecher J, Klebe G. Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. J Med Chem 1999; 42(3): 458-77. (Pubitemid 29085107)
22. Sturzebecher J, Prasa D, Wikstrom P, Vieweg H. Structureactivity- relationships of inhibitors derived from 3-amidinophenylalanine. J Enzym Inhib 1995; 9(1): 87-99.
23. http://www.kyb.mpg.de/bs/people/spider/.
24. Venkatraman V, Dalby AR, Yang ZR. Evaluation of mutual information and genetic programming for feature selection in QSAR. J Chem Inf Comp Sci 2004; 44(5): 1686-92.
25. Holland JH. Genetic algorithms and the optimal allocation of trials. SIAM J Comput 1973; 2(2): 88-105.
26. Vapnik VN. An overview of statistical learning theory. IEEE Trans. Neural Networks 1999; 10 (5): 988-99.
27. Smola AJ, Schölkopf B. A tutorial on support vector regression. Statistics and Computing 2004; 14 (3): 199-222. (Pubitemid 39063488)
28. Fisher RA. On the "Probable Error" of a coefficient of correlation deduced from a small sample. Metron 1921; 1: 3-32.