Chemical descriptors are more important than learning algorithms for modelling

Molecular Informatics

Volumn 31, Issue 10, 2012, Pages 707-710

  Young, S Stanley ^a   Yuan, Fei ^b   Zhu, Mu ^c  

^a NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES   (United States)

^b HAMILTON GENERAL HOSPITAL   (Canada)

^c UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Average precision; Boosting; Ensemble algorithms; Kernel algorithms; Random forest; Support vector machine; Virtual screening

Indexed keywords

AVERAGE PRECISION; BOOSTING; CHEMICAL DESCRIPTORS; ENSEMBLE ALGORITHMS; KERNEL ALGORITHMS; RANDOM FORESTS; SUPPORT VECTORS MACHINE; VIRTUAL SCREENING;

SUPPORT VECTOR MACHINES;

4 AMINOBUTYRIC ACID; ADENOSINE; ANTIBIOTIC AGENT; CEPHAMYCIN; HORMONE;

ARTICLE; CHEMICAL MODEL; DATA MINING; DRUG INDUSTRY; KERNEL METHOD; LEARNING ALGORITHM; MOLECULAR LIBRARY; MOLECULAR MODEL; PREDICTOR VARIABLE; PRIORITY JOURNAL; RANDOM FOREST; SAMPLE SIZE; SCREENING; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE;

EID: 84867564789     PISSN: 18681743     EISSN: 18681751     Source Type: Journal    
DOI: 10.1002/minf.201200031     Document Type: Article

References (22)

1. N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, Cambridge, 2002.
2. L. Breiman, Mach. Learn. 2001, 45, 5-32.
3. V. Svetnik, A. Liaw, C. Tong, T. Wang, in Multiple Classifier Systems, Lecture Notes in Computer Science, Vol. 3077 (Eds:, F. Roli, J. Kittler, T. Windeatt,), Springer, Heidelberg, 2004, pp. 334-343.
4. V. V. Zernov, K. V. Balakin, A. A. Ivaschenko, N. P. Savchuk, I. V. Pletnev, J. Chem. Inf. Comp. Sci. 2003, 43, 2048-2056.
5. T. Hou, J. Wang, Y. Li, J. Chem. Inf. Model. 2007, 47, 2408-2415.
6. H. M. Ashtawy, N. R. Mahapatrat, in Proc. 2011 IEEE Intern. Conf. Bioinformatics Biomedicine, 2011, pp. 627-630.
7. J. Bajorath, Nat. Rev. Drug Discov. 2002, 1, 882-894.
8. D. J. Hand, Stat. Sci. 2006, 21, 1-14.
9. R. Todeschini, V. Consonni, Handbook of Molecular Descriptors, Wiley-VCH, Berlin, 2002.
10. N. Huang, B. K. Shoichet, J. J. Irwin, J. Med. Chem. 2006, 49, 6789-6801
11. see also: DUD: A Directory of Useful Decoys, http://dud.docking.org.
12. F. R. Burden, J. Chem. Inf. Comp. Sci. 1989, 29, 225-227.
13. K. Liu, J. Feng, S. S. Young, J. Chem. Inf. Model. 2005, 45, 515-522.
14. G. Shmueli, Stat. Sci. 2010, 25, 289-310.
15. M. Zhu, Am. Stat. 2008, 62, 97-109.
16. L. Breiman, Mach. Learn. 1996, 24, 123-140.
17. J. H. Friedman, T. J. Hastie, R. J. Tibshirani, Ann. Stat. 2000, 28, 337-407.
18. Y. Freund, R. Iyer, R. E. Schapire, Y. Singer, J. Mach. Learn. Res. 2003, 4, 933-969.
19. M. Zhu, W. Su, H. A. Chipman, Technometrics 2006, 48, 193-205.
20. M. Pepe, The Statistical Evaluation of Medical Tests for Classification and Prediction, Oxford University Press, Oxford, 2003.
21. th, 2012.
22. J. M. Hughes-Oliver, A. D. Brooks, W. J. Welch, M. G. Khaledi, D. Hawkins, S. S. Young, K. Patil, G. W. Howell, R. T. Ng, M. T. Chu, In Silico Biol. 2011 /2012, 11, 61-81.