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Cited by 3,573

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Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling

Svetnik, Vladimir, Liaw, Andy, Tong, Christopher, Culberson, J. Christopher, Sheridan, Robert P., Feuston, Bradley P.·2003·Journal of Chemical Information and Computer Sciences
DOI 10.1021/ci034160g
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Abstract

A new classification and regression tool, Random Forest, is introduced and investigated for predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Random Forest is an ensemble of unpruned classification or regression trees created by using bootstrap samples of the training data and random feature selection in tree induction. Prediction is made by aggregating (majority vote or averaging) the predictions of the ensemble. We built predictive models for six cheminformatics data sets. Our analysis demonstrates that Random Forest is a powerful tool capable of delivering performance that is among the most accurate methods to date. We also present three additional features of Random Forest: built-in performance assessment, a measure of relative importance of descriptors, and a measure of compound similarity that is weighted by the relative importance of descriptors. It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.

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Research works printed but not linked: 36

  1. Rusinko A., 1999, J. Chem. Inf. Comput. Sci., 39, 1026, 10.1021/ci9903049 · DOI · Paper page
  2. Doniger S., 2002, J. Comput. Biol., 9, 864, 10.1089/10665270260518317 · DOI · Paper page
  3. Kauffman G. W., 2001, J. Chem. Inf. Comput. Sci., 41, 1560
  4. Sheridan R. P., 1994, J. Comput.-Aided Mol. Des., 8, 340, 10.1007/BF00126749 · DOI
  5. Bakken G. A., 2000, J. Med. Chem., 43, 4541, 10.1021/jm000244u · doi:10.1021/jm000244u
  6. Tong W., 2003, J. Chem. Inf. Comput. Sci., 43, 531, 10.1021/ci020058s · DOI · Paper page
  7. Dietterich T. G., 2002, The Handbook of Brain Theory and Neural Networks
  8. Breiman L, 2001, Machine Learning, 45, 32
  9. The Mathematics, 1995, Addison-Wesley: Reading
  10. Breiman L, 1996, Machine Learning, 24, 140
  11. Breiman L., 1984, Classification and Regression Trees
  12. Meyer D., 2003, The support vector machine under test. Neurocomputing
  13. Hawkins D. M., 1999, Comput. Sci. Stat., 30, 542
  14. Hawkins D. M., 2003, J. Chem. Inf. Comput. Sci., 43, 586, 10.1021/ci025626i · DOI · Paper page
  15. Breiman L., 1996, Out-of-bag estimation
  16. Breiman L., 2002, IMS Wald Lecture 2
  17. Schapire R. E., 1998, Ann. Stat., 26, 1686
  18. Brown R. D., 1996, J. Chem. Inf. Comput. Sci., 36, 584, 10.1021/ci9501047 · DOI · Paper page
  19. Penzotti J. E., 2002, J. Med. Chem., 45, 1740, 10.1021/jm0255062 · DOI · Paper page
  20. Carhart R. E., 1985, J. Chem. Inf. Comput. Sci., 25, 73, 10.1021/ci00046a002 · DOI · Paper page
  21. Klopman G., 1997, Mol. Pharmacol., 52, 334, 10.1124/mol.52.2.323 · DOI
  22. Gilligan P. J., 1992, J. Med. Chem., 35, 4361, 10.1021/jm00101a012 · doi:10.1021/jm00101a012
  23. Liaw A., 2002, R News, 2, 22
  24. Therneau T. M., 1997, An introduction to recursive partitioning using the RPART routines
  25. Nguyen D. V., 2002, Bioinformatics, 18, 50
  26. Barker M., 2003, J. Chemometrics, 17, 173, 10.1002/cem.785 · DOI · Paper page
  27. Guyon I., 2003, J. Machine Learning Res., 3, 1182
  28. Svetnik V., 2004, Proceedings of the 7th Course on Ensemble Methods for Learning Machines
  29. Willett P, 1998, J. Chem. Inf. Comput. Sci., 38, 996, 10.1021/ci9800211 · DOI · Paper page
  30. Liaw A., 2003, 26th Annual Midwest Biopharmaceutical Statistics Workshop; Muncie, IN
  31. Breiman L., 2003, Manual-Setting up, using, and understanding Random Forests
  32. Ripley B. D., 1996, Pattern Recognition and Neural Networks, 10.1017/CBO9780511812651 · DOI · Paper page
  33. Breiman L, 1998, Ann. Stat., 26, 849, 10.1214/aos/1024691079 · DOI · Paper page
  34. Friedman J. H., 1997, Data Mining Knowledge Discovery, 1, 77, 10.1023/A:1009778005914 · DOI · Paper page
  35. Domingos P., 2000, Proceedings of the Seventeenth National Conference on Artificial Intelligence; AAAI Press: Austin
  36. Valentini G., 2002, Third International Workshop on Multiple Classifier Systems; Kittler, J., Roli, F., Eds.; Springer: New York

Cited by

Metadata

Authors
Svetnik, V., Liaw, A., Tong, C., Culberson, J. C., Sheridan, R. P., & Feuston, B. P.
Venue
Journal of Chemical Information and Computer Sciences
Publisher
American Chemical Society (ACS)
Published
2003-11-01
Volume
43
Issue
6
Pages
1947-1958
ISSN
0095-2338 (ISSN-L)
DOI
10.1021/ci034160g
OpenAlex
W1988195734
Cited by
3,573
References
3
FWCI
18.21
Type
journal article
Language
en
Open access
closed

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