An analytical method for multiclass molecular cancer classification

SIAM Review

Volumn 45, Issue 4, 2003, Pages 706-723

  Rifkin, Ryan ^a,d   Mukherjee, Sayan ^a,d   Tamayo, Pablo ^a   Ramaswamy, Sridhar ^a,b   Yeang, Chen Hsiang ^a,e   Angelo, Michael ^f   Reich, Michael ^a   Poggio, Tomaso ^d   Lander, Eric S ^a,c   Golub, Todd R ^a,b   Mesirov, Jill P ^a  

^a WHITEHEAD INSTITUTE FOR BIOMEDICAL RESEARCH   (United States)

^b DANA FARBER CANCER INSTITUTE   (United States)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^d MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^e Department of Electrical Engineering and Computer Science   (United States)

^f X Mine   (United States)

Author keywords

Cancer; Computational biology; Molecular classification; Multiclass classification; Pattern recognition; Support vector machine; Tumor

Indexed keywords

ALGORITHMS; DNA; FUNCTIONS; GENES; LEARNING SYSTEMS; MATRIX ALGEBRA; PROBLEM SOLVING; TUMORS;

COMPUTATIONAL BIOLOGY; MULTICLASS MOLECULAR CANCER CLASSIFICATION; RECURSIVE FEATURE ELIMINATION; SUPPORT VECTOR MACHINE;

ONCOLOGY;

EID: 1642502427     PISSN: 00361445     EISSN: None     Source Type: Journal    
DOI: 10.1137/S0036144502411986     Document Type: Article

References (33)

1. E. L. Allwein, R. Schapire, and Y. Singer, Reducing multiclass to binary: A unifying approach for margin classifiers, J. Mach. Learn. Res., 1 (2001), pp. 113-141.
2. N. Aronszajn, Theory of reproducing kernels, Trans. Amer. Math. Soc., 686 (1950), pp. 337-404.
3. K. J. Arrow, Social Choice and Individual Values, Wiley, New York, 1951.
4. A. Ben-Dor, L. Bruhn, N. Friedman, I. Nachman, M. Schummer, and Z. Yakhini, Tissue classification with gene expression profiles, J. Comput. Biology, 7 (2000), pp. 559-584.
5. R. C. Bose and D. K. Ray-Chaudhuri, On a class of error-correcting binary group codes. Inform. and Control, 3 (1960), pp. 68-79.
6. M. P. Brown, W. N. Grundy, D. Lin, N. Christianini, C. Sugnet, M. Ares, Jr., and D. Haussler, Support vector machine classification of microarray gene expression data, Proc. Natl. Acad. Sci. USA, 97 (2000), pp. 262-267.
7. A. Califano, G. Stolovitzky, and Y. Tu, Analysis of gene expression microarrays for phenotype classification, in Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, San Diego, CA, 1999, pp. 75-85.
8. Chipping Forecast, Nature Genetics, 21 (1999) Supplement.
9. Chipping Forecast, Nature Genetics, 32 (2002), Supplement.
10. T. Dietterich and S. Bakiri, Error-correcting output codes: A general method for improving multiclass inductive programs, in Proceedings of the Ninth AAAI National Conference on Artificial Intelligence, AAAI Press, Menlo Park, CA, 1991, pp. 572-577.
11. E. Dougherty, Pattern recognition and gene expression, SIAM News, May 2002; available online from http://www.siam.org/siamnews/05-02/genes.htm.
12. D. Duggan, M. Bittner, Y. Chen, P. Meltzer, and J. M. Trent, Expression profiling using cDNA microarrays, Nature Genetics Suppl., 21 (1999), pp. 10-14.
13. B. Efron and R. Tibshirani, Introduction to the Boostrap, Chapman and Hall, New York, London, 1983.
14. T. Evgeniou, M. Pontil, and T. Poggio, Regularization networks and support vector machines, Adv. Comput. Math., 13 (2000), pp. 1-50.
15. T. S. Furey, N. Cristianini, N. Duffy, D. W. Bednarski, M. Schummer, and D. Haussler, Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics, 16 (2000), pp. 906-914.
16. T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caliguiri, C. D. Bloomfield, and E. S. Lander, Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring, Science, 286 (1999), pp. 531-537.
17. V. Guruswami and A. Sahai, Multiclass learning, boosting and error-correcting codes, in Proceedings of the Twelfth Annual Conference on Computational Learning Theory, ACM Press, New York, 1999, pp. 145-155.
18. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines, Machine Learning, 46 (2002), pp. 389-422.
19. T. Haste and R. Tibshirani, Classification by pairwise coupling, in Advances on Neural Processing Systems 10, MIT Press, Cambridge, MA, 1998, pp. 507-513.
20. R. J. Lipschutz, S. P. A. Fodor, T. R. Gingeras, and D. J. Lockhart, High density synthetic oligonucleotide arrays, Nature Genetics Supp., 21 (1999), pp. 20-24.
21. P. Michaud, Condorcet: A man of the avant-garde, Appl. Stoch. Models Data Anal., 3 (1987), pp. 173-189.
22. M. Minsky and S. Papert, Perceptrons. An Introduction to Computational Geometry, MIT Press, Cambridge, MA, 1972.
23. S. Mukherjee and R. Rifkin, Support Vector Machine Classification of Microarray Data, CBCL Paper 182, Artificial Intelligence Lab. Memo 1676, MIT, Cambridge, MA, 1999.
24. S. Ramaswamy and T. R. Golub, DNA microarrays in clinical oncology J. Clinical Oncology, 20 (2001), pp. 711-719.
25. S. Ramaswamy, P. Tamayo, R. Rifkin, C. Yeang, M Angelo, C. Ladd, M. Loda, E. Lander, and T. Golub, A uniform approach to molecular cancer diagnosis using tumor gene expression signatures, Proc. Natl. Acad. Sci. USA, 98 (2002), pp. 15149-15154.
26. F. Rosenblatt, Principles of Neurodynamics, Spartan Books, New York, 1962.
27. R. Schapire, Y. Freund, P. Bartlett, and W. S. Lee, Boosting the margin: A new explanation for the effectiveness of voting methods, Ann. Statist., 26 (1998), pp. 1651-1686.
28. R. Schapire, A brief introduction to boosting, in Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, Morgan-Kaufmann, San Francisco, 1999, pp. 1401-1406.
29. D. K. Slonim, P. Tamayo, J. P. Mesirov, T. R. Golub, and E. S. Lander, Class prediction and discovery using gene expression data, in Proceedings of the Fourth Annual International Conference on Computational Molecular Biology (RECOMB), Universal Academy Press, Tokyo, Japan, 2000, pp. 263-272.
30. A. N. Tikhonov and V. Y. Arsenin, Solutions of Ill-Posed Problems, W. H. Winston, Washington, DC, 1977.
31. V. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
32. J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik, Feature selection for SVMs, in Advances on Neural Processing Systems 13, MIT Press, Cambridge, MA, 2001, pp. 668-674.
33. C. Yeang, S. Ramaswamy, P. Tamayo, R. Rifkin, M. Angelo, M. Reich, E. Lander, J. Mesirov, and T. Golub, Molecular classification of multiple tumor types, Bioinformatics Discovery Note, 1 (2001), pp. 1-7.