Classifying gene expression data of cancer using classifier ensemble with mutually exclusive features

Proceedings of the IEEE

Volumn 90, Issue 11, 2002, Pages 1744-1753

  Cho, Sung Bae ^a   Ryu, Jungwon ^a  

^a Yonsei University   (South Korea)

Author keywords

Cancer classification; Classifier ensemble with mutually exclusive features; Gene expression classification; Neural network ensemble classifier

Indexed keywords

DATABASE SYSTEMS; DNA SEQUENCES; GENES; INFORMATION TECHNOLOGY; PROTEINS; TUMORS;

CANCER CLASSIFICATION; CLASSIFIER ENSEMBLE WITH MUTUALLY EXCLUSIVE FEATURES; GENE EXPRESSION CLASSIFICATION; NEURAL NETWORK ENSEMBLE CLASSIFIER;

GENETIC ENGINEERING;

EID: 0141686299     PISSN: 00189219     EISSN: None     Source Type: Journal    
DOI: 10.1109/JPROC.2002.804682     Document Type: Article

References (29)

1. T. R. Golub, "Molecular classification of cancer: Class discovery and class prediction by gene expression monitorins," Science, vol. 286, pp. 531-537, 1999.
2. P. Tamayo, "Interpreting patterns of gene expression with self-organizing map: Methods and application to hematopoietic differentiation," in Proc. Nat. Acad. Sci. USA, vol. 96, 1999, pp. 2907-2912.
3. M. West, J. R. Nevins, J. R. Marks, R. Spang, C. Blanchette, and H. Zuzan, "DNA microarray data analysis and regression modeling for genetic expression profiling," Institute of Statistics and Decision Sciences, Duke University, Chapel Hill, NC, Tech. Rep. 00-15, 2001.
4. 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, vol. 16, no. 10, pp. 906-914, 2000.
5. W. Li and Y. Yang, "How many genes are needed for a discriminant microarray data analysis," in Proc. Critical Assessment of Techniques for Microarray Data Mining Workshop, Dec. 2000.
6. A. Ben-Dor, L. Bruhn, N. Friedman, I. Nachman, M. Schummer, and N. Yakhini, "Tissue classification with gene expression profiles," J. Comput. Biol., vol. 7, pp. 559-584, 2000.
7. S. Dudoit, J. Fridlyand, and T. P. Speed, "Comparison of discrimination methods for the classification of tumors using gene expression data," Department of Statistics, University of California, Berkeley, Tech. Rep. 576, June 2000.
8. D. V. Nguyen and D. M. Rocke, "Tumor classification by partial least squares using microarray gene expression data," Bioinformatics, 2002, to be published.
9. J. Ryu and S.-B. Cho, "Toward optimal feature and classifier for gene expression classification of cancer," Lecture Notes in Artificial Intelligence, vol. 2275, pp. 310-317, 2002.
10. D. Lashkari, J. Derisi, J. McCusker, A. Namath, C. Gentile, S. Hwang, P. Brown, and R. Davis, "Yeast microarrays for genome wide parallel genetic and gene expression analysis," in Proc. Nat. Acad. Sci. USA, vol. 94, 1997, pp. 13057-13062.
11. J. Derisi, V. Iyer, and P. Brosn, "Exploring the metabolic and genetic control of gene expression on a genomic scale," Science, vol. 278, pp. 680-686, 1997.
12. M. Eisen, P. Spellman, P. Brown, and D. Bostein, "Cluster analysis and display of genome-wide expression patterns," in Proc. Nat. Acad. Sci. USA, vol. 95, 1998, pp. 14863-14868.
13. M. Eisen, P. Spellman, P. Brown, and D. Bostein, "Cluster analysis and display of genome-wide expression patterns," in Proc. Nat. Acad. Sci. USA, vol. 95, 1998, pp. 14863-14868.
14. D. N. Osherson, S. Weinstein, and M. Stob, "Modular learning," in Computational Neuroscience, E. L. Schwartz, Ed. Cambridge, MA: MIT Press, 1990, pp. 369-377.
15. J. Pollack, "Cascaded back-propagation on dynamic connectionist networks," in Proc. Ninth Ann. Conf. Cognitive Sci. Soc., 1987, pp. 391-404.
16. R. Jacobs, "Initial experiments on constructing domains of expertise and hierarchies in connectionist systems," in Proc. Connectionist Models Summer School, San Mateo, CA, 1988, pp. 144-153.
17. J. B. Hampshire and A. Waibel, "The Meta-Pi network: Building distributed knowledge representations for robust multisource pattern recognition," IEEE Trans. Pattern Anal. Machine Intell., vol. 14, pp. 751-769, July 1992.
18. W. P. Lincoln and J. Skrzypek, "Synergy of clustering multiple backpropagation networks," in Advances in Neural Information Processing Systems, D. S. Touretzky, Ed. San Mateo, CA: Morgan Kauffmann, 1990, vol. 1, pp. 650-657.
19. R. Battiti and A. M. Colla, "Democracy in neural nets: Voting schemes for classification," Neural Networks, vol. 7, pp. 691-707, July 1994.
20. S. Gutta, R. J. Huang, P. Jonathon, and H. Wechsler, "Mixture of experts for classification for gender, ethnic origin, and pose of human faces," IEEE Trans. Neural Networks, vol. 11, pp. 948-960, July 2000.
21. Y. Liu and X. Yao, "Ensemble learning via negative correlation," Neural Networks, vol. 12, pp. 1399-1404, Dec. 1999.
22. _, "Evolutionary ensembles with negative correlation learning," IEEE Trans. Evol. Comput., vol. 4, pp. 1399-1404, Nov. 2000.
23. R. P. Lippman, "An introduction to computing with neural nets," IEEE Acoust., Speech, Signal Processing Mag., pp. 4-22, Apr. 1987.
24. H. D. Beale, Neural Network Design: PWS, 1996, vol. 11, pp. 1-17.
25. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer, 1995.
26. B. Moghaddam and M.-H. Yang, "Gender classification with support vector machines," in Proc. 4th IEEE Int. Conf. Automatic Face and Gesture Recognition, 2000, pp. 306-311.
27. B. Moghaddam and M.-H. Yang, "Gender classification with support vector machines," in Proc. 4th IEEE Int. Conf. Automatic Face and Gesture Recognition, 2000, pp. 306-311.
28. J. Ryu and S.-B. Cho, "Gender recognition of human behaviors using neural network ensembles," in Proc. IEEE-INNS Int. Joint Conf. on Neural Networks, Washington, DC, July 2001, pp. 571-576.
29. U. Alon et al., "Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays," in Proc. Nat. Acad. Sci. USA, vol. 96, 1999, pp. 6745-6750.