A model-free greedy gene selection for microarray sample class prediction

Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'06

Volumn , Issue , 2006, Pages 406-413

  Shi, Yi ^a   Cai, Zhipeng ^a   Xu, Lizhe ^b   Ren, Wei ^c   Goebel, Randy ^a   Lin, Guohui ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

^b UNIVERSITY OF NEW ORLEANS   (United States)

^c CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Discriminatory gene; Gene selection; Greedy; Microarray data analysis; Sample class prediction

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOCOMMUNICATIONS; BIOINFORMATICS; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; DATA REDUCTION; GENE EXPRESSION; GENES; INFORMATION SCIENCE; INTELLIGENT CONTROL; LEARNING SYSTEMS; LIGHT MEASUREMENT; STATISTICAL METHODS; SUPPORT VECTOR MACHINES;

CLASS PREDICTION; COMPUTATIONAL BIOLOGY; COMPUTATIONAL INTELLIGENCE; COMPUTATIONALLY EFFICIENT; DIFFERENTIALLY EXPRESSED GENES; DISCRIMINATORY GENE; EXPRESSION DATA; GENE EXPRESSION MICROARRAY TECHNOLOGY; GENE SELECTION; GREEDY; K-NEAREST NEIGHBOR; MICRO ARRAYS; MICROARRAY DATA ANALYSIS; MICROARRAY DATASETS; MODEL-FREE; SAMPLE CLASS PREDICTION; STATISTICAL DISTRIBUTIONS; SUPPORT-VECTOR MACHINE;

COMPUTATIONAL METHODS;

EID: 50249161876     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIBCB.2006.330965     Document Type: Conference Paper

References (13)

1. S. Dudoit, J. Fridlyand, and T. P. Speed. Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association, 97:77-87, 2002.
2. C. Ding and H. Peng. Minimum redundancy feature selection from microarray gene expression data. In Proceedings of IEEE Computer Society Bioinformatics Conference (CSB'03), pages 523-530, 2003.
3. J. Cho, D. Lee, J. H. Park, and I. B. Lee. New gene selection for classification of cancer subtype considering within-class variation. FEBS Letters, 551:3-7, 2003.
4. K. Yang, Z. Cai, J. Li, and G.-H. Lin. A stable gene selection in microarray data analysis. BMC Bioinformatics, 7:228, 2006.
5. H. Liu and L. Yu. Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17:491-502, 2005.
6. P. Baldi and A. D. Long. A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inferences of gene changes. Bioinformatics, 17:509-519, 2001.
7. O. G. Troyanskaya, M. E. Garber, P. O. Brown, D. Botstein, and R. B. Altman. Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics, 18:1454-1461, 2002.
8. M. Xiong, X. Fang, and J. Zhao. Biomarker identification by feature wrappers. Genome Research, 11:1878-1887, 2001.
9. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Machine Learning, 46:389-422, 2002.
10. P. Pudil, J. Novovicova, and J. Kittler. Floating search methods for feature selection. Pattern Recognition Letters, 15:1119-1125, 1994.
11. C. L. Nutt, D. R. Mani, R. A. Betensky, P. Tamayo, J. G. Cairncross, C. Ladd, U. Pohl, C. Hartmann, M. E. McLaughlin, T. T. Batchelor, P. M. Black, A. V. Deimling, S. L. Pomeroy, T. R. Golub, and D. N. Louis. Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Research, 63:1602-1607, 2003.
12. A. Bhattacharjee, W. G. Richards, J. Staunton, C. Li, S. Monti, P. Vasa, C. Ladd, J. Beheshti, R. Bueno, M. Gillette, M. Loda, G. Weber, E. J. Mark, E. S. Lander, W. Wong, B. E. Johnson, T. R. Golub, D. J. Sugarbaker, and M. Meyerso. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proceedings of National Academy of Sciences of the United States of America, 98:13790-13795, 2001.
13. J. Khan, J. S. Wei, M. Ringner, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, and P. S. Meltzer. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine, 7:673-679, 2001.