Fast gene selection for microarray data using SVM-based evaluation criterion

Proceedings - IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2008

Volumn , Issue , 2008, Pages 386-389

  Zhou, Xin ^a   Wu, X Y ^b   Mao, K Z ^c   Tuck, David P ^a  

^a YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b YALE UNIVERSITY   (United States)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

BIOASSAY; BIOINFORMATICS; COMPUTATIONAL EFFICIENCY; GEARS; GENE EXPRESSION; GENES; IMAGE RETRIEVAL; MICROARRAYS; MULTILAYER NEURAL NETWORKS; VECTORS;

ADAPTIVE THRESHOLDS; CLASSIFICATION PERFORMANCES; EVALUATION CRITERIONS; FEATURE SUBSETS; GENE EXPRESSION DATUMS; GENE SELECTION ALGORITHMS; GENE SELECTIONS; HIGH DIMENSIONALITIES; IRRELEVANT FEATURES; MICROARRAY DATUMS; PREDICTIVE CAPACITIES; SEARCH PROCEDURES; SELECTION PROCEDURES; SUPPORT VECTORS;

SUPPORT VECTOR MACHINES;

EID: 58049148203     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBM.2008.57     Document Type: Conference Paper

References (5)

1. M. Dash and H. Liu. Feature selection for classification. Intelligent Data Analysis, 1:131-156, 1997.
2. T. Golub, et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286:531-537, 1999.
3. I. Guyon, et al. Gene selection for cancer classification using support vector machines. Machine Learning, 46:389-422, 2002.
4. V. N. Vapnik. Statistical Learning Theory. John Wiley and Sons, New York, 1998.
5. M. West, et al. Predicting the clinical status of human breast cancer by using gene expression profiles. Proceedings of the National Academy of Sciences, 98(20):11462-11467, 2001.