Stable gene selection from microarray data via sample weighting

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 9, Issue 1, 2012, Pages 262-272

  Yu, Lei ^a   Han, Yue ^a   Berens, Michael E ^b  

^a State University of New York at Binghamton   (United States)

^b TRANSLATIONAL GENOMICS RESEARCH INSTITUTE   (United States)

Author keywords

classification; Feature selection; gene expression microarray; gene selection; stability

Indexed keywords

CANDIDATE GENES; CLASSIFICATION PERFORMANCE; COMMON PROBLEMS; ENSEMBLE METHODS; FEATURE RELEVANCE; FEATURE SELECTION ALGORITHM; FEATURE SELECTION METHODS; GENE EXPRESSION DATA; GENE EXPRESSION MICROARRAY; GENE SELECTION; HIGH CONFIDENCE; MICROARRAY DATA; MICROARRAY DATA SETS; PREDICTIVE ABILITIES; RELIEFF; SAMPLE WEIGHTING; SELECTION METHODS; SVM-RFE; TRAINING SETS;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); CONVERGENCE OF NUMERICAL METHODS; GENE EXPRESSION; STABILITY;

FEATURE EXTRACTION;

ARTICLE; BIOLOGICAL MODEL; BIOLOGY; DATA MINING; DNA MICROARRAY; GENE; GENE EXPRESSION PROFILING; GENETICS; HUMAN; METABOLISM; METHODOLOGY; NEOPLASM; SUPPORT VECTOR MACHINE; PROCEDURES;

COMPUTATIONAL BIOLOGY; DATA MINING; GENE EXPRESSION PROFILING; GENES; HUMANS; MODELS, GENETIC; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; SUPPORT VECTOR MACHINES;

EID: 81455132651     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2011.47     Document Type: Article

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