Kernel based gene expression pattern discovery and its application on cancer classification

Neurocomputing

Volumn 73, Issue 13-15, 2010, Pages 2562-2570

  Cai, Ruichu ^a,b   Hao, Zhifeng ^a   Wen, Wen ^a   Huang, Han ^b,c  

^a GUANGDONG UNIVERSITY OF TECHNOLOGY   (China)

^b SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^c NANJING UNIVERSITY   (China)

Author keywords

Association rule; Cancer classification; Gene expression; Kernel density estimation

Indexed keywords

CANCER CLASSIFICATION; CLASSIFICATION MODELS; CONDITIONAL INDEPENDENCES; GENE EXPRESSION DATA; GENE EXPRESSION DATA ANALYSIS; GENE EXPRESSION PATTERNS; HIGH-DIMENSIONAL; INTERESTING RULES; INTERESTINGNESS; INTERESTINGNESS MEASURES; KERNEL DENSITY ESTIMATION; OVERFITTING; PRUNING STRATEGY; REDUNDANT RULES;

ASSOCIATION RULES; ASSOCIATIVE PROCESSING; DATA REDUCTION; ESTIMATION;

GENE EXPRESSION;

ALGORITHM; ARTICLE; CANCER CLASSIFICATION; COMPUTER SIMULATION; CONCEPTUAL FRAMEWORK; DATA MINING; ENTROPY; GENE EXPRESSION; GENE REGULATORY NETWORK; GENETIC DATABASE; HUMAN; KERNEL METHOD; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PRIORITY JOURNAL;

EID: 77955319589     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.05.019     Document Type: Article

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