Exploring genetic variability in drug therapy by selecting a minimum subset of the most informative single nucleotide polymorphisms through approximation of a markov blanket in a kernel-induced space

2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012

Volumn , Issue , 2012, Pages 156-163

  Lou, Qiang ^a   Parkman, Henry P ^b   Jacobs, Michael R ^a   Krynetskiy, Evgeny ^a   Obradovic, Zoran ^a  

^a TEMPLE UNIVERSITY   (United States)

^b TEMPLE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

drug therapy; feature selection; genetic variability; SNP

Indexed keywords

AFFYMETRIX; CLINICAL EFFICACY; DNA MICRO-ARRAY; DNA SAMPLES; DOMPERIDONE; EXPERIMENTAL EVALUATION; FEATURE SELECTION METHODS; GENETIC VARIABILITY; HIGH DIMENSIONAL DATA; MARKOV BLANKETS; MICROARRAY DATASET; MINIMUM SUBSETS; SIDE EFFECT; SINGLE-NUCLEOTIDE POLYMORPHISMS; SNP;

ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; DATA REDUCTION; FEATURE EXTRACTION; GENES; NUCLEOTIDES; PATIENT MONITORING;

DRUG THERAPY;

EID: 84864051685     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIBCB.2012.6217225     Document Type: Conference Paper

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