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156 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

By Qiang Lou, Henry P. Parkman, Michael R. Jacobs, Evgeny Krynetskiy and Zoran Obradovic


Abstract—Genome-wide analysis of single nucleotide polymorphisms (SNP) can potentially be helpful in exploring the role of genetic variability in drug therapy. However, two major problems with such an analysis are the need for a large number of interrogated genomes, and the resulting high-dimensional data where the number of SNPs used as features is much larger than the number of subjects. The aim of this study is to identify informative SNPs associated with clinical efficacy and side effects of domperidone treatment for gastroparesis from DNA microarray experiments by applying our feature selection method, which approximates the Markov Blanket in a kernelinduced space. DNA samples extracted from the saliva of 46 patients treated with domperidone were analyzed using Affymetrix 6.0 SNP microarrays. Experimental evaluations on this SNP microarray dataset provide evidence that our feature selection method can remove useless SNP features more accurately than existing Markov Blanket based alternatives. Keywords-genetic variability; feature selection; drug therapy; SNP I

Year: 2013
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