Article
An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions.
Bioinformatics (Oxford, England) - 1 Oct 2009
Miller David J, Zhang Yanxin, Yu Guoqiang, Liu Yongmei, Chen Li, Langefeld Carl D, Herrington David, Wang Yue
Abstract excerpt
MOTIVATION: In both genome-wide association studies (GWAS) and pathway analysis, the modest sample size relative to the number of genetic markers presents formidable computational, statistical and methodological challenges for accurately identifying markers/interactions and for building phenotype-predictive models. RESULTS: We address these objectives via maximum entropy conditional probability modeling (MECPM),...
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