Epistasis analysis using artificial intelligence.
Here we introduce artificial intelligence (AI) methodology for detecting and characterizing epistasis in genetic association studies. The ultimate goal of our AI strategy is to analyze genome-wide genetics data as a human would using sources of expert knowledge as a guide. The methodology presented here is based on computational evolution, which is a type of genetic programming. The ability to generate interesting solutions while at the same time learning how to solve the problem at hand distinguishes computational evolution from other genetic programming approaches. We provide a general overview of this approach and then present a few examples of its application to real data.
Department of Genetics, Geisel School of Medicine, DHMC, One Medical Center Dr., HB 7937, Lebanon, NH, 03756, USA, Jason.H.Moore@Dartmouth.edu.
Jason H
JH
Moore
Doug P
DP
Hill
D000544:Alzheimer Disease / Q000235:genetics; D001185:Artificial Intelligence; D004843:Epistasis, Genetic; D005901:Glaucoma / Q000235:genetics; D006801:Humans; D008297:Male; D008957:Models, Genetic; D009154:Mutation / Q000235:genetics; D011471:Prostatic Neoplasms / Q000235:genetics; D012984:Software