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EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection (1809.09143v1)
Published 24 Sep 2018 in cs.LG, q-bio.QM, and stat.ML
Abstract: Epistasis (gene-gene interaction) is crucial to predicting genetic disease. Our work tackles the computational challenges faced by previous works in epistasis detection by modeling it as a one-step Markov Decision Process where the state is genome data, the actions are the interacted genes, and the reward is an interaction measurement for the selected actions. A reinforcement learning agent using policy gradient method then learns to discover a set of highly interacted genes.
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