  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.
