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. Our preliminary study shows a positive result.
CITATION STYLE
Huang, K., & Nogueira, R. (2020). EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection. In Studies in Computational Intelligence (Vol. 843, pp. 187–191). Springer Verlag. https://doi.org/10.1007/978-3-030-24409-5_19
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