EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection
This work addresses a domain-specific problem in genetics for researchers, but it appears incremental as it applies existing RL methods to epistasis detection.
The authors tackled the computational challenges in epistasis detection by modeling it as a one-step Markov Decision Process and using a reinforcement learning agent with policy gradient to discover highly interacted genes, but no concrete performance numbers are provided.
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.