Local Alignment of DNA Sequence Based on Deep Reinforcement Learning.

ABSTRACT: Goal: Over the decades, there have been improvements in the sequence alignment algorithm, with significant advances in various aspects such as complexity and accuracy. However, human-defined algorithms have an explicit limitation in view of developmental completeness. This paper introduces a novel local alignment method to obtain optimal sequence alignment based on reinforcement learning. Methods: There is a DQNalign algorithm that learns and performs sequence alignment through deep reinforcement learning. This paper proposes a DQN x-drop algorithm that performs local alignment without human intervention by combining the x-drop algorithm with this DQNalign algorithm. The proposed algorithm performs local alignment by repeatedly observing the subsequences and selecting the nex

SUBMITTER: Song YJ 

PROVIDER: S-EPMC8975175 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature