Multiple sequence alignment based on deep reinforcement learning with self-attention and positional encoding.

ABSTRACT:

Motivation

Multiple sequence alignment (MSA) is one of the hotspots of current research and is commonly used in sequence analysis scenarios. However, there is no lasting solution for MSA because it is a Nondeterministic Polynomially complete problem, and the existing methods still have room to improve the accuracy.

Results

We propose Deep reinforcement learning with Positional encoding and self-Attention for MSA, based on deep reinforcement learning, to enhance the accuracy of the alignment Specifically, inspired by the translation technique in natural language processing, we introduce self-attention and positional encoding to improve accuracy and reliability. Firstly, positional encoding encodes the position of the sequence to prevent the loss of nucleotide position informa

SUBMITTER: Liu Y 

PROVIDER: S-EPMC10628385 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature