IEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models.

ABSTRACT:

Motivation

Enhancers are important cis-regulatory elements that regulate a wide range of biological functions and enhance the transcription of target genes. Although many feature extraction methods have been proposed to improve the performance of enhancer identification, they cannot learn position-related multiscale contextual information from raw DNA sequences.

Results

In this article, we propose a novel enhancer identification method (iEnhancer-ELM) based on BERT-like enhancer language models. iEnhancer-ELM tokenizes DNA sequences with multi-scale k-mers and extracts contextual information of different scale k-mers related with their positions via an multi-head attention mechanism. We first evaluate the performance of different scale k-mers, the

SUBMITTER: Li J 

PROVIDER: S-EPMC10125906 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature