EPI-DynFusion: enhancer-promoter interaction prediction model based on sequence features and dynamic fusion mechanisms.

ABSTRACT:

Introduction

Enhancer-promoter interactions (EPIs) play a vital role in the regulation of gene expression. Although traditional wet-lab methods provide valuable insights into EPIs, they are often constrained by high costs and limited scalability. As a result, the development of efficient computational models has become essential. However, many current deep learning and machine learning approaches utilize simplistic feature fusion strategies, such as direct averaging or concatenation, which fail to effectively model complex relationships and dynamic importance across features. This often results in suboptimal performance in challenging biological contexts.

Methods

To address these limitations, we propose a deep learning model named EPI-DynFusion. This model begins by encoding

SUBMITTER: Zhang A 

PROVIDER: S-EPMC12325019 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature