DeepEPI: CNN-transformer-based model for extracting TF interactions through predicting enhancer-promoter interactions.

ABSTRACT:

Motivation

We introduce DeepEPI, a deep learning framework for studying enhancer-promoter interactions (EPIs) directly from genomic sequences. By integrating convolutional neural networks (CNNs) with Transformer blocks, DeepEPI captures the complex regulatory interplay between enhancers and promoters, a key factor in gene expression and disease mechanisms. The model emphasizes interpretability and efficiency by employing embedding layers for OneHot encoding and multihead attention for detecting and analyzing transcription factor (TF) interactions. A DNA2Vec-based version of DeepEPI is also evaluated.

Results

DeepEPI is assessed in two dimensions: comparison with existing models and analysis of encoding methods. Across six cell lines, DeepEPI consistently outperforms prior ap

SUBMITTER: Tabatabaei SF 

PROVIDER: S-EPMC12478696 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature