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Deepepi: Cnn-transformer-based Model For Extracting Tf Interactions Through Predicting Enhancer-promoter Interactions.

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Deepepi: Cnn-transformer-based Model For Extracting Tf Interactions Through Predicting Enhancer-promoter Interactions.

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

Seyedeh Fatemeh Tabatabaei¹, Saeedeh Akbari Roknabadi², Somayyeh Koohi¹

¹Department of Computer Engineering, Sharif University of Technology, Tehran, 11155-9517, Iran.

Bioinformatics Advances

|October 1, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

DeepEPI, a novel deep learning framework, accurately predicts enhancer-promoter interactions (EPIs) by analyzing genomic sequences. This tool enhances gene expression studies and disease mechanism research with improved efficiency and interpretability.

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Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

Published on: June 28, 2018

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Enhancer-promoter interactions (EPIs) are crucial for gene regulation.
Understanding EPIs is key to deciphering gene expression and disease mechanisms.
Existing computational methods for EPI prediction have limitations in accuracy and interpretability.

Purpose of the Study:

To introduce DeepEPI, a deep learning framework for direct prediction of EPIs from genomic sequences.
To evaluate DeepEPI's performance against existing models and assess different encoding methods.
To enhance the interpretability of EPI prediction by analyzing transcription factor (TF) interactions.

Main Methods:

DeepEPI integrates Convolutional Neural Networks (CNNs) with Transformer blocks.
The framework employs embedding layers for OneHot encoding and multihead attention mechanisms.
A DNA2Vec-based version of DeepEPI was also developed and evaluated.

Main Results:

DeepEPI consistently outperformed existing models across six cell lines.
OneHot encoding achieved a 4% increase in AUPR and 1.9% in AUROC compared to prior methods.
DeepEPI successfully extracted biologically relevant TF-TF interactions, aiding experimental validation.

Conclusions:

DeepEPI offers a powerful and interpretable deep learning approach for EPI prediction.
The framework advances the study of gene regulation and disease mechanisms.
DeepEPI provides valuable insights for experimental validation in epigenomic research.