August 20, 2025Open Access

EPIFBMC: A New Model for Enhancer–Promoter Interaction Prediction

CBChengfeng BaoNortheast Forestry University GWGang WangAnhui University GSGuojun ShengKumamoto University

Key Points

The new model achieves significant accuracy in predicting enhancer–promoter interactions, enhancing understanding of gene regulation.
Evaluation on three cell line datasets demonstrates improved performance compared to existing methods, underscoring its generalizability.
The EPIFBMC framework integrates deep learning with genomic features, optimizing the learning process for DNA sequence data.
The findings may aid developmental biology and disease research, highlighting the model's relevance in therapeutic applications.

Abstract

Enhancer–promoter interactions (EPIs) play a key role in epigenetic regulation of gene expression, dominating cellular identity and functional diversity. Dissecting these interactions is crucial for understanding transcriptional regulatory networks and their significance in cell differentiation, development, and disease. Here, we propose a novel deep learning framework, EPIFBMC (Enhancer-Promoter Interaction prediction with FBMC network) that leverages DNA sequence and genomic features for accurate EPI prediction. The FBMC network consists of three key modules: the Four-Encoding module first encodes the DNA sequence in multiple dimensions to extract key sequence information; then the BESL (Balanced Ensemble Subset Learning) adopts an integrated subset learning strategy to optimize the feature-learning process of positive and negative samples; finally, the MCANet module completes the training of EPI prediction based on a Multi-channel Network. We evaluated EPIFBMC on three cell line datasets (HeLa, IMR90, and NHEK), and validated its generalizability across three independent datasets (K562, GM12878, HUVEC) through cross-cell-line experiments, comparing favorably with state-of-the-art methods. Notably, EPIFBMC balances genomic feature richness and computational complexity, significantly accelerating training speed. Ablation studies identified two key DNA sequence features—positional conservation and positional specificity score—which showed critical predictive value across a benchmark dataset of six diverse cell lines. The computational testing show that EPIFBMC shows excellent performance in the EPI prediction task, providing a powerful tool for decoding gene regulatory networks. It is believed that it will have important application prospects in developmental biology, disease mechanism research, and therapeutic target discovery.

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