EPIHC: Improving Enhancer-Promoter Interaction Prediction by using Hybrid features and Communicative learning

TL;DR

EPIHC is a deep learning method that combines sequence and genomic features with communicative learning to improve enhancer-promoter interaction prediction, outperforming existing methods on benchmark datasets.

Contribution

The paper introduces a novel communicative learning module and hybrid feature integration in a deep neural network for more accurate EPI prediction.

Findings

EPIHC outperforms state-of-the-art methods on benchmark datasets.

Communicative learning captures explicit enhancer-promoter interaction information.

Cross-cell line prediction is improved with strategies based on training on multiple cell lines.

Abstract

Enhancer-promoter interactions (EPIs) regulate the expression of specific genes in cells, and EPIs are important for understanding gene regulation, cell differentiation and disease mechanisms. EPI identification through the wet experiments is costly and time-consuming, and computational methods are in demand. In this paper, we propose a deep neural network-based method EPIHC based on sequence-derived features and genomic features for the EPI prediction. EPIHC extracts features from enhancer and promoter sequences respectively using convolutional neural networks (CNN), and then design a communicative learning module to captures the communicative information between enhancer and promoter sequences. EPIHC also take the genomic features of enhancers and promoters into account. At last, EPIHC combines sequence-derived features and genomic features to predict EPIs. The computational experiments show that EPIHC outperforms the existing state-of-the-art EPI prediction methods on the benchmark datasets and chromosome-split datasets, and the study reveal that the communicative learning module can bring explicit information about EPIs, which is ignore by CNN. Moreover, we consider two strategies to improve performances of EPIHC in the cross-cell line prediction, and experimental results show that EPIHC constructed on training cell lines exhibit improved performances for the other cell lines.