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Enhancer-promoter interactions (EPIs) regulate the expression of specificgenes in cells, and EPIs are important for understanding gene regulation, celldifferentiation and disease mechanisms. EPI identification through the wetexperiments is costly and time-consuming, and computational methods are indemand. In this paper, we propose a deep neural network-based method EPIHCbased on sequence-derived features and genomic features for the EPI prediction.EPIHC extracts features from enhancer and promoter sequences respectively usingconvolutional neural networks (CNN), and then design a communicative learningmodule to captures the communicative information between enhancer and promotersequences. EPIHC also take the genomic features of enhancers and promoters intoaccount. At last, EPIHC combines sequence-derived features and genomic featuresto predict EPIs. The computational experiments show that EPIHC outperforms theexisting state-of-the-art EPI prediction methods on the benchmark datasets andchromosome-split datasets, and the study reveal that the communicative learningmodule can bring explicit information about EPIs, which is ignore by CNN.Moreover, we consider two strategies to improve performances of EPIHC in thecross-cell line prediction, and experimental results show that EPIHCconstructed on training cell lines exhibit improved performances for the othercell lines.

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