  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.
