EPnet: A general network to predict enhancer-promoter interactions

Abstract

The mechanism of spatio-temporal gene expression is significantly related to the interaction between the two regulatory elements on the DNA, enhancer and promoter. Identifying enhancer-promoter interactions that disrupt cell-specific gene expression and cause different human diseases remains to be a great challenge. To figure this out, we construct a sequence-based deep learning model, Enhancer-Promoter interactions prediction network, briefly called the EPnet which accurately predicts the interaction between enhancer and promoter with given DNA sequences. The method we proposed requires no genomic data which makes it convenient to make predictions. Comparison with other existing methods and application on predicting interactions show that our method is of superior performance in multiple cell lines which proves that our model is trustworthy and robust.