Prediction of enhancer-promoter interactions using the cross-cell type information and domain adversarial neural network.

ABSTRACT:

Background

Enhancer-promoter interactions (EPIs) play key roles in transcriptional regulation and disease progression. Although several computational methods have been developed to predict such interactions, their performances are not satisfactory when training and testing data from different cell lines. Currently, it is still unclear what extent a across cell line prediction can be made based on sequence-level information.

Results

In this work, we present a novel Sequence-based method (called SEPT) to predict the enhancer-promoter interactions in new cell line by using the cross-cell information and Transfer learning. SEPT first learns the features of enhancer and promoter from DNA sequences with convolutional neural network (CNN), then designing the gradient reversal layer

SUBMITTER: Jing F 

PROVIDER: S-EPMC7648314 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature