Prediction of Gene Co-expression from Chromatin Contacts with Graph Attention Network.

ABSTRACT:

Motivation

The technology of high-throughput chromatin conformation capture (Hi-C) allows genome-wide measurement of chromatin interactions. Several studies have shown statistically significant relationships between gene-gene spatial contacts and their co-expression. It is desirable to uncover epigenetic mechanisms of transcriptional regulation behind such relationships using computational modeling. Existing methods for predicting gene co-expression from Hi-C data use manual feature engineering or unsupervised learning, which either limits the prediction accuracy or lacks interpretability.

Results

To address these issues, we propose HiCoEx, a novel end-to-end framework for explainable prediction of gene co-expression from Hi-C data based on graph neural network. We apply gra

SUBMITTER: Zhang K 

PROVIDER: S-EPMC9525008 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature