Chromatin accessibility prediction via a hybrid deep convolutional neural network.

ABSTRACT:

Motivation

A majority of known genetic variants associated with human-inherited diseases lie in non-coding regions that lack adequate interpretation, making it indispensable to systematically discover functional sites at the whole genome level and precisely decipher their implications in a comprehensive manner. Although computational approaches have been complementing high-throughput biological experiments towards the annotation of the human genome, it still remains a big challenge to accurately annotate regulatory elements in the context of a specific cell type via automatic learning of the DNA sequence code from large-scale sequencing data. Indeed, the development of an accurate and interpretable model to learn the DNA sequence signature and further enable the identification of c

SUBMITTER: Liu Q 

PROVIDER: S-EPMC6192215 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature