Prediction of enhancer-promoter interactions via natural language processing.

ABSTRACT:

Background

Precise identification of three-dimensional genome organization, especially enhancer-promoter interactions (EPIs), is important to deciphering gene regulation, cell differentiation and disease mechanisms. Currently, it is a challenging task to distinguish true interactions from other nearby non-interacting ones since the power of traditional experimental methods is limited due to low resolution or low throughput.

Results

We propose a novel computational framework EP2vec to assay three-dimensional genomic interactions. We first extract sequence embedding features, defined as fixed-length vector representations learned from variable-length sequences using an unsupervised deep learning method in natural language processing. Then, we train a classifier to predict EPIs

SUBMITTER: Zeng W 

PROVIDER: S-EPMC5954283 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature