A simple convolutional neural network for prediction of enhancer-promoter interactions with DNA sequence data.

ABSTRACT:

Motivation

Enhancer-promoter interactions (EPIs) in the genome play an important role in transcriptional regulation. EPIs can be useful in boosting statistical power and enhancing mechanistic interpretation for disease- or trait-associated genetic variants in genome-wide association studies. Instead of expensive and time-consuming biological experiments, computational prediction of EPIs with DNA sequence and other genomic data is a fast and viable alternative. In particular, deep learning and other machine learning methods have been demonstrated with promising performance.

Results

First, using a published human cell line dataset, we demonstrate that a simple convolutional neural network (CNN) performs as well as, if no better than, a more complicated and state-of-the-art arc

SUBMITTER: Zhuang Z 

PROVIDER: S-EPMC6735851 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature