Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding.

ABSTRACT:

Motivation

Experimental techniques for measuring chromatin accessibility are expensive and time consuming, appealing for the development of computational approaches to predict open chromatin regions from DNA sequences. Along this direction, existing methods fall into two classes: one based on handcrafted k -mer features and the other based on convolutional neural networks. Although both categories have shown good performance in specific applications thus far, there still lacks a comprehensive framework to integrate useful k -mer co-occurrence information with recent advances in deep learning.

Results

We fill this gap by addressing the problem of chromatin accessibility prediction with a convolutional Long Short-Term Memory (LSTM) network with k -mer embedding. We first split

SUBMITTER: Min X 

PROVIDER: S-EPMC5870572 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature