Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Jul 15, 2017
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.