Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding-Reference-Cited by-同舟云学术

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

Published:2017-07-12 Issue:14 Volume:33 Page:i92-i101
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Min Xu¹²,Zeng Wanwen¹³,Chen Ning¹²,Chen Ting¹²⁴,Jiang Rui¹³

Affiliation:

1. MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing, China

2. Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing, China

3. Department of Automation, Tsinghua University, Beijing, China

4. Program in Computational Biology and Bioinformatics, University of Southern California, CA, USA

Funder

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/article-pdf/33/14/i92/25157018/btx234.pdf

Reference33 articles.

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3. Greedy layer-wise training of deep networks;Bengio,2007

4. The encode (encyclopedia of DNA elements) project;Consortium;Science,2004

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