A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential-Reference-Cited by-同舟云学术

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A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential

Published:2018-07-09 Issue:16 Volume:46 Page:8105-8113
ISSN:0305-1048
Container-title:Nucleic Acids Research
language:en
Short-container-title:

Author:

Hill Steven T¹,Kuintzle Rachael²,Teegarden Amy²,Merrill Erich¹,Danaee Padideh¹,Hendrix David A¹²

Affiliation:

1. School of Electrical Engineering and Computer Science, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR 97331, USA

2. Department of Biochemistry and Biophysics, Oregon State University, 2011 Ag & Life Sciences Bldg, Corvallis, OR 97331, USA

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Genetics

Link

http://academic.oup.com/nar/article-pdf/46/16/8105/25802460/gky567.pdf

Reference29 articles.

1. Predicting effects of noncoding variants with deep learning-based sequence model;Zhou;Nat. Methods,2015

2. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning;Alipanahi;Nat. Biotechnol.,2015

3. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks;Kelley;Genome Res.,2016

4. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling;Chung;NIPS Deep Learn. Workshop,2014

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