A curated list of awesome deep learning applications in the field of computational biology

• 2012-07 | Deep architectures for protein contact map prediction | Pietro Di Lena, Ken Nagata and Pierre Baldi Bioinformatics

• 2012-10 | Predicting protein residue–residue contacts using deep networks and boosting | Jesse Eickholt and Jianlin Cheng | Bioinformatics

• 2013-03 | DNdisorder: predicting protein disorder using boosting and deep networks | Jesse Eickholt and Jianlin Cheng | BMC Bioinformatics

• 2014-06 | Deep learning of the tissue-regulated splicing code | Michael K. K. Leung, Hui Yuan Xiong, Leo J. Lee and Brendan J. Frey | Bioinformatics

• 2014-10 | DANN: a deep learning approach for annotating the pathogenicity of genetic variants | Daniel Quang, Yifei Chen and Xiaohui Xie | Bioinformatics

• 2015-01 | The human splicing code reveals new insights into the genetic determinants of disease | Hui Y. Xiong, Babak Alipanahi, Leo J. Lee, Hannes Bretschneider, Daniele Merico, Ryan K. C. Yuen, Yimin Hua, Serge Gueroussov, Hamed S. Najafabadi, Timothy R. Hughes, Quaid Morris, Yoseph Barash, Adrian R. Krainer, Nebojsa Jojic, Stephen W. Scherer, Benjamin J. Blencowe, Brendan J. Frey | Science

• 2015-03 | Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters | Yifeng Li, Chih-Yu Chen, and Wyeth W. Wasserman | 19th Annual International Conference, RECOMB 2015, Warsaw, Proceedings

• 2015-05 | Trans-species learning of cellular signaling systems with bimodal deep belief networks | Lujia Chen, Chunhui Cai, Vicky Chen and Xinghua Lu | Bioinformatics

• 2015-05 | Deep convolutional neural networks for annotating gene expression patterns in the mouse brain | Tao Zeng, Rongjian Li, Ravi Mukkamala, Jieping Ye and Shuiwang Ji | BMC Bioinformatics

• 2015-07 | DeepBind: Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning | Babak Alipanahi, Andrew Delong, Matthew T. Weirauch & Brendan J. Frey | Nature Biotechnology

• 2015-08 | Deep learning for regulatory genomics | Yongjin Park & Manolis Kellis | Nature Biotechnology

• 2015-08 | DeepSEA: Predicting effects of noncoding variants with deep learning–based sequence model | Jian Zhou & Olga G. Troyanskaya | Nature Methods: Short intro & Nature Methods

• 2015-10 | A deep learning framework for modeling structural features of RNA-binding protein targets | Sai Zhang, Jingtian Zhou, Hailin Hu, Haipeng Gong, Ligong Chen, Chao Cheng, and Jianyang Zeng | NAR

• 2015-10 | Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks | David R. Kelley, Jasper Snoek, John Rinn | Biorxiv

• 2015-11 | De novo identification of replication-timing domains in the human genome by deep learning | Feng Liu, Chao Ren, Hao Li, Pingkun Zhou, Xiaochen Bo and Wenjie Shu | Bioinformatics

• 2016-01 | PEDLA: predicting enhancers with a deep learning-based algorithmic framework | Feng Liu, Hao Li, Chao Ren, Xiaochen Bo, Wenjie Shu | Biorxiv

• 2016-01 | TensorFlow: Biology’s Gateway to Deep Learning? | Ladislav Rampasek, Anna Goldenberg | Cell Systems

• 2016-02 | Gene expression inference with deep learning | Yifei Chen, Yi Li, Rajiv Narayan, Aravind Subramanian, Xiaohui Xie | Bioinformatics

• 2016-03 | Genome-Wide Prediction of cis-Regulatory Regions Using Supervised Deep Learning Methods | Yifeng Li, Wenqiang Shi, Wyeth W Wasserman | Biorxiv

• 2016-03 | Deep Learning in Bioinformatics | Seonwoo Min, Byunghan Lee, Sungroh Yoon | Arxiv

• 2016-03 | Applications of deep learning in biomedicine | Polina Mamoshina, Armando Vieira, Evgeny Putin, and Alex Zhavoronkov | ACS Molecular Pharmaceutics

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