Awesome DeepBio

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

• 2014-11 | Pairwise input neural network for target-ligand interaction prediction | Caihua Wang, Juan Liu, Fei Luo, Yafang Tan, Zixin Deng, Qian-Nan Hu | 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

• 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

• 2015-08 | Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach | Muxuan Liang, Zhizhong Li, Ting Chen, Jianyang Zeng | IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

• 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 | code

• 2015-10 | Deep Learning for Drug-Induced Liver Injury | Youjun Xu, Ziwei Dai, Fangjin Chen, Shuaishi Gao, Jianfeng Pei, and Luhua Lai | ASC Journal of Chemical Information and Modeling

• 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 | Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model | Lujia Chen, Chunhui Cai, Vicky Chen and Xinghua Lu | BMC 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

• 2016-01 | ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions | mSystems | code

• 2016-01 | Deep Learning in Drug Discovery | Erik Gawehn, Jan A. Hiss and Gisbert Schneider | Molecular Informatics

• 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

• 2016-03 | DeepNano: Deep Recurrent Neural Networks for Base Calling in MinION Nanopore Reads | Vladimír Boža, Broňa Brejová, Tomáš Vinař | Arxiv | code

• 2016-03 | deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks | Byunghan Lee, Junghwan Baek, Seunghyun Park, Sungroh Yoon | Arxiv

• 2016-03 | Deep Learning in Label-free Cell Classification | Claire Lifan Chen, Ata Mahjoubfar, Li-Chia Tai, Ian K. Blaby, Allen Huang, Kayvan Reza Niazi & Bahram Jalali | Nature Scientific Reports

• 2016-04 | DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences | Daniel Quang & Xiaohui Xie | Nucleic Acids Research | code

• 2016-04 | deepMiRGene: Deep Neural Network based Precursor microRNA Prediction | Seunghyun Park, Seonwoo Min, Hyun-soo Choi, and Sungroh Yoon | Arxiv

• 2016-04 | Microscopy cell counting and detection with fully convolutional regression networks | Weidi Xie, J. Alison Noble and Andrew Zisserman | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

• 2016-05 | Denoising genome-wide histone ChIP-seq with convolutional neural networks | Pang Wei Koh, Emma Pierson, Anshul Kundaje | Biorxiv

• 2016-05 | Deep Motif: Visualizing Genomic Sequence Classifications | Jack Lanchantin, Ritambhara Singh, Zeming Lin, Yanjun Qi | Arxiv

• 2016-05 | Deep biomarkers of human aging: Application of deep neural networks to biomarker development | Evgeny Putin, Polina Mamoshina, Alexander Aliper, Mikhail Korzinkin, Alexey Moskalev, Alexey Kolosov, Alexander Ostrovskiy, Charles Cantor, Jan Vijg, and Alex Zhavoronkov | Aging

• 2016-05 | Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data | Alexander Aliper, Sergey Plis, Artem Artemov, Alvaro Ulloa, Polina Mamoshina, and Alex Zhavoronkov | ACS Molecular Pharmaceutics

• 2016-05 | Accurate prediction of single-cell DNA methylation states using deep learning | Christof Angermueller, Heather Lee, Wolf Reik, Oliver Stegle | Biorxiv

• 2016-05 | Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping | Michael P. Pound, Alexandra J. Burgess, Michael H. Wilson, Jonathan A. Atkinson, Marcus Griffiths, Aaron S. Jackson, Adrian Bulat, Yorgos Tzimiropoulos, Darren M. Wells, Erik H. Murchie, Tony P. Pridmore, Andrew P. French | Biorxiv

• 2016-05 | DeepCyTOF: Automated Cell Classification of Mass Cytometry Data by Deep Learning and Domain Adaptation | Huamin Li, Uri Shaham, Yi Yao, Ruth Montgomery, Yuval Kluger | Biorxiv

• 2016-06 | Classifying and segmenting microscopy images with deep multiple instance learning | Oren Z. Kraus, Jimmy Lei Ba and Brendan J. Frey | Bioinformatics

• 2016-06 | Convolutional neural network architectures for predicting DNA–protein binding | Haoyang Zeng, Matthew D. Edwards, Ge Liu and David K. Gifford | Bioinformatics

• 2016-06 | DeepLNC, a long non-coding RNA prediction tool using deep neural network | Rashmi Tripathi, Sunil Patel, Vandana Kumari, Pavan Chakraborty, Pritish Kumar Varadwaj | Network Modeling Analysis in Health Informatics and Bioinformatics

• 2016-06 | Virtual Screening: A Challenge for Deep Learning | Javier Pérez-Sianes, Horacio Pérez-Sánchez, Fernando Díaz | 10th International Conference on Practical Applications of Computational Biology & Bioinformatics

• 2016-07 | DeepChrome: Deep-learning for predicting gene expression from histone modifications | Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi | Arxiv

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