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 | Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders. | Jie Tan, Matt Ung, Chao Cheng, Casey Greene | Pacific Symposium on Biocomputing (PSB) | Models & Data
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
2015-11 | Recurrent Neural Network Based Hybrid Model of Gene Regulatory Network | Khalid Raza, Mansaf Alam | Arxiv
2015-11 | Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics | Ehsaneddin Asgari, Mohammad R. K. Mofrad | PloS one
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
2016-02 | Semi-Supervised Learning of the Electronic Health Record for Phenotype Stratification | Brett Beaulieu-Jones, Casey Greene | bioRxiv
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 | Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning | Tanel Pärnamaa, Leopold Parts | bioRxiv
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
2016-04 | Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks | Zhen Li and Yizhou Yu | Arxiv
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 | Not Just a Black Box: Learning Important Features Through Propagating Activation Differences | Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, Anshul Kundaje | 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 | Genetic Architect: Discovering Genomic Structure with Learned Neural Architectures | Laura Deming, Sasha Targ, Nate Sauder, Diogo Almeida, Chun Jimmie Ye | Arxiv
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
2016-07 | Deep learning for computational biology | Christof Angermueller, Tanel Pärnamaa, Leopold Parts, Oliver Stegle | Molecular Systems Biology
2016-08 | Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications | Lucas Antón Pastur-Romay, Francisco Cedrón, Alejandro Pazos and Ana Belén Porto-Pazos | International Journal of Molecular Sciences
2016-08 | Deep GDashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks | Jack Lanchantin, Ritambhara Singh, Beilun Wang, Yanjun Qi | Arxiv
2016-08 | Modeling translation elongation dynamics by deep learning reveals new insights into the landscape of ribosome stalling | Sai Zhang, Hailin Hu, Jingtian Zhou, Xuan He and Jianyang Zeng | bioRxiv
2016-08 | DeepWAS: Directly integrating regulatory information into GWAS using deep learning supports master regulator MEF2C as risk factor for major depressive disorder | Gökcen Eraslan, Janine Arloth, Jade Martins, Stella Iurato, Darina Czamara, Elisabeth B. Binder, Fabian J. Theis, Nikola S. Mueller | bioRxiv
2016-09 | The Next Era: Deep Learning in Pharmaceutical Research | Sean Ekins | Pharmaceutical Research
2016-09 | Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model | Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang, Jinbo Xu | Arxiv
2016-10 | FIDDLE: An integrative deep learning framework for functional genomic data inference | Umut Eser, L. Stirling Churchman | bioRxiv
2016-10 | Deep Learning for Imaging Flow Cytometry: Cell Cycle Analysis of Jurkat Cells | Philipp Eulenberg, Niklas Koehler, Thomas Blasi, Andrew Filby, Anne E. Carpenter, Paul Rees, Fabian J. Theis, F. Alexander Wolf | bioRxiv

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