Literature DB >> 30478442

A primer on deep learning in genomics.

James Zou1,2,3, Mikael Huss4,5, Abubakar Abid6, Pejman Mohammadi7,8, Ali Torkamani7,8, Amalio Telenti9,10.   

Abstract

Deep learning methods are a class of machine learning techniques capable of identifying highly complex patterns in large datasets. Here, we provide a perspective and primer on deep learning applications for genome analysis. We discuss successful applications in the fields of regulatory genomics, variant calling and pathogenicity scores. We include general guidance for how to effectively use deep learning methods as well as a practical guide to tools and resources. This primer is accompanied by an interactive online tutorial.

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Year:  2018        PMID: 30478442     DOI: 10.1038/s41588-018-0295-5

Source DB:  PubMed          Journal:  Nat Genet        ISSN: 1061-4036            Impact factor:   38.330


  33 in total

1.  Reducing the dimensionality of data with neural networks.

Authors:  G E Hinton; R R Salakhutdinov
Journal:  Science       Date:  2006-07-28       Impact factor: 47.728

Review 2.  Deep learning.

Authors:  Yann LeCun; Yoshua Bengio; Geoffrey Hinton
Journal:  Nature       Date:  2015-05-28       Impact factor: 49.962

Review 3.  Next-Generation Machine Learning for Biological Networks.

Authors:  Diogo M Camacho; Katherine M Collins; Rani K Powers; James C Costello; James J Collins
Journal:  Cell       Date:  2018-06-07       Impact factor: 41.582

Review 4.  Deep learning of genomic variation and regulatory network data.

Authors:  Amalio Telenti; Christoph Lippert; Pi-Chuan Chang; Mark DePristo
Journal:  Hum Mol Genet       Date:  2018-05-01       Impact factor: 6.150

Review 5.  Machine learning applications in genetics and genomics.

Authors:  Maxwell W Libbrecht; William Stafford Noble
Journal:  Nat Rev Genet       Date:  2015-05-07       Impact factor: 53.242

6.  Predicting effects of noncoding variants with deep learning-based sequence model.

Authors:  Jian Zhou; Olga G Troyanskaya
Journal:  Nat Methods       Date:  2015-08-24       Impact factor: 28.547

7.  Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks.

Authors:  David R Kelley; Jasper Snoek; John L Rinn
Journal:  Genome Res       Date:  2016-05-03       Impact factor: 9.043

Review 8.  Deep learning for computational biology.

Authors:  Christof Angermueller; Tanel Pärnamaa; Leopold Parts; Oliver Stegle
Journal:  Mol Syst Biol       Date:  2016-07-29       Impact factor: 11.429

9.  DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.

Authors:  Daniel Quang; Xiaohui Xie
Journal:  Nucleic Acids Res       Date:  2016-04-15       Impact factor: 16.971

Review 10.  Opportunities and obstacles for deep learning in biology and medicine.

Authors:  Travers Ching; Daniel S Himmelstein; Brett K Beaulieu-Jones; Alexandr A Kalinin; Brian T Do; Gregory P Way; Enrico Ferrero; Paul-Michael Agapow; Michael Zietz; Michael M Hoffman; Wei Xie; Gail L Rosen; Benjamin J Lengerich; Johnny Israeli; Jack Lanchantin; Stephen Woloszynek; Anne E Carpenter; Avanti Shrikumar; Jinbo Xu; Evan M Cofer; Christopher A Lavender; Srinivas C Turaga; Amr M Alexandari; Zhiyong Lu; David J Harris; Dave DeCaprio; Yanjun Qi; Anshul Kundaje; Yifan Peng; Laura K Wiley; Marwin H S Segler; Simina M Boca; S Joshua Swamidass; Austin Huang; Anthony Gitter; Casey S Greene
Journal:  J R Soc Interface       Date:  2018-04       Impact factor: 4.293

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  141 in total

Review 1.  Systems biology: perspectives on multiscale modeling in research on endocrine-related cancers.

Authors:  Robert Clarke; John J Tyson; Ming Tan; William T Baumann; Lu Jin; Jianhua Xuan; Yue Wang
Journal:  Endocr Relat Cancer       Date:  2019-06       Impact factor: 5.678

Review 2.  Biophysics and the Genomic Sciences.

Authors:  David C Schwartz
Journal:  Biophys J       Date:  2019-07-30       Impact factor: 4.033

3.  DeepTFactor: A deep learning-based tool for the prediction of transcription factors.

Authors:  Gi Bae Kim; Ye Gao; Bernhard O Palsson; Sang Yup Lee
Journal:  Proc Natl Acad Sci U S A       Date:  2021-01-12       Impact factor: 11.205

4.  TCR Repertoires of Thymic Conventional and Regulatory T Cells: Identification and Characterization of Both Unique and Shared TCR Sequences.

Authors:  Annette Ko; Masashi Watanabe; Thomas Nguyen; Alvin Shi; Achouak Achour; Baojun Zhang; Xiaoping Sun; Qun Wang; Yuan Zhuang; Nan-Ping Weng; Richard J Hodes
Journal:  J Immunol       Date:  2020-01-10       Impact factor: 5.422

5.  A deep dense inception network for protein beta-turn prediction.

Authors:  Chao Fang; Yi Shang; Dong Xu
Journal:  Proteins       Date:  2019-07-23

6.  Combining artificial intelligence: deep learning with Hi-C data to predict the functional effects of non-coding variants.

Authors:  Xiang-He Meng; Hong-Mei Xiao; Hong-Wen Deng
Journal:  Bioinformatics       Date:  2021-06-16       Impact factor: 6.937

7.  Application of deep learning in genomics.

Authors:  Jianxiao Liu; Jiying Li; Hai Wang; Jianbing Yan
Journal:  Sci China Life Sci       Date:  2020-10-10       Impact factor: 6.038

8.  Can artificial neural replicators be useful for studying RNA replicators?

Authors:  Alexandr A Ezhov
Journal:  Arch Virol       Date:  2020-08-19       Impact factor: 2.574

9.  DeepRiPP integrates multiomics data to automate discovery of novel ribosomally synthesized natural products.

Authors:  Nishanth J Merwin; Walaa K Mousa; Chris A Dejong; Michael A Skinnider; Michael J Cannon; Haoxin Li; Keshav Dial; Mathusan Gunabalasingam; Chad Johnston; Nathan A Magarvey
Journal:  Proc Natl Acad Sci U S A       Date:  2019-12-23       Impact factor: 11.205

Review 10.  Genetic underpinnings of cerebral edema in acute brain injury: an opportunity for pathway discovery.

Authors:  Elayna Kirsch; Natalia Szejko; Guido J Falcone
Journal:  Neurosci Lett       Date:  2020-05-26       Impact factor: 3.046

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