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Zou J, Huss M, Abid A, et al. A primer on deep learning in genomics. Nat Genet. 2018;51(1):12-18doi: 10.1038/s41588-018-0295-5.
Zou, J., Huss, M., Abid, A., Mohammadi, P., Torkamani, A., & Telenti, A. (2019). A primer on deep learning in genomics. Nature genetics, 51(1), 12-18. https://doi.org/10.1038/s41588-018-0295-5
Zou, James, et al. "A primer on deep learning in genomics." Nature genetics vol. 51,1 (2019): 12-18. doi: https://doi.org/10.1038/s41588-018-0295-5
Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A. A primer on deep learning in genomics. Nat Genet. 2019 Jan;51(1):12-18. doi: 10.1038/s41588-018-0295-5. Epub 2018 Nov 26. PMID: 30478442.
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Nat Genet. 2019 Jan;51(1):12-18. doi: 10.1038/s41588-018-0295-5. Epub 2018 Nov 26.

A primer on deep learning in genomics.

Nature genetics

James Zou, Mikael Huss, Abubakar Abid, Pejman Mohammadi, Ali Torkamani, Amalio Telenti

Affiliations

  1. Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA. [email protected].
  2. Chan-Zuckerberg Biohub, San Francisco, CA, USA. [email protected].
  3. Department of Electrical Engineering, Stanford University, Palo Alto, CA, USA. [email protected].
  4. Peltarion, Stockholm, Sweden.
  5. Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden.
  6. Department of Electrical Engineering, Stanford University, Palo Alto, CA, USA.
  7. Scripps Research Translational Institute, La Jolla, CA, USA.
  8. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.
  9. Scripps Research Translational Institute, La Jolla, CA, USA. [email protected].
  10. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA. [email protected].

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

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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