A primer on deep learning in genomics
Open accessAbstract
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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Deep neural networks, convolutional neural networks, and recurrent neural networks are widely employed for healthcare applications.
“With advanced DL techniques, deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks are widely employed for healthcare applications, i.e., epilepsy seizure prediction [52], segmentation of brain tumor [53], Alzheimer detection [54], genomics [55], and medical prescriptions [56].”
From Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2022) · cites this paper as [53]
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BackgroundMethodExtendable and explainable deep learning for pan-cancer radiogenomics research
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- Authors
- Zou, J., Huss, M., Abid, A., Mohammadi, P., Torkamani, A., & Telenti, A.
- Affiliations
- Chan Zuckerberg Biohub San Francisco (US)
- Karolinska Institutet (SE)
- Stanford University (US)
- Scripps Research Institute (US)
- Corresponding author
- Zou, James
- Telenti, Amalio
- Venue
- Nature Genetics
- Published
- 2018-11-26
- Volume
- 51
- Issue
- 1
- Pages
- 12-18
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC11180539
- OpenAlex
- W2901218091
- Cited by
- 891
- References
- 68
- Citations by year
- 2026: 65 · 2025: 123 · 2024: 138 · 2023: 104 · 2022: 135 · 2021: 134 · 2020: 124 · 2019: 65 · 2018: 1
- FWCI
- 7.97
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- National Institutes of Health (U24TR002306, UL1TR002550)
- National Institute on Aging (P30AG059307)
- National Institute of General Medical Sciences (U54GM114833)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- deep learning, genome analysis, regulatory genomics, variant calling, pathogenicity score, machine learning tools
- MeSH terms
- Machine Learning, Deep Learning, Humans, Genome, Human, Genomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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