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Open Access Green as soon as Postprint is submitted to ZB.
Deep learning: New computational modelling techniques for genomics.
Nat. Rev. Genet. 20, 389-403 (2019)
As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing.
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Publication type
Article: Journal article
Document type
Review
Keywords
Neural-networks; Chip-seq; Dna; Prediction; Gene; Classification; Cancer; Sites
ISSN (print) / ISBN
1471-0056
e-ISSN
1471-0064
Journal
Nature Reviews - Genetics
Quellenangaben
Volume: 20,
Issue: 7,
Pages: 389-403
Publisher
Nature Publishing Group
Publishing Place
Macmillan Building, 4 Crinan St, London N1 9xw, England
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)