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Eraslan, G. ; Avsec, Ž.* ; Gagneur, J.* ; Theis, F.J.

Deep learning: New computational modelling techniques for genomics.

Nat. Rev. Genet. 20, 389-403 (2019)
Open Access Green as soon as Postprint is submitted to ZB.
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
Quellenangaben Volume: 20, Issue: 7, Pages: 389-403 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place Macmillan Building, 4 Crinan St, London N1 9xw, England
Reviewing status Peer reviewed