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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)
Verlagsversion DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Neural-networks; Chip-seq; Dna; Prediction; Gene; Classification; Cancer; Sites
ISSN (print) / ISBN 1471-0056
e-ISSN 1471-0064
Quellenangaben Band: 20, Heft: 7, Seiten: 389-403 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort Macmillan Building, 4 Crinan St, London N1 9xw, England
Begutachtungsstatus