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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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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
Zeitschrift
Nature Reviews - Genetics
Quellenangaben
Band: 20,
Heft: 7,
Seiten: 389-403
Verlag
Nature Publishing Group
Verlagsort
Macmillan Building, 4 Crinan St, London N1 9xw, England
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Computational Biology (ICB)