Deep learning: new computational modelling techniques for genomics

Eraslan G, Avsec Z, Gagneur J, Theis FJ (2019)

Publication Type: Journal article, Review article

Publication year: 2019

Journal

Nature reviews genetics Nature Publishing Group

Book Volume: 20

Pages Range: 389-403

Journal Issue: 7

DOI: 10.1038/s41576-019-0122-6

Abstract

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.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Eraslan, G., Avsec, Z., Gagneur, J., & Theis, F.J. (2019). Deep learning: new computational modelling techniques for genomics. Nature reviews genetics, 20(7), 389-403. https://doi.org/10.1038/s41576-019-0122-6

MLA:

Eraslan, Gokcen, et al. "Deep learning: new computational modelling techniques for genomics." Nature reviews genetics 20.7 (2019): 389-403.

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