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Current progress and open challenges for applying deep learning across the biosciences

Authors :
Nicolae Sapoval
Amirali Aghazadeh
Michael G. Nute
Dinler A. Antunes
Advait Balaji
Richard Baraniuk
C. J. Barberan
Ruth Dannenfelser
Chen Dun
Mohammadamin Edrisi
R. A. Leo Elworth
Bryce Kille
Anastasios Kyrillidis
Luay Nakhleh
Cameron R. Wolfe
Zhi Yan
Vicky Yao
Todd J. Treangen
Source :
Nature Communications, Vol 13, Iss 1, Pp 1-12 (2022)
Publication Year :
2022
Publisher :
Nature Portfolio, 2022.

Abstract

Deep learning has enabled advances in understanding biology. In this review, the authors outline advances, and limitations of deep learning in five broad areas and the future challenges for the biosciences.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.14e0a06b00c7444c9c738a631681c526
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-022-29268-7