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

Sapoval, Nicolae; Aghazadeh, Amirali; Nute, Michael; Antunes, Dinler A.; Balaji, Advait; Baraniuk, Richard G.; Barberan, CJ; Dannenfelser, Ruth; Dun, Chen; Edrisi, Mohammadamin; Elworth, R. A. Leo; Kille, Bryce; Kyrillidis, Anastasios; Nakhleh, Luay; Wolfe, C.; Yan, Zhi; Yao, Vicky; Treangen, Todd J.

doi:10.1038/s41467-022-29268-7

reviewNature CommunicationsApr 1, 2022GOLD OA

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

NSNicolae Sapoval AAAmirali Aghazadeh MNMichael Nute DADinler A. Antunes ABAdvait Balaji

Rice University · University of California, Berkeley · +1 more institution

PubMed

Indexed incrossrefdoajpubmed

Abstract

Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across…

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Keywords

Data science
Computer science
Current (fluid)
Computational biology
Biology
Physics

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