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

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 and Todd J. Treangen ()
Additional contact information
Nicolae Sapoval: Rice University
Amirali Aghazadeh: University of California Berkeley
Michael G. Nute: Rice University
Dinler A. Antunes: University of Houston
Advait Balaji: Rice University
Richard Baraniuk: Rice University
C. J. Barberan: Rice University
Ruth Dannenfelser: Rice University
Chen Dun: Rice University
Mohammadamin Edrisi: Rice University
R. A. Leo Elworth: Rice University
Bryce Kille: Rice University
Anastasios Kyrillidis: Rice University
Luay Nakhleh: Rice University
Cameron R. Wolfe: Rice University
Zhi Yan: Rice University
Vicky Yao: Rice University
Todd J. Treangen: Rice University

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: 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 the biosciences.

Date: 2022
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DOI: 10.1038/s41467-022-29268-7

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