Nature Communications (Apr 2022)

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,
  • Todd J. Treangen

DOI
https://doi.org/10.1038/s41467-022-29268-7
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 12

Abstract

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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.