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

Nicolae Sapoval

doi:10.1038/s41467-022-29268-7

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

2022-04-01

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Anastasios Kyrillidis

DOI：10.1038/s41467-022-29268-7

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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. 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.

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

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