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

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dc.citation.articleNumber1728
dc.citation.journalTitleNature Communications
dc.citation.volumeNumber13
dc.contributor.authorSapoval, Nicolae
dc.contributor.authorAghazadeh, Amirali
dc.contributor.authorNute, Michael G.
dc.contributor.authorAntunes, Dinler A.
dc.contributor.authorBalaji, Advait
dc.contributor.authorBaraniuk, Richard
dc.contributor.authorBarberan, C.J.
dc.contributor.authorDannenfelser, Ruth
dc.contributor.authorDun, Chen
dc.contributor.authorEdrisi, Mohammadamin
dc.contributor.authorElworth, R.A. Leo
dc.contributor.authorKille, Bryce
dc.contributor.authorKyrillidis, Anastasios
dc.contributor.authorNakhleh, Luay
dc.contributor.authorWolfe, Cameron R.
dc.contributor.authorYan, Zhi
dc.contributor.authorYao, Vicky
dc.contributor.authorTreangen, Todd J.
dc.date.accessioned2022-04-28T14:29:14Z
dc.date.available2022-04-28T14:29:14Z
dc.date.issued2022
dc.description.abstractDeep 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.
dc.identifier.citationSapoval, Nicolae, Aghazadeh, Amirali, Nute, Michael G., et al.. "Current progress and open challenges for applying deep learning across the biosciences." <i>Nature Communications,</i> 13, (2022) Springer Nature: https://doi.org/10.1038/s41467-022-29268-7.
dc.identifier.digitals41467-022-29268-7
dc.identifier.doihttps://doi.org/10.1038/s41467-022-29268-7
dc.identifier.urihttps://hdl.handle.net/1911/112196
dc.language.isoeng
dc.publisherSpringer Nature
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleCurrent progress and open challenges for applying deep learning across the biosciences
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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