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

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dc.citation.articleNumber1728en_US
dc.citation.journalTitleNature Communicationsen_US
dc.citation.volumeNumber13en_US
dc.contributor.authorSapoval, Nicolaeen_US
dc.contributor.authorAghazadeh, Amiralien_US
dc.contributor.authorNute, Michael G.en_US
dc.contributor.authorAntunes, Dinler A.en_US
dc.contributor.authorBalaji, Advaiten_US
dc.contributor.authorBaraniuk, Richarden_US
dc.contributor.authorBarberan, C.J.en_US
dc.contributor.authorDannenfelser, Ruthen_US
dc.contributor.authorDun, Chenen_US
dc.contributor.authorEdrisi, Mohammadaminen_US
dc.contributor.authorElworth, R.A. Leoen_US
dc.contributor.authorKille, Bryceen_US
dc.contributor.authorKyrillidis, Anastasiosen_US
dc.contributor.authorNakhleh, Luayen_US
dc.contributor.authorWolfe, Cameron R.en_US
dc.contributor.authorYan, Zhien_US
dc.contributor.authorYao, Vickyen_US
dc.contributor.authorTreangen, Todd J.en_US
dc.date.accessioned2022-04-28T14:29:14Zen_US
dc.date.available2022-04-28T14:29:14Zen_US
dc.date.issued2022en_US
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.en_US
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.en_US
dc.identifier.digitals41467-022-29268-7en_US
dc.identifier.doihttps://doi.org/10.1038/s41467-022-29268-7en_US
dc.identifier.urihttps://hdl.handle.net/1911/112196en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
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.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleCurrent progress and open challenges for applying deep learning across the biosciencesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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