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Journal article
Opportunities and obstacles for deep learning in biology and medicine
Journal of the Royal Society interface, v 15(141), pp 20170387-20170387
Apr 2018
PMID: 29618526
Abstract
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.
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Details
- Title
- Opportunities and obstacles for deep learning in biology and medicine
- Creators
- Travers Ching - University of Hawaii SystemDaniel S Himmelstein - Raymond and Ruth Perelman School of Medicine at the University of PennsylvaniaBrett K Beaulieu-Jones - Raymond and Ruth Perelman School of Medicine at the University of PennsylvaniaAlexandr A Kalinin - University of Michigan–Ann ArborBrian T Do - Harvard UniversityGregory P Way - Raymond and Ruth Perelman School of Medicine at the University of PennsylvaniaEnrico Ferrero - GlaxoSmithKlinePaul-Michael Agapow - Data Science Institute, Imperial College London, London, UK.Michael Zietz - Raymond and Ruth Perelman School of Medicine at the University of PennsylvaniaMichael M Hoffman - University of TorontoWei Xie - Vanderbilt UniversityGail L Rosen - Drexel UniversityBenjamin J Lengerich - Carnegie Mellon UniversityJohnny Israeli - Stanford UniversityJack Lanchantin - University of VirginiaStephen Woloszynek - Drexel UniversityAnne E Carpenter - Broad InstituteAvanti Shrikumar - Stanford UniversityJinbo Xu - Toyota Technological Institute at ChicagoEvan M Cofer - Trinity UniversityChristopher A Lavender - National Institute of Environmental Health SciencesSrinivas C Turaga - Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, USA.Amr M Alexandari - Stanford UniversityZhiyong Lu - National Center for Biotechnology InformationDavid J Harris - University of FloridaDave DeCaprio - ClosedLoop.ai, Austin, TX, USA.Yanjun Qi - University of VirginiaAnshul Kundaje - Stanford UniversityYifan Peng - National Center for Biotechnology InformationLaura K Wiley - University of Colorado DenverMarwin H S Segler - University of MünsterSimina M Boca - Georgetown University Medical CenterS Joshua Swamidass - Washington University in St. LouisAustin Huang - Brown UniversityAnthony Gitter - University of Wisconsin–MadisonCasey S Greene (Corresponding Author) - Raymond and Ruth Perelman School of Medicine at the University of Pennsylvania
- Publication Details
- Journal of the Royal Society interface, v 15(141), pp 20170387-20170387
- Publisher
- The Royal Society
- Grant note
- T32 HG000046 / NHGRI NIH HHS R01 GM089753 / NIGMS NIH HHS P30 ES013508 / NIEHS NIH HHS UL1 TR001409 / NCATS NIH HHS T32 GM007753 / NIGMS NIH HHS DP2 GM123485 / NIGMS NIH HHS R35 GM122547 / NIGMS NIH HHS R21 CA220398 / NCI NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000431106100001
- Scopus ID
- 2-s2.0-85045190865
- Other Identifier
- 991019168737804721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications