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Opportunities and obstacles for deep learning in biology and medicine

Travers ChingMolecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, USADaniel HimmelsteinUniversity of PennsylvaniaBrett K. Beaulieu‐JonesGenomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USAAlexandr A. KalininDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USAT. BrianHarvard Medical School, Boston, MA, USAGregory P. WayDepartment of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USAEnrico FerreroComputational Biology and Stats, Target Sciences, GlaxoSmithKline, Stevenage, UKPaul‐Michael AgapowData Science Institute, Imperial College London, London, UKMichael ZietzDepartment of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USAMichael M. HoffmanDepartment of Computer Science, University of Toronto, Toronto, Ontario, CanadaWei XieElectrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USAGail RosenEcological and Evolutionary Signal-processing and Informatics Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USABenjamin J. LengerichComputational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USAJohnny IsraeliBiophysics Program, Stanford University, Stanford, CA, USAJack LanchantinDepartment of Computer Science, University of Virginia, Charlottesville, VA, USAStephen WoloszynekEcological and Evolutionary Signal-processing and Informatics Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USAAnne E. CarpenterImaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USAAvanti ShrikumarDepartment of Computer Science, Stanford University, Stanford, CA, USAJinbo XuToyota Technological Institute at Chicago, Chicago, IL, USAEvan M. CoferDepartment of Computer Science, Trinity University, San Antonio, TX, USAChristopher A. LavenderIntegrative Bioinformatics, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USASrinivas C. TuragaHoward Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, USAAmr M. AlexandariDepartment of Computer Science, Stanford University, Stanford, CA, USAZhiyong LuNational Center for Biotechnology Information and National Library of Medicine, National Institutes of Health, Bethesda, MD, USADavid J. HarrisDepartment of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USADavid DeCaprioYanjun QiDepartment of Computer Science, University of Virginia, Charlottesville, VA, USAAnshul KundajeDepartment of Computer Science, Stanford University, Stanford, CA, USAYifan PengNational Center for Biotechnology Information and National Library of Medicine, National Institutes of Health, Bethesda, MD, USALaura K. WileyDivision of Biomedical Informatics and Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USAMarwin SeglerInstitute of Organic Chemistry, Westfälische Wilhelms-Universität Münster, Münster, GermanySimina M. BocaInnovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USAS. Joshua SwamidassDepartment of Pathology and Immunology, Washington University in Saint Louis, St Louis, MO, USAAustin HuangDepartment of Medicine, Brown University, Providence, RI, USAAnthony GitterDepartment of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USACasey S. GreeneDepartment of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

2018en

ABI

Annotatsiya

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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Identifikatorlar

DOI: 10.1098/rsif.2017.0387

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