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J R Soc Interface. 2018 Apr;15(141). pii: 20170387. doi: 10.1098/rsif.2017.0387.

Opportunities and obstacles for deep learning in biology and medicine.

Author information

1
Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, USA.
2
Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
3
Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
4
Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
5
Harvard Medical School, Boston, MA, USA.
6
Computational Biology and Stats, Target Sciences, GlaxoSmithKline, Stevenage, UK.
7
Data Science Institute, Imperial College London, London, UK.
8
Princess Margaret Cancer Centre, Toronto, Ontario, Canada.
9
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
10
Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
11
Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA.
12
Ecological and Evolutionary Signal-processing and Informatics Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA.
13
Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
14
Biophysics Program, Stanford University, Stanford, CA, USA.
15
Department of Computer Science, University of Virginia, Charlottesville, VA, USA.
16
Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
17
Department of Computer Science, Stanford University, Stanford, CA, USA.
18
Toyota Technological Institute at Chicago, Chicago, IL, USA.
19
Department of Computer Science, Trinity University, San Antonio, TX, USA.
20
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
21
Integrative Bioinformatics, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA.
22
Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, USA.
23
National Center for Biotechnology Information and National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
24
Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA.
25
ClosedLoop.ai, Austin, TX, USA.
26
Department of Genetics, Stanford University, Stanford, CA, USA.
27
Division of Biomedical Informatics and Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA.
28
Institute of Organic Chemistry, Westfälische Wilhelms-Universität Münster, Münster, Germany.
29
Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA.
30
Department of Pathology and Immunology, Washington University in Saint Louis, St Louis, MO, USA.
31
Department of Medicine, Brown University, Providence, RI, USA.
32
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA gitter@biostat.wisc.edu.
33
Morgridge Institute for Research, Madison, WI, USA.
34
Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA greenescientist@gmail.com.

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.

KEYWORDS:

deep learning; genomics; machine learning; precision medicine

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