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Opportunities and obstacles for deep learning in biology and medicine

View ORCID ProfileTravers Ching, View ORCID ProfileDaniel S. Himmelstein, View ORCID ProfileBrett K. Beaulieu-Jones, View ORCID ProfileAlexandr A. Kalinin, View ORCID ProfileBrian T. Do, View ORCID ProfileGregory P. Way, View ORCID ProfileEnrico Ferrero, View ORCID ProfilePaul-Michael Agapow, View ORCID ProfileWei Xie, View ORCID ProfileGail L. Rosen, View ORCID ProfileBenjamin J. Lengerich, View ORCID ProfileJohnny Israeli, View ORCID ProfileJack Lanchantin, View ORCID ProfileStephen Woloszynek, View ORCID ProfileAnne E. Carpenter, View ORCID ProfileAvanti Shrikumar, View ORCID ProfileJinbo Xu, View ORCID ProfileEvan M. Cofer, View ORCID ProfileDavid J. Harris, View ORCID ProfileDave DeCaprio, View ORCID ProfileYanjun Qi, View ORCID ProfileAnshul Kundaje, View ORCID ProfileYifan Peng, View ORCID ProfileLaura K. Wiley, View ORCID ProfileMarwin H.S. Segler, View ORCID ProfileAnthony Gitter, View ORCID ProfileCasey S. Greene
doi: https://doi.org/10.1101/142760
Travers Ching
1Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI
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Daniel S. Himmelstein
2Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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Brett K. Beaulieu-Jones
3Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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Alexandr A. Kalinin
4Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI
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Brian T. Do
5Harvard Medical School, Boston, MA
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Gregory P. Way
2Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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Enrico Ferrero
6Computational Biology and Stats, Target Sciences, GlaxoSmithKline, Stevenage, United Kingdom
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Paul-Michael Agapow
7Data Science Institute, Imperial College London, London, United Kingdom
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Wei Xie
8Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN
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Gail L. Rosen
9Ecological and Evolutionary Signal-processing and Informatics Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA
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Benjamin J. Lengerich
10Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA
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Johnny Israeli
11Biophysics Program, Stanford University, Stanford, CA
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Jack Lanchantin
12Department of Computer Science, University of Virginia, Charlottesville, VA
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Stephen Woloszynek
9Ecological and Evolutionary Signal-processing and Informatics Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA
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Anne E. Carpenter
13Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA
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Avanti Shrikumar
14Department of Computer Science, Stanford University, Stanford, CA
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Jinbo Xu
15Toyota Technological Institute at Chicago, Chicago, IL
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Evan M. Cofer
16Department of Computer Science, Trinity University, San Antonio, TX
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David J. Harris
17Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL
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Dave DeCaprio
18ClosedLoop.ai, Austin, TX
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Yanjun Qi
12Department of Computer Science, University of Virginia, Charlottesville, VA
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Anshul Kundaje
19Department of Genetics and Department of Computer Science, Stanford University, Stanford, CA
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Yifan Peng
20National Center for Biotechnology Information and National Library of Medicine, National Institutes of Health, Bethesda, MD
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Laura K. Wiley
21Division of Biomedical Informatics and Personalized Medicine, University of Colorado School of Medicine, Aurora, CO
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Marwin H.S. Segler
22Institute of Organic Chemistry, Westfälische Wilhelms-Universität Münster, Münster, Germany
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Anthony Gitter
23Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison and Morgridge Institute for Research, Madison, WI
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  • For correspondence: gitter@biostat.wisc.edu csgreene@upenn.edu
Casey S. Greene
2Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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  • ORCID record for Casey S. Greene
  • For correspondence: gitter@biostat.wisc.edu csgreene@upenn.edu
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Abstract

Deep learning, which describes a class of machine learning algorithms, has recently showed impressive results across a variety of domains. Biology and medicine are data rich, but the data are complex and often ill-understood. Problems of this nature may be particularly well-suited to deep learning techniques. We examine applications of deep learning to a variety of biomedical problems -- patient classification, fundamental biological processes, and treatment of patients -- to predict whether deep learning will transform these tasks or if the biomedical sphere poses unique challenges. We find that deep learning has yet to revolutionize or definitively resolve any of these problems, but promising advances have been made on the prior state of the art. Even when improvement over a previous baseline has been modest, we have seen signs that deep learning methods may speed or aid human investigation. More work is needed to address concerns related to interpretability and how to best model each problem. Furthermore, the limited amount of labeled data for training presents problems in some domains, as can legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning powering changes at the bench and bedside with the potential to transform several areas of biology and medicine.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted May 28, 2017.
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Opportunities and obstacles for deep learning in biology and medicine
Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Wei Xie, Gail L. Rosen, Benjamin J. Lengerich, Johnny Israeli, Jack Lanchantin, Stephen Woloszynek, Anne E. Carpenter, Avanti Shrikumar, Jinbo Xu, Evan M. Cofer, David J. Harris, Dave DeCaprio, Yanjun Qi, Anshul Kundaje, Yifan Peng, Laura K. Wiley, Marwin H.S. Segler, Anthony Gitter, Casey S. Greene
bioRxiv 142760; doi: https://doi.org/10.1101/142760
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Opportunities and obstacles for deep learning in biology and medicine
Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Wei Xie, Gail L. Rosen, Benjamin J. Lengerich, Johnny Israeli, Jack Lanchantin, Stephen Woloszynek, Anne E. Carpenter, Avanti Shrikumar, Jinbo Xu, Evan M. Cofer, David J. Harris, Dave DeCaprio, Yanjun Qi, Anshul Kundaje, Yifan Peng, Laura K. Wiley, Marwin H.S. Segler, Anthony Gitter, Casey S. Greene
bioRxiv 142760; doi: https://doi.org/10.1101/142760

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