+1 Recommend
0 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Opportunities and obstacles for deep learning in biology and medicine

      1 , 2 , 3 , 4 , 5 , 2 , 6 , 7 , 2 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 17 , 12 , 18 , 15 , 19 , 20 , 21 , 22 , 23 , 15 , 24 , 25 , 26 , 17 , 15 , 16 , 24 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 2

      Journal of the Royal Society Interface

      The Royal Society

      deep learning, genomics, precision medicine, machine learning

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          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.

          Related collections

          Most cited references 297

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Deep Learning in Neural Networks: An Overview

          In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
            • Record: found
            • Abstract: not found
            • Article: not found


              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              U-Net: Convolutional Networks for Biomedical Image Segmentation

              There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .

                Author and article information

                J R Soc Interface
                J R Soc Interface
                Journal of the Royal Society Interface
                The Royal Society
                April 2018
                4 April 2018
                4 April 2018
                : 15
                : 141
                [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, Stanford University , Stanford, CA, USA
                [16 ]Department of Genetics, Stanford University , Stanford, CA, USA
                [17 ]Department of Computer Science, University of Virginia , Charlottesville, VA, USA
                [18 ]Imaging Platform, Broad Institute of Harvard and MIT , Cambridge, MA, USA
                [19 ]Toyota Technological Institute at Chicago , Chicago, IL, USA
                [20 ]Department of Computer Science, Trinity University , San Antonio, TX, USA
                [21 ]Lewis-Sigler Institute for Integrative Genomics, Princeton University , Princeton, NJ, USA
                [22 ]Integrative Bioinformatics, National Institute of Environmental Health Sciences, National Institutes of Health , Research Triangle Park, NC, USA
                [23 ]Howard Hughes Medical Institute , Janelia Research Campus, Ashburn, VA, USA
                [24 ]National Center for Biotechnology Information and National Library of Medicine, National Institutes of Health , Bethesda, MD, USA
                [25 ]Department of Wildlife Ecology and Conservation, University of Florida , Gainesville, FL, USA
                [26 ]ClosedLoop.ai , Austin, TX, 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
                [33 ]Morgridge Institute for Research , Madison, WI, USA
                Author notes

                Author order was determined with a randomized algorithm.

                © 2018 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                Funded by: Gordon and Betty Moore Foundation, http://dx.doi.org/10.13039/100000936;
                Award ID: GBMF 4552
                Award ID: GBMF 4563
                Funded by: National Institutes of Health, http://dx.doi.org/10.13039/100000002;
                Award ID: DP2GM123485
                Award ID: P30CA051008
                Award ID: R01AI116794
                Award ID: R01GM089652
                Award ID: R01GM089753
                Award ID: R01LM012222
                Award ID: R01LM012482
                Award ID: R21CA220398
                Award ID: T32GM007753
                Award ID: T32HG000046
                Award ID: U54AI117924
                Funded by: Roy and Diana Vagelos Scholars Program in the Molecular Life Sciences;
                Funded by: U.S. National Library of Medicine, http://dx.doi.org/10.13039/100000092;
                Award ID: Intramural Research Program
                Funded by: National Science Foundation, http://dx.doi.org/10.13039/501100008982;
                Award ID: 1245632
                Award ID: 1531594
                Award ID: 1564955
                Funded by: Natural Sciences and Engineering Research Council of Canada, http://dx.doi.org/10.13039/501100000038;
                Award ID: RGPIN-2015-3948
                Funded by: NSF;
                Award ID: 1245632
                Award ID: 1531594
                Award ID: 1564955
                Funded by: Howard Hughes Medical Institute, http://dx.doi.org/10.13039/100000011;
                Review Articles
                Headline Review
                Custom metadata
                April, 2018

                Life sciences

                precision medicine, genomics, deep learning, machine learning


                Comment on this article