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

      Deep learning for computational biology

      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.


          Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.

          Related collections

          Most cited references 95

          • Record: found
          • Abstract: found
          • Article: not found

          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
            • 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

              ImageNet Large Scale Visual Recognition Challenge


                Author and article information

                Mol Syst Biol
                Mol. Syst. Biol
                Molecular Systems Biology
                John Wiley and Sons Inc. (Hoboken )
                29 July 2016
                July 2016
                : 12
                : 7 ( doiID: 10.1002/msb.v12.7 )
                [ 1 ] European Molecular Biology Laboratory European Bioinformatics InstituteWellcome Trust Genome Campus Hinxton CambridgeUK
                [ 2 ] Department of Computer ScienceUniversity of Tartu TartuEstonia
                [ 3 ] Wellcome Trust Sanger InstituteWellcome Trust Genome Campus Hinxton CambridgeUK
                Author notes
                [* ] Corresponding author. Tel: +44 1223 834 244; E‐mail: leopold.parts@ 123456sanger.ac.uk

                Corresponding author. Tel: +44 1223 494 101; E‐mail: oliver.stegle@ 123456ebi.ac.uk


                These authors contributed equally to this work

                © 2016 The Authors. Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Pages: 16
                Funded by: European Molecular Biology Organization (EMBO)
                Funded by: European Commission (EC)
                Funded by: Estonian Research Council
                Award ID: IUT34‐4
                Funded by: Wellcome Trust
                Funded by: European Research Council
                Award ID: N635290
                Custom metadata
                July 2016
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.9.2 mode:remove_FC converted:29.07.2016


                Comment on this article