Next-Generation Machine Learning for Biological Networks.

Journal: Cell
Published Date: Jun 7, 2018

Abstract

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.

Authors

  • Diogo M Camacho
    Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.
  • Katherine M Collins
    Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Brain & Cognitive Sciences and Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • Rani K Powers
    Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
  • James C Costello
    Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA. Electronic address: james.costello@ucdenver.edu.
  • James J Collins
    Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

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