Visible Machine Learning for Biomedicine.

Journal: Cell
Published Date: Jun 14, 2018

Abstract

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Here, we argue for "visible" approaches that guide model structure with experimental biology.

Authors

  • Michael K Yu
    Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA; Cancer Cell Map Initiative, University of California San Diego, La Jolla, CA, USA; UCSD Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA, USA.
  • Jianzhu Ma
    Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave. Chicago, Illinois 60637 USA.
  • Jasmin Fisher
    Department of Biochemistry, University of Cambridge, Cambridge, UK.
  • Jason F Kreisberg
    Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA; Cancer Cell Map Initiative, University of California San Diego, La Jolla, CA, USA.
  • Benjamin J Raphael
    Department of Computer Science, Princeton University, Princeton, NJ, USA. Electronic address: braphael@princeton.edu.
  • Trey Ideker

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