Visible Machine Learning for Biomedicine

Michael K Yu; Jianzhu Ma; Jasmin Fisher; Jason F Kreisberg; Benjamin J Raphael; Trey Ideker

doi:10.1016/j.cell.2018.05.056

Visible Machine Learning for Biomedicine

Cell. 2018 Jun 14;173(7):1562-1565. doi: 10.1016/j.cell.2018.05.056.

Authors

Michael K Yu¹, Jianzhu Ma², Jasmin Fisher³, Jason F Kreisberg², Benjamin J Raphael⁴, Trey Ideker⁵

Affiliations

¹ 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.
² 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.
³ Department of Biochemistry, University of Cambridge, Cambridge, UK.
⁴ Department of Computer Science, Princeton University, Princeton, NJ, USA. Electronic address: braphael@princeton.edu.
⁵ 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. Electronic address: tideker@ucsd.edu.

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.

Visible Machine Learning for Biomedicine

Authors

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Abstract

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Grants and funding