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

Visible Machine Learning for Biomedicine.

Author information

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.

PMID:
29906441
DOI:
10.1016/j.cell.2018.05.056

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