Visible Machine Learning for Biomedicine

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

Research output: Contribution to journalComment/debate

36 Scopus citations

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. 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.

Original languageEnglish (US)
Pages (from-to)1562-1565
Number of pages4
JournalCell
Volume173
Issue number7
DOIs
StatePublished - Jun 14 2018

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)

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