Visible Machine Learning for Biomedicine | Zendy

Michael Yu | Zendy; Jianzhu Ma | Zendy; Jasmin Fisher | Zendy; Jason F. Kreisberg | Zendy; Benjamin J. Raphael | Zendy; Trey Ideker | Zendy

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Visible Machine Learning for Biomedicine

Author(s) -

Michael Yu,

Jianzhu Ma,

Jasmin Fisher,

Jason F. Kreisberg,

Benjamin J. Raphael,

Trey Ideker

Publication year - 2018

Publication title -

cell

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 26.304

H-Index - 776

eISSN - 1097-4172

pISSN - 0092-8674

DOI - 10.1016/j.cell.2018.05.056

Subject(s) - biomedicine , artificial intelligence , scopus , deep learning , machine learning , field (mathematics) , data science , face (sociological concept) , biology , computer science , medline , bioinformatics , biochemistry , social science , mathematics , sociology , pure mathematics

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.

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