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

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

Research output: Contribution to journal › Comment/debate › peer-review

116 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 language	English (US)
Pages (from-to)	1562-1565
Number of pages	4
Journal	Cell
Volume	173
Issue number	7
DOIs	https://doi.org/10.1016/j.cell.2018.05.056
State	Published - Jun 14 2018

All Science Journal Classification (ASJC) codes

General Biochemistry, Genetics and Molecular Biology

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10.1016/j.cell.2018.05.056

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title = "Visible Machine Learning for Biomedicine",

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.",

author = "Yu, {Michael K.} and Jianzhu Ma and Jasmin Fisher and Kreisberg, {Jason F.} and Raphael, {Benjamin J.} and Trey Ideker",

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AU - Ma, Jianzhu

AU - Fisher, Jasmin

AU - Kreisberg, Jason F.

AU - Raphael, Benjamin J.

AU - Ideker, Trey

PY - 2018/6/14

Y1 - 2018/6/14

N2 - 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.

AB - 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.

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JO - Cell

JF - Cell

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Visible Machine Learning for Biomedicine

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All Science Journal Classification (ASJC) codes

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