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

129 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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T1 - Visible Machine Learning for Biomedicine

AU - Yu, Michael K.

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.

UR - https://www.scopus.com/pages/publications/85048162030

UR - https://www.scopus.com/pages/publications/85048162030#tab=citedBy

U2 - 10.1016/j.cell.2018.05.056

DO - 10.1016/j.cell.2018.05.056

M3 - Comment/debate

C2 - 29906441

AN - SCOPUS:85048162030

SN - 0092-8674

VL - 173

SP - 1562

EP - 1565

JO - Cell

JF - Cell

IS - 7

ER -

Visible Machine Learning for Biomedicine

Abstract

All Science Journal Classification (ASJC) codes

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