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Visible Machine Learning for Biomedicine.

Author(s): Yu, Michael K; Ma, Jianzhu; Fisher, Jasmin; Kreisberg, Jason F; Raphael, Benjamin J; et al

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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.
Publication Date: Jun-2018
Citation: Yu, Michael K, Ma, Jianzhu, Fisher, Jasmin, Kreisberg, Jason F, Raphael, Benjamin J, Ideker, Trey. (2018). Visible Machine Learning for Biomedicine.. Cell, 173 (7), 1562 - 1565. doi:10.1016/j.cell.2018.05.056
DOI: doi:10.1016/j.cell.2018.05.056
ISSN: 0092-8674
EISSN: 1097-4172
Pages: 1562 - 1565
Language: eng
Type of Material: Journal Article
Journal/Proceeding Title: Cell
Version: Author's manuscript

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