Visible Machine Learning for Biomedicine.

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

Download

To refer to this page use: http://arks.princeton.edu/ark:/88435/pr1jg0j

Full metadata record

DC Field	Value	Language
dc.contributor.author	Yu, Michael K	-
dc.contributor.author	Ma, Jianzhu	-
dc.contributor.author	Fisher, Jasmin	-
dc.contributor.author	Kreisberg, Jason F	-
dc.contributor.author	Raphael, Benjamin J	-
dc.contributor.author	Ideker, Trey	-
dc.date.accessioned	2021-10-08T19:47:05Z	-
dc.date.available	2021-10-08T19:47:05Z	-
dc.date.issued	2018-06	en_US
dc.identifier.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	en_US
dc.identifier.issn	0092-8674	-
dc.identifier.uri	http://arks.princeton.edu/ark:/88435/pr1jg0j	-
dc.description.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.	en_US
dc.format.extent	1562 - 1565	en_US
dc.language	eng	en_US
dc.language.iso	en_US	en_US
dc.relation.ispartof	Cell	en_US
dc.rights	Author's manuscript	en_US
dc.title	Visible Machine Learning for Biomedicine.	en_US
dc.type	Journal Article	en_US
dc.identifier.doi	doi:10.1016/j.cell.2018.05.056	-
dc.identifier.eissn	1097-4172	-
pu.type.symplectic	http://www.symplectic.co.uk/publications/atom-terms/1.0/journal-article	en_US

Files in This Item:

File	Description	Size	Format
VisibleMachineLearningBiomedicine.pdf		234.17 kB	Adobe PDF	View/Download