Visible Machine Learning for Biomedicine

Publication date: 14 June 2018 Source:Cell, Volume 173, Issue 7 Author(s): Michael K. Yu, Jianzhu Ma, Jasmin Fisher, Jason F. Kreisberg, Benjamin J. Raphael, Trey Ideker 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. Teaser 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.
Source: Cell - Category: Cytology Source Type: research