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 \u2018\u2018visible\u2019\u2019 approaches that guide model structure with experimental biology.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 \u2018\u2018visible\u2019\u2019 approaches that guide model structure with experimental biology.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Michael K. Yu","user_id":0},{"type":"text","value":"Jianzhu Ma","user_id":0},{"type":"user_nicename","value":"Jasmin Fisher","user_id":"32247"},{"type":"text","value":"Jason F. Kreisberg","user_id":0},{"type":"text","value":"Benjamin J. 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