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Visible Machine Learning for Biomedicine

    1. [1] University of Cambridge

      University of Cambridge

      Cambridge District, Reino Unido

    2. [2] Department of Medicine, University of California San Diego
  • Localización: Cell, ISSN 0092-8674, Vol. 173, Nº. 7, 2018, págs. 1562-1565
  • Idioma: inglés
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  • Resumen
    • 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.


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