Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA; Cancer Cell Map Initiative, University of California San Diego, La Jolla, CA, USA; UCSD Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA, USA.
Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA; Cancer Cell Map Initiative, University of California San Diego, La Jolla, CA, USA.
Cell. 2018 Jun 14;173(7):1562-1565. doi: 10.1016/j.cell.2018.05.056.
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.
人工智能的主要目标之一在于将患者数据转化为成功的治疗方法。然而，机器学习模型在生物医学领域面临着特殊的挑战，包括处理极端的数据异质性以及缺乏对预测的机制理解。在这里，我们提倡采用“可见”的方法，用实验生物学指导模型结构。
