Building a two-way street between cell biology and machine learning

In a Comment for Nature Cell Biology, the Eric and Wendy Schmidt Center's director Caroline Uhler discusses how the rise of large-scale datasets in biology positions the field to become a driver of foundational advances in machine learning — and vice versa. Uhler, who is also a full professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at MIT, advocates for new machine learning models that can better integrate different types of biological data and can uncover causal mechanisms in disease, not just associations. She also discusses the need for close collaborations between biologists and computational scientists so that predictive and causal algorithms can be incorporated into experimental design — and outlines some of the challenges, such as distinct cultures and vocabularies, of building those teams.

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