Lee Lab

Explainable Machine Learning for Biology & Precision Medicine

Prof. Su-In Lee's lab seeks to develop interpretable machine learning techniques to learn from big data: (1) how the human genome or protein works, (2) how to improve healthcare, and (3) how to treat challenging diseases such as cancer and Alzheimer's disease. Her research page lists her projects, including treating cancer based on a patient's own expression profile, finding therapeutic targets for Alzheimer's, predicting kidney disease, preventing complications during surgery, enabling pre-hospital predictions for trauma patients, analyzing medical images, and improving our understanding of pan-cancer biology and genome biology.


  • 12/31/2018: Scott's SHAP paper (NeurIPS oral presentation) got cited 100 times as of today after about 1 year it was published.
  • 11/1/2018: Our EMBARKER project on identifying therapeutic targets for Alzheimer's disease won the Madrona Prize at the Allen School 2018 Industry Affiliates Annual Research Day.
  • Last year, Scott won the same prize on his model interpretation work, SHAP (NIPS oral presentation) and his Nature BME paper featured on the cover (see below).
    • GeekWire - From fighting Alzheimer’s to AR captions, UW computer science students show cutting-edge innovations
    • BusinessWire - Madrona Awards 2018 Madrona Prize to UW Project That Applies Machine Learning to Fighting Alzheimer’s Disease
  • 10/10/2018: Scott's Prescience paper is published as a cover article of the October issue of Nature Biomedical Engineering.
    • Nature BME Editorial - Towards trustable machine learning
    • UW News - Prescience: Helping doctors predict the future
    • GeekWire - Univ. of Washington researchers unveil Prescience, an AI system that predicts problems during surgery
    • Allen School News - “Prescience” interpretable machine-learning system for predicting complications during surgery featured in Nature Biomedical Engineering