Alzheimer's disease therapeutic target


Naozumi Hiranuma, Scott M. Lundberg, and Su-In Lee*. AIControl: Replacing matched control experiments with machine learning improves ChIP-seq peak identification. Nucleic Acids Research [Paper] [Github]

  • Allen School News - With AIControl, Allen School researchers replace biological experiments with AI to better understand the human genome

Explainable machine learning

Scott M. Lundberg, and Su-In Lee. A unified approach to interpreting model predictions. Neural Information Processing Systems (NeurIPS) December, 2017 Oral Presentation [Paper in arxiv] [GitHub]

  • Our SHAP paper received the Madrona Prize at the Allen School 2017 Industry Affiliates Annual Research Day.

  • Our SHAP paper got cited 100 times within the first one year after publication.


Scott M. Lundberg, Bala Nair, Monica S. Vavilala, Mayumi Horibe, Michael J. Eisses, Trevor Adams, David E. Liston, Daniel King-Wai Low, Shu-Fang Newman, Jerry Kim, and Su-In Lee*. Explainable machine learning predictions to help anesthesiologists prevent hypoxemia during surgery. [Paper] Nature BME 2, 749–760 (2018) - Featured on the Cover

  • 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

Cancer precision medicine

Su-In Lee*,C, Safiye CelikC , Benjamin A. Logsdon, Scott M. Lundberg, Timothy J. Martins, Vivian M. Oehler, Elihu H. Estey, Chris P. Miller, Sylvia Chien, Akanksha Saxena, C. Anthony Blau, and Pamela S. Becker. A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nature Communications 9, Article number: 42 2018 [Paper] [MERGE website]