Explainable Artificial Intelligence for Medicine and Science (AIMS)

Paul G. Allen School Computer Science & Engineering

Research keywords:

  • Computational biology & medicine (bioinformatics) - precision medicine, systems biology, & genetics

  • Machine learning - explainable AI, interpretability, feature selection, & probabilistic graphical models

Modern machine learning (ML) models can accurately predict patient progress, an individual's phenotype, or molecular events such as transcription factor binding. However, they do not explain why selected features make sense or why a particular prediction was made. For example, a model may predict that a patient will get chronic kidney disease, which can lead to kidney failure. The lack of explanations about which features drove the prediction – e.g., high systolic blood pressure, high BMI, or others – hinders medical professionals in making diagnoses and decisions on appropriate clinical actions. Our lab seeks to develop approaches based on explainable artificial intelligence (AI) and machine learning (ML) for biology and medicine.

Some of our highlighted research include (i) finding therapeutic targets for Alzheimer’s disease (press articles), (ii) treating cancer based on a patient’s own molecular profile (Nature Communications 2018; Selected by F1000), (iii) core ML work on explainable AI (NeurIPS Dec 2017, which got full Oral (top 1%) and was cited 233 times as of June, 2019; and Nature Machine Intelligence , cover article), and (iv) preventing complications during surgery (Nature BME 2018, cover article), (v) predicting kidney diseases (Nature Machine Intelligence, cover article), (vi) enabling pre-hospital predictions for trauma patients, and (vii) improving our understanding of pan-cancer biology, (viii) human genome, and (ix) gene regulatory networks. Lee Lab is collaborating with biomedical researchers in UW School of Medicine, Allen Institutes, Harvard Medical School, and so on.


  • 3/27/2020: Will Chen receives the prestigious Mary Gates Research Scholarship.

  • 1/17/2020: Scott's TreeExplainer is published as a cover article of the January issue of Nature Machine Intelligence.

    • Allen School News - Seeing the forest for the trees: UW team advances explainable AI for popular machine learning models used to predict human disease and mortality risks.

  • 11/20/2019: Gabe won the Madrona Prize (1st place) at the 2019 Allen School Annual Research Day.

    • "CoAI: Cost-Aware Artificial Intelligence for Health Care"

    • Safiye and Scott won this prize in 2018 and 2017, respectively.

  • 11/19/2019: Scott's SHAP paper (NeurIPS, Dec 2017) is cited 500 times over <2 years after publication.

  • 2/21/2019: Nao's AIControl paper got accepted for publication in Nucleic Acids Research (IF: 11.56). See Allen School News.

  • 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: Safiye's EMBARKER project on identifying therapeutic targets for Alzheimer's disease won the Madrona Prize (1st place) at 2018 Allen School Annual Research Day.

    • 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

    • Last year, Scott won this prize on his model interpretation work, SHAP (NIPS oral presentation) and his Nature BME paper featured on the cover (see below).

  • 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

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.