Lee Lab of

Artificial Intelligence for Biological and Medical Sciences (AIMS)

The AIMS lab, led by Su-In Lee, aims to conceptually and fundamentally advance how AI/ML can be integrated with biomedical sciences by addressing novel, forward-looking and stimulating questions, enabled by advancing foundational AI/ML or applying advanced AI/ML methods. The AIMS lab’s recent research focuses on a broad spectrum of problems, including developing explainable AI (a.k.a. interpretable ML) techniques, identifying the cause and treatment of challenging diseases such as cancer and Alzheimer’s disease, and developing and auditing clinical AI models.

AI models, such as deep neural networks, are transforming biomedical sciences; however, their black-box nature has been a well-known bottleneck impeding the widespread adoption of AI in biomedicine. For example, these models do not answer the key questions in biology and medicine, such as mechanistic explanations or causal relationships for biological understanding, drug development, or clinical decisions.

When the primary focus of AI applications in biology and medicine was in accurately predicting patient's outcome or individual's phenotype (e.g., predicting the response to certain chemotherapy based on the patient's gene expression profile), we uniquely focused on why a certain prediction was made, which can help medical professions make diagnoses or decisions on appropriate clinical actions or point to the molecular mechanisms underlying an individual phenotype. This line of our work has led to highly cited publications: (1) the cover article of Nature Biomedical Engineering, Oct 2018 (cited 174 times over 2 years), (2) the cover article of Nature Machine Intelligence, Jan 2020 (cited 196 times over 1 year; two preprint versions cited 289 and 51 times), and (3) an article in Nature Communications, Jan 2018 (cited 75 times over 3 years; recommended by F1000). Our foundational AI research on interpreting an AI prediction led to (4) a paper on the SHAP framework, which was selected for oral presentation (top 1%) in NeurIPS (Neural Information Processing Systems) in December 2017 (cited 2,048 times over 3 years), which is broadly used by scientists in medicine, biology, finance, computer science, etc (ODSC Open Data Science Award'19).

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.

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

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


  • 3/23/2021: Ethan Weinberger receives the NSF Graduate Research Fellowship (GRF).

  • 6/16/2020: Gabe Erion receives the F30 fellowship from NIH.

  • 3/27/2020: Will Chen receives the 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.