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 and complex diseases such as cancer and Alzheimer's disease.
- Enabling precision cancer medicine and drug development (collaboration with Hematology, and Center for Cancer Innovation)
- Seeking cure for Alzheimer's disease (with Pathology, Neuropathology, and Internal Medicine)
- Developing interpretable deep learning techniques for genomic, multi-omic, and protein structure data
- Predicting kidney diseases (with Kidney Research Institute)
- Bringing ML to operating rooms (with Anesthesiology & Pain Medicine)
- Enabling efficient pre-hospital prediction for trauma patients (with Emergency Medicine)
- Making medical examinations more efficient (with School of Dentistry, and Global Health)
- Developing interpretable ML principles, techniques, and theories
- 10/10/2018: Scott's Prescience paper is published as a cover article of the October issue of Nature Biomedical Engineering.
- 5/3/2018: Safiye Celik's and Su-In Lee's research is featured in GeekWire.
- 4/3/2018: Hugh Chen receives the NSF Graduate Research Fellowships Program (GRFP).
- 3/27/2018: Safiye Celik's MERGE paper (Nature Communications 2018) is recommended in F1000Prime as being of special significance in its field.
- 3/21/2018: Safiye Celik's research is featured in I Am CSE.
- Su-In's lecture was selected as the best talk at CGWI 2018 (Computational Genomics Winter Institute): Interpretable Machine Learning for Precision Medicine.
- 1/3/2018: Safiye's precision oncology paper got published in Nature Communications. See Allen School News, UW Huddle, and UW Medicine News.
- 9/4/2017: Scott Lundberg's SHAP paper is accepted to Neural Information Processing Systems (NIPS) 2017 for Full Oral Presentation.
- 8/7/2017: Gabriel Erion won the Best Poster Award: "Prediction and Prevention of Perioperative Adverse Events with Machine Learning Models", University of Washington MSTP (MD/PhD program) retreat, 2017.
- 2/7/2017: Scott Lundberg's ChromNet paper (Genome Biology 2016) is recommended in F1000Prime as being of special significance in its field.
- 11/20/2016: Scott Lundberg receives the Best Paper Award at the NIPS workshop "Interpretable Machine Learning for Complex Systems".
- 8/12/2016: Javad Hosseini's GRAB paper is accepted to Neural Information Processing Systems (NIPS) 2016.
- 7/15/2016: Nao Hiranuma's CloudControl paper is accepted to ACM Conference on Bioinformatics, Computational Biology (ACM-BCB) 2016.
- 7/6/2016: Safiye Celik's INSPIRE paper is featured in Casey Greene. The future is unsupervised. Science Translational Medicine July 2016.
- 6/10/2016: Safiye Celik's INSPIRE work is published in Genome Medicine June 2016.
- 5/4/2016: Maxim Grechkin's DISCERN work is published in PLOS Computational Biology May 2016.
- 4/30/2016: Scott Lundberg's ChromNet work is published in Genome Biology April 2016. See ChromNet browser.
- 2/8/2016: Javad Hosseini got ranked #1 in the DiMSUM competition. See Hosseini, Smith and Lee. NAACL Workshop SemEval 2016 Task10.