A workshop as part of the Pacific Symposium on Biocomputing 2023

Risk prediction: Methods, Challenges, and Opportunities

The following speakers have accepted our invitation:

• David Conti (University of Southern California)

• Yong Chen (University of Pennsylvania)

• Dana Crawford (Case Western Reserves University)

• Rui Duan (Harvard University)

• Lifang He (Lehigh University)

• Ruowang Li (Cedars Sinai Medical Center)

• Shannon Lynch (Fox Chase Cancer Center)

• William La Cava (Boston Children’s hospital/Harvard Medical School)

Presentation and discussion topics include:

• Cancer Risk Prediction: Existing Models, Machine Learning, and Potential Roadblocks in Translating Models into Personalized Clinical Tools

• Challenges and methods for risk prediction using clinical data

• Challenges of making risk prediction models fair

• Interpretable Machine Learning for Biomedical Imaging Analysis

• Integrated Analysis of Multi-Omic data

• On population diversity to better ensure risk prediction equity

• Polygenic risk vectors improve risk stratification for cardio-metabolic diseases

• Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach

Workshop Organizers

Rui Duan, Ph.D. is an Assistant Professor of Biostatistics at the Harvard T.H. Chan School of Public Health. Her research interests focus on developing statistical and machine learning methods for effective use of biomedical data, in order to generate reliable evidence and knowledge that enable precise and accurate diagnostics, support clinical decision making, and optimize individualized treatments. Specifically, her lab focuses on predictive models based on electronic health records (EHR) and EHR-linked biobanks, federated learning and meta-analysis methods for effective evidence synthesis and data integration, and methods to account for suboptimality of real-world data, including missing data and measurement errors.

Lifang He, Ph.D. is an Assistant Professor in the Department of Computer Science and Engineering at Lehigh University. Her group focuses on developing advanced computational methods for biomedical research such as on understanding disease mechanisms, diagnosis, prognosis, disease biomarkers, and disease pathways. Her research interests broadly include machine learning computational medical imaging, AI for health, tensor computing, and multimodal analysis.

Ruowang Li, Ph.D. is an Assistant Professor in the Department of Computational Biomedicine at Cedars Sinai Medical Center. His lab focuses on developing computational methods to extract knowledge from large-scale population-level data, such as biobank-linked electronic health record data. His area of research includes multi-omics data integration, federated learning of patients’ data, genetic risk prediction, and genomephenome associations.

Jason H. Moore, Ph.D. is a biomedical informatician and founding Chair of the Department of Computational Biomedicine at Cedars-Sinai Medical Center in Los Angeles. His research on artificial intelligence methods for the analysis of biomedical data has been continuously funded the NIH for more than 20 years. He has been a pioneer in the development of automated machine learning methods for risk prediction in populationbased studies and samples derived from electronic health records. He is an elected fellow of the American College of Medical Informatics, the International Academy of Health Sciences Informatics, the American Statistical Association, the International Statistics Institute, and the American Association for Advancement of Science. He is Editor-in-Chief of the open-access journal BioData Mining.