Seminars

Abstract: Reliable and accurate prediction tools are essential for advancing precision medicine. With the rapid accumulation of diverse biomedical data—both in scale and variety—there is a growing opportunity to leverage these data for clinical decision making. This talk highlights advancements in breast cancer prognosis and polygenic risk prediction through large-scale data integration. First, we validated the PREDICT Breast v3 prognostic model in a cohort of over 860,000 breast cancer patients, demonstrating robust overall performance while identifying areas for model refinement. Building on these insights, we develop PREDICT Breast v4, a machine learning-based model that integrates clinical and socioeconomic data to improve predictive accuracy. Additionally, we introduce the S4-Multi model, a cross-ancestry polygenic risk model designed for multiple phenotypes and biobanks. Together, these studies highlight the potential of integrating diverse datasets to improve risk assessment, inform clinical decisions, and optimize personalized treatment strategies.

Bio: Dr. Pei-Chen Peng is an Assistant Professor in the Department of Computational Biomedicine at Cedars-Sinai Medical Center. Her research focuses on machine learning and statistical modeling of heterogenous multi-omics data to improve the prevention and treatment of cancer and other diseases. She obtained her Ph.D. in Computer Science from University of Illinois at Urbana-Champaign and holds a M.S. and a B.S. in Computer Science from National Taiwan University. She received the NIH/NCI Early K99/R00 Pathway to Independence Award in 2021 and was recognized as a Rising Star in Electrical Engineering and Computer Sciences. 

Abstract: TBA