Speaker
Description
In this talk, we present our research on using mathematical modeling and machine learning to characterize the evolutionary dynamics of the tumor immune microenvironment (TIME) for personalized cancer therapy. We developed various ODE-based TIME models, integrating genomic and transcriptomic data. These models, enhanced by deep reinforcement learning (DRL), optimize therapy regimens, including intermittent androgen deprivation therapy (IADT) for prostate cancer and personalized schedules for immune checkpoint inhibitors (ICIs) and chemotherapy. Our findings demonstrate the potential of these approaches to improve patient outcomes by tailoring treatments to individual tumor dynamics.
Bibliography
Qingpeng ZHANG is an Associate Professor at the Musketeers Foundation Institute of Data Science and the Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, University of Hong Kong. He is a Senior Member of the IEEE and a Fellow of the Royal Society of Medicine. He serves as an Associate Editor for npj Digital Medicine, BMJ Mental Health, INFORMS Journal on Data Science, IEEE TCSS, and IEEE TITS. Dr. Zhang’s research centers on developing knowledge-enhanced, AI-driven predictive decision analytics methods. These methods aim to dissect high-dimensional biological, clinical, and behavioral data to contribute to drug discovery, precision medicine, and public health. His work has appeared in journals such as Nature Human Behaviour, Nature Communications and PNAS, and has been highlighted in media outlets such as The Washington Post, The New York Times, The Guardian, BBC, CNN, The Times, and Ming Pao.
