School of Biomedical Sciences cordially invites you to join the following seminar:

Date: 10 May 2024 (Friday)
Time: 4:00 pm – 5:00 pm
Venue: Lecture Theatre 1, G/F, William M.W. Mong Block, 21 Sassoon Road

Speaker: Professor Qingpeng Zhang, Associate Professor, Musketeers Foundation Institute of Data Science and Department of Pharmacology and Pharmacy, HKU
Talk Title: Biology-inspired machine learning approach to characterizing the mechanism of drug action

Biography

Professor Qingpeng Zhang is an Associate Professor at HKU, affiliated with the Musketeers Foundation Institute of Data Science and the Department of Pharmacology and Pharmacy. He joined HKU in August 2023, after serving as an Associate Professor at the School of Data Science of CityU. He obtained his Ph.D. degree in Systems and Industrial Engineering from the University of Arizona and conducted his postdoctoral research in the Department of Computer Science at Rensselaer Polytechnic Institute. He is a senior member of IEEE, and an associate editor for BMJ Mental Health, IEEE TITS, and IEEE TCSS.

His research focuses on medical informatics, AI in drug discovery, healthcare data analytics and network science. He has published in top journals such as Nature Human Behaviour, Nature Communications, PNAS, and MIS Quarterly, and his work has been featured in media outlets such as The Washington Post, The New York Times, New York Public Radio, The Guardian and Ming Pao. He has received several awards for his research excellence, including The President’s Award (2022) and the Outstanding Research Award (2021) from CityU and the Andrew P. Sage Best Transactions Paper Award (2021) from IEEE.

Abstract
Drug discovery is a challenging and costly process that requires a deep understanding of the mechanism of drug action (MODA), which is how a drug affects the biological system at the molecular level. In this talk, I will present our recent studies on using a network-based machine learning approach to characterize MODA by analyzing a comprehensive biological network that captures the complex high-dimensional molecular interactions between genes, proteins and chemicals. I will show that our methods outperform state-of-the-art machine learning baselines in predicting MODA. I will also demonstrate that our methods can identify explicit critical paths that are consistent with clinical evidence, and explain how these paths reveal the underlying biological mechanisms of drug action. Our research provides a novel interpretable artificial intelligence perspective on drug discovery, and has the potential to facilitate the development of new and effective drugs.

ALL ARE WELCOME

Should you have any enquiries, please feel free to contact Miss Crystal Chan at 3917 6830.