内容簡介：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.

報告人：張清鵬

報告人簡介：香港大學同心基金數據科學研究院和醫學院藥理及藥劑學系副教授，曾任教于香港城市大學數據科學學院。他在亞利桑那大學獲得系統工業工程博士學位，本科畢業于華中科技大學自動化專業，在倫斯勒理工學院計算機系從事博士後研究。張博士長期緻力于基于網絡化知識的精準醫學研究，并在藥物發現、公共衛生和健康管理等領域取得成功應用。張博士的研究成果發表在Nature Human Behaviour、Nature Communications、PNAS、Proceedings of the Royal Society A、MIS Quarterly等期刊上，并獲得華盛頓郵報、紐約時報、衛報等中外媒體的廣泛報道。張博士是IEEE Senior Member和Royal Society of Medicine Fellow，并擔任npj Digital Medicine, BMJ Mental Health, INFORMS Journal on Data Science, IEEE TCSS和IEEE TITS編委。

時  間：2024年8月31日（周六）下午14：00開始

地  點：騰訊會議号54238559950

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