报告时间：2026年5月19日（周二）上午 9:30

报告地点：陕西师范大学文津楼3425报告厅

邀请人：雷秀娟 教授

报告题目：Deep Tensor Factorization for Computational Drug Development

报告人：吴方向 教授

报告摘要:

Traditional drug development is an expensive process that typically requires the investment of a huge amount of resources in terms of finances, equipment, and time. To overcome the limitations of this process, computational drug discovery becomes attractive due to its flexible and economic nature. Drug repositioning, which aims to find new applications for existing drugs, is one of the promising approaches in computational drug discovery. In this talk, after brief introductions to computational drug discovery, I present several (deep) tensor factorization methods developed by my research group in past years for computational drug discovery, including nonnegative matrix factorization (NMF) for mRNA-drug interaction prediction, nonnegative matrix tri-factorization (NMTF) for drug-target interaction prediction, Nonnegative tensor factorization (NTF) for drug repositioning, and Deep matrix/tensor factorization for biomarker discovery.

报告人简介:

吴方向，现任加拿大萨斯喀彻温大学计算机科学系、生物医学工程系、机械工程系终身教授，EIC Fellow、IEEE Fellow、IET Fellow和AAIA Fellow，荣获萨斯喀彻温大学杰出研究者奖（该校最高科研荣誉）。主要研究方向涵盖人工智能、机器学习与深度学习、生物信息学、计算生物学、健康信息学、医学图像分析、AI药物发现、复杂网络分析等前沿领域。累计发表期刊及会议论文500余篇，谷歌学术总引用量超19800次，H指数76。同时担任了多个国际期刊编委（如Neurocomputing、IEEE Transactions on Computational Biology and Bioinformatics等），多次担任国际期刊特邀主编，在众多顶级国际学术会议中出任程序委员会主席或委员。