| 演讲者 | Lei Xie | | 头衔职位 | 教授，The City University of New York | | 时间 | 2019 年 11 月 27 日（周三）上午 10 点到 11 点 | | 地点 | 邯郸校区逸夫楼 407 | | 承办单位 | 上海市智能信息处理重点实验室
复旦大学计算机学院
| | 联系人 | 朱山风，zhusf@fudan.edu.cn |
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演讲简介
Genome-Wide Association Studies, whole genome sequencing, and high-throughput techniques have generated vast amounts of diverse omics and phenotypic data. However, these sets of data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along the one-drug-one-gene-one-disease paradigm. In order to tackling complex diseases such as cancer and Alzheimer’s disease, we need to target complex biological system under individual genetic and environmental background. In this talk, I will present our recent work in developing a data-driven framework for the multi-scale modeling of molecular interaction networks, and applying it to drug discovery and precision medicine. Specifically, I will introduce several new deep learning algorithms that are developed in my lab for addressing challenges in drug discovery. They include graph attention neural network for molecular representation, Geometric invariant neural network for 3D object recognition, and multi-level information transfer learning for data integration. Furthermore, I will highlight the importance in integrating mechanism-based modeling and deep learning for knowledge discovery.
关于讲者
Dr. Lei Xie is currently a professor in Computer Science, Biology, and Biochemistry at The City University of New York, and Adjunct Professor in Neuroscience at Weill Cornell Medicine, Cornell University. His research focuses on developing new methods in omics data integration, machine learning, systems biology, and computational simulation for multi-scale modeling of causal genotype-phenotype associations and drug actions, and applying them to drug discovery and precision medicine. From 2000 to 2011, he was a principle scientist at San Diego Supercomputer Center (SDSC), research scientist in pharmaceutical company Hoffmann-La Roche, and Vice President at biotechnology start-up Eidogen. He was trained in Computational Biology and Biophysics as a postdoctoral fellow at Columbia University and Howard Hughes Medical Institute. He obtained his Ph.D. in Medicinal Chemistry and M.S. in Computer Science from Rutgers University, and B.S. in Polymer Physics from University of Science and Technology of China.
