报告题目：Joint Learning of Multiple Gene Networks based on Gaussian Graphical Models
报 告 人：欧阳乐
报告地点：腾讯会议750-549-502
报告时间：2022年10月28日下午14:30
主 持 人：郭菲
报告简介：
Gaussian Graphical models have been widely used to learn the conditional dependence structures among random variables. In many controlled experiments, such as the studies of disease or drug effectiveness, learning the structural changes of gene networks under different conditions is of great importance. However, most existing graphical models are developed for estimating a single graph and based on a tacit assumption that the differences between networks are driven by individual edges, and there is no missing relevant variables, which wastes the common information provided by multiple heterogeneous data sets, and may fail in identifying driver genes that lead to the changes of networks, and underestimates the influence of latent/unobserved relevant variables. In this talk, to address the above problems, I will introduce several Gaussian graphical model-based network inference methods. Extensive simulation experiments demonstrate that these models outperform other state-of-the-art methods in estimating the structural changes of graphical models. Moreover, a series of experiments on several real-world data sets have been performed and experiment results consistently show that our proposed methods are effective in identifying gene networks under different conditions.
讲者简介：欧阳乐，深圳大学副教授，博士生导师。于2015年6月获中山大学理学博士学位；2013年-2014年，赴新加坡南洋理工大学计算机科学系交流访问。2015年-2016年在香港城市大学电子工程系从事博士后研究。主要从事数据挖掘、机器学习和生物信息学等领域的科研和教学工作。广东省珠江人才计划青年拔尖人才和深圳大学荔园优青获得者。主持多项国家级和省部级科研项目，已在IEEE TCYB、Bioinformatics、Briefings in Bioinformatics、Pattern Recognition等国际期刊发表SCI论文50余篇。担任Bioinformatics、PLoS Computational Biology、Briefings in Bioinformatics、IEEE JBHI、Communication Biology等重要刊物审稿人，AAAI、IJCAI、ICML等国际学术会议程序委员会委员。
