应002资讯网、数学研究院邀请，深圳大学欧阳乐副研究员将于近日在公司作关于生物信息学方面的学术报告，欢迎感兴趣的师生参加！

报告题目：Joint learning of multiple differential networks based on Gaussian graphical models

报告摘要：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 graphical models under two different conditions is of great importance. However, most existing graphical models are developedfor estimating a single graph and based on a tacit assumption that there is no missing relevant variables, which wastes the common information provided by multiple heterogeneous data sets and underestimates the influence of latent/unobserved relevant variables. In this work, we propose a joint differential network analysis (JDNA) model to jointly estimate multiple differential networks with latent variables from multiple data sets. Extensive simulation experiments demonstrate that JDNA model outperforms 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 JDNA model is effective in identifying differential networks under different conditions.

报告时间：2020年10月17日13:00 -14:00

报告地点：明德楼201

报告人简介：欧阳乐，博士，副研究员，硕士研究生导师，IEEE会员。于2015年获中山大学概率论与数理统计专业理学博士学位；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、深圳市海外高层次人才（孔雀计划）C类、南山区“领航人才”C类获得者。目前已在IEEE TCYB，Bioinformatics，Pattern Recognition，IEEE/ACM TON，BMC Bioinformatics，IEEE/ACM TCBB, Methods, IEEE TCOM等国际权威期刊发表SCI论文40余篇。申请发明专利2项、获授权发明专利1项。担任Bioinformatics、PLoS Computational Biology、Briefings in Bioinformatics、IEEE/ACM TCBB等重要刊物审稿人。主持国家自然科学基金青年项目1项，广东省自然科学基金面上项目1项和深圳市基础研究项目1项。