报 告 人：陈洛南 教授[中国科学院上海生命科学研究院研究员、博士生导师、中国科学院系统生物学重点实验室执行主任]
报告时间：2014年9月24日（周三）15:00
报告地点：上海大学校本部东区计算机大楼1001室
邀 请 人：谢江 博士
报告简介：
Biomarker discovery is one of the major topics in translational biomedicine study based on high-throughput biological data analysis. Traditional methods focus on differentially expressed genes (or node-biomarkers) but ignore non-differentials. However, non-differentially expressed genes also play important roles in the biological processes and the rewired interactions / edges among non-differential genes may reveal fundamental difference between variable conditions. Therefore, it is necessary to identify relevant interactions or gene pairs to elucidate the molecular mechanism of complex biological phenomena, e.g. distinguish different phenotypes. To address this issue, we proposed a new method based on a new vector representation of an edge, EdgeMarker, to (1) identify edge-biomarkers, i.e. the differentially correlated gene pairs with optimal classification ability, and (2) transform the ‘node expression’ data in node space into the ‘edge expression’ data in edge space and classify the phenotype of each single sample in edge space, which generally cannot be achieved in traditional methods. Unlike the traditional methods which analyze the node space (i.e. molecular expression space) or higher dimensional space using arbitrary kernel methods, this study provides an exact mathematical model to explore the edge space (i.e. correlation space) for classification of a single sample. In this work, we show that the identified edge-biomarkers indeed have strong ability in distinguishing normal and disease samples even when all involved genes are not significantly differentially expressed. The analysis of human cholangiocarcinoma dataset and diabetes dataset also suggested that the identified edge-biomarkers may cast new biological insight into the pathogenesis of human complex diseases.
报告人简介：
陈洛南教授于1984年华中科技大学电气工程系学士学位，1991年从日本东北大学获得电气工程博士学位。近年来，主要工作有网络系统生物学、合成系统生物学、计算系统生物学3个方面，采用系统工程、动力学分析、优化和数学建模的方式，结合生物信息学和现代生命科学实验，以生物复杂网络和动态行为为主线来研究生命系统。目前，在计算系统生物学领域的主要国际学术期刊都担任重要工作，在日本和美国从事科研教学工作等25年以上。近五年多，在系统生物学及系统临床医学等研究领域发表了100篇以上原创性期刊论文，著书11部，主持了3个国家级项目。