讲座题目：Detecting Causality from Nonlinear Dynamics with Short-term Time Series
讲座人: 陈洛南 教授
讲座时间：11:00，2017-11-27
地点：理科楼321
摘要：Quantifying causality between variables from observed time series data is of great importance in various disciplines but also a challenging task, especially when the observed data are short. Unlike the conventional methods, we developed a new framework to detect causality only with very short time series data, based on embedding theory of an attractor for nonlinear dynamics. Specifically, we first show that measuring the smoothness of a cross map between two observed variables can be used to detect a causal relation. Then, we provide a very effective algorithm to computationally evaluate the smoothness of the cross map, or ‘‘Cross Map Smoothness’’ (CMS), and thus to infer the causality, which can achieve high accuracy even with very short time series data. Analysis of both mathematical models from various benchmarks and real data from biological systems validates our method.
讲座人简介：陈洛南，中国科学院上海生命科学研究院系统生物学重点实验室执行主任。中国运筹学会《计算系统生物学分会》理事长，IEEE-SMC《系统生物学委员会》主席，中国细胞生物学会《功能基因组学与系统生物学分会》副会长，中国药理学会《网络药理学专业委员会》副主任委员。国家基金委重大研究计划专家组，国家重点研发计划重点专项首席科学家。近年，在计算系统生物学和复杂疾病研究领域发表了300篇余SCI期刊论文及10余部专著及编著书籍(h-index:52;i10-index:221)，被引12000余次，开发生物网络30余软件，详细文章目录见主页：http://sysbio.sibcb.ac.cn/cb/chenlab/LuonanChen.htm.
