2016.05.09 Part mutual information for quantifying direct associations in networks

2019-07-07 00:31:01 2

北京大学定量生物学中心

学术报告

题 目:Part mutual information for quantifying direct associations in networks

报告人: 陈洛南 研究员

        中国科学院系统生物学重点实验室

        中国科学院上海生命科学研究院

时 间:2016-5-9(周一),13:00-14:00

地 点:北京大学老化学楼东配楼一层101报告厅

主持人:汤超 教授

 

摘 要:

Quantitatively identifying direct dependencies between variables is an important task in data analysis, in particular for reconstructing various types of networks and causal relations in science and engineering. One of the most widely used criteria is partial correlation, but it can only measure linearly direct association and miss nonlinear associations. However, based on conditional independence, conditional mutual information (CMI) is able to quantify nonlinearly direct relationships among variables from the observed data, superior to linear measures, but suffers from a serious problem of underestimation, in particular for those variables with tight associations in a network, which severely limits its applications. In this work, we propose a new concept, partial independence, with a new measure, part mutual information (PMI), which not only can overcome the problem of CMI but also retains the quantification properties of both mutual information (MI) and CMI. Specifically, we first defined PMI to measure nonlinearly direct dependencies between variables and then derived its relations with MI and CMI. Finally, we used a number of simulated data as benchmark examples to numerically demonstrate PMI features and further real gene expression data from Escherichia coli and yeast to reconstruct gene regulatory networks, which all validated the advantages of PMI for accurately quantifying nonlinearly direct associations in networks.

 

报告人简介:

  陈洛南研究员1984年获华中科技大学电气工程学士学位;1988年获日本东北大学系统科学硕士学位;1991年获日本东北大学系统科学博士学位。1997年起任日本大阪产业大学副教授;2000年起任美国加州大学洛杉矶分校(UCLA)访问教授;2002年起任日本大阪产业大学教授;2007年筹建上海大学系统生物技术研究所并首任所长(兼);20094月起任日本东京大学(兼)研究教授;200910月至今任中科院系统生物学重点实验室执行主任,研究员,博士生导师,研究组组长。研究方向为计算系统生物学及网络生物学,主要从事生物信息学、网络生物学及计算系统生物学等领域的研究工作。