Part mutual information for quantifying direct associations in networks

doi:10.1073/pnas.1522586113

	Part mutual information for quantifying direct associations in networks
	Zhao, Juan 1; Zhou, Yiwei1,2 ; Zhang, Xiujun 1; Chen, Luonan1,2,3
	2016-05-03
发表期刊	PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
ISSN	0027-8424
卷号	113 期号:18 页码:5130-5135
发表状态	已发表
DOI	10.1073/pnas.1522586113
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.
关键词	conditional mutual information systems biology network inference conditional independence
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61134013] ; National Natural Science Foundation of China[91439103] ; National Natural Science Foundation of China[91529303]
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000375395700060
出版者	NATL ACAD SCIENCES
WOS关键词	GENE REGULATORY NETWORKS ; DEPENDENCIES ; INFERENCE
原始文献类型	Article
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/1843
专题	生命科学与技术学院生命科学与技术学院_特聘教授组_陈洛南组生命科学与技术学院_博士生
通讯作者	Chen, Luonan
作者单位	1.Chinese Acad Sci, Univ Chinese Acad Sci, Shanghai Inst Biol Sci,Inst Biochem & Cell Biol, Innovat Ctr Cell Signaling Network,Key Lab Syst B, Shanghai 200031, Peoples R China 2.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 200031, Peoples R China 3.Univ Tokyo, Inst Ind Sci, Collaborat Res Ctr Innovat Math Modelling, Tokyo 1138654, Japan
通讯作者单位	生命科学与技术学院
推荐引用方式 GB/T 7714	Zhao, Juan,Zhou, Yiwei,Zhang, Xiujun,et al. Part mutual information for quantifying direct associations in networks[J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA,2016,113(18):5130-5135.
APA	Zhao, Juan,Zhou, Yiwei,Zhang, Xiujun,&Chen, Luonan.(2016).Part mutual information for quantifying direct associations in networks.PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA,113(18),5130-5135.
MLA	Zhao, Juan,et al."Part mutual information for quantifying direct associations in networks".PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 113.18(2016):5130-5135.