Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks

	Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks
	Zhang, XJ; Zhao, J; Hao, JK; Zhao, XM; Chen, LN
刊名	NUCLEIC ACIDS RESEARCH
	2015
卷号	43 期号:5 页码:e31-e31
通讯作者	Chen, LN (reprint author), Chinese Acad Sci, Key Lab Syst Biol, Inst Biochem & Cell Biol, Shanghai Inst Biol Sci, Shanghai 200031, Peoples R China.,xm_zhao@tongji.edu.cn ; lnchen@sibs.ac.cn
英文摘要	Mutual information (MI), a quantity describing the nonlinear dependence between two random variables, has been widely used to construct gene regulatory networks (GRNs). Despite its good performance, MI cannot separate the direct regulations from indirect ones among genes. Although the conditional mutual information (CMI) is able to identify the direct regulations, it generally underestimates the regulation strength, i.e. it may result in false negatives when inferring gene regulations. In this work, to overcome the problems, we propose a novel concept, namely conditional mutual inclusive information (CMI2), to describe the regulations between genes. Furthermore, with CMI2, we develop a new approach, namely CMI2NI (CMI2-based network inference), for reverse-engineering GRNs. In CMI2NI, CMI2 is used to quantify the mutual information between two genes given a third one through calculating the Kullback-Leibler divergence between the postulated distributions of including and excluding the edge between the two genes. The benchmark results on the GRNs from DREAM challenge as well as the SOS DNA repair network in Escherichia coli demonstrate the superior performance of CMI2NI. Specifically, even for gene expression data with small sample size, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the regulation strength between genes. As a case study, CMI2NI was also used to reconstruct cancer-specific GRNs using gene expression data from The Cancer Genome Atlas (TCGA).
学科主题	Biochemistry & Molecular Biology
类目[WOS]	Biochemistry & Molecular Biology
关键词[WOS]	HUMAN B-CELLS ; TRANSCRIPTIONAL REGULATION ; INFERENCE METHODS ; ESCHERICHIA-COLI ; COMPLEX DISEASES ; EXPRESSION ; IDENTIFICATION ; BIOMARKERS ; ALGORITHM ; PATHWAYS
收录类别	SCI
语种	英语
WOS记录号	WOS:000352487100004
内容类型	期刊论文
版本	出版稿
源URL	[http://202.127.25.143/handle/331003/60]
专题	上海生化细胞研究所_上海生科院生化细胞研究所
推荐引用方式 GB/T 7714	Zhang, XJ,Zhao, J,Hao, JK,et al. Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks[J]. NUCLEIC ACIDS RESEARCH,2015,43(5):e31-e31.
APA	Zhang, XJ,Zhao, J,Hao, JK,Zhao, XM,&Chen, LN.(2015).Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks.NUCLEIC ACIDS RESEARCH,43(5),e31-e31.
MLA	Zhang, XJ,et al."Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks".NUCLEIC ACIDS RESEARCH 43.5(2015):e31-e31.