Inference of Gene Regulatory Network Based on Local Bayesian Networks

doi:10.1371/journal.pcbi.1005024

	Inference of Gene Regulatory Network Based on Local Bayesian Networks
	Liu, Fei 1,2; Zhang, Shao-Wu 1; Guo, Wei-Feng 1; Wei, Ze-Gang 1; Chen, Luonan1,3,4
	2016-08
发表期刊	PLOS COMPUTATIONAL BIOLOGY (IF:3.8[JCR-2023],4.3[5-Year])
ISSN	1553-734X
卷号	12 期号:8
发表状态	已发表
DOI	10.1371/journal.pcbi.1005024
摘要	The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E. coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce the computational cost of BN due to much smaller sizes of local GRNs, but also identify the directions of the regulations.
收录类别	SCI ; EI
语种	英语
资助项目	JSPS KAKENHI[15H05707]
WOS研究方向	Biochemistry & Molecular Biology ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Mathematical & Computational Biology
WOS记录号	WOS:000383346100008
出版者	PUBLIC LIBRARY SCIENCE
WOS关键词	CONDITIONAL MUTUAL INFORMATION ; EXPRESSION DATA ; TRANSCRIPTIONAL REGULATION ; ESCHERICHIA-COLI ; ALGORITHM ; RECONSTRUCTION ; MODEL
原始文献类型	Article
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/1769
专题	生命科学与技术学院_特聘教授组_陈洛南组
通讯作者	Zhang, Shao-Wu
作者单位	1.Northwestern Polytech Univ, Minist Educ, Sch Automat, Key Lab Informat Fusion Technol, Xian, Peoples R China 2.Baoji Univ Arts & Sci, Inst Phys & Optoelect Technol, Baoji, Peoples R China 3.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Biochem & Cell Biol, Key Lab Syst Biol,Innovat Ctr Cell Signaling Netw, Shanghai, Peoples R China 4.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Liu, Fei,Zhang, Shao-Wu,Guo, Wei-Feng,et al. Inference of Gene Regulatory Network Based on Local Bayesian Networks[J]. PLOS COMPUTATIONAL BIOLOGY,2016,12(8).
APA	Liu, Fei,Zhang, Shao-Wu,Guo, Wei-Feng,Wei, Ze-Gang,&Chen, Luonan.(2016).Inference of Gene Regulatory Network Based on Local Bayesian Networks.PLOS COMPUTATIONAL BIOLOGY,12(8).
MLA	Liu, Fei,et al."Inference of Gene Regulatory Network Based on Local Bayesian Networks".PLOS COMPUTATIONAL BIOLOGY 12.8(2016).