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  4. 中科院合肥智能机械研究所
Adaptive modelling of gene regulatory network using Bayesian information criterion-guided sparse regression approach

文献类型：期刊论文

作者Shi, Ming1,2; Shen, Weiming2; Wang, Hong-Qiang1; Chong, Yanwen2
刊名IET SYSTEMS BIOLOGY
出版日期2016-12-01
卷号10期号:6页码:252-259
关键词Genetics Bayes Methods Genomics Regression Analysis Inference Mechanisms Bioinformatics Adaptive Modelling Gene Regulatory Network Bayesian Information Criterion-guided Sparse Regression Approach Grn Microarray Expression Data Systems Biology Grn Reconstruction Optimisation l(1)-norm Regularisation
DOI10.1049/iet-syb.2016.0005
文献子类Article
英文摘要Inferring gene regulatory networks (GRNs) from microarray expression data are an important but challenging issue in systems biology. In this study, the authors propose a Bayesian information criterion (BIC)-guided sparse regression approach for GRN reconstruction. This approach can adaptively model GRNs by optimising the l(1)-norm regularisation of sparse regression based on a modified version of BIC. The use of the regularisation strategy ensures the inferred GRNs to be as sparse as natural, while the modified BIC allows incorporating prior knowledge on expression regulation and thus avoids the overestimation of expression regulators as usual. Especially, the proposed method provides a clear interpretation of combinatorial regulations of gene expression by optimally extracting regulation coordination for a given target gene. Experimental results on both simulation data and real-world microarray data demonstrate the competent performance of discovering regulatory relationships in GRN reconstruction.
WOS关键词QUANTITATIVE TRAIT LOCI ; ESCHERICHIA-COLI ; TRANSCRIPTION FACTOR ; EXPRESSION PROFILES ; INFERENCE ; SELECTION ; LASSO ; IDENTIFICATION
WOS研究方向Cell Biology ; Mathematical & Computational Biology
语种英语
WOS记录号WOS:000389473600007
资助机构National Natural Science Foundation of China(61374181 ; National Natural Science Foundation of China(61374181 ; National Natural Science Foundation of China(61374181 ; National Natural Science Foundation of China(61374181 ; Anhui Province Natural Science Foundation(1408085MF133) ; Anhui Province Natural Science Foundation(1408085MF133) ; Anhui Province Natural Science Foundation(1408085MF133) ; Anhui Province Natural Science Foundation(1408085MF133) ; Shanghai Aerospace Science and Technology Innovation Fund Projects(SAST201425) ; Shanghai Aerospace Science and Technology Innovation Fund Projects(SAST201425) ; Shanghai Aerospace Science and Technology Innovation Fund Projects(SAST201425) ; Shanghai Aerospace Science and Technology Innovation Fund Projects(SAST201425) ; state key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Special Research Funding ; state key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Special Research Funding ; state key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Special Research Funding ; state key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Special Research Funding ; K.C. Wong education foundation ; K.C. Wong education foundation ; K.C. Wong education foundation ; K.C. Wong education foundation ; 61272339 ; 61272339 ; 61272339 ; 61272339 ; 61402010 ; 61402010 ; 61402010 ; 61402010 ; 61572372 ; 61572372 ; 61572372 ; 61572372 ; 41271398) ; 41271398) ; 41271398) ; 41271398) ; National Natural Science Foundation of China(61374181 ; National Natural Science Foundation of China(61374181 ; National Natural Science Foundation of China(61374181 ; National Natural Science Foundation of China(61374181 ; Anhui Province Natural Science Foundation(1408085MF133) ; Anhui Province Natural Science Foundation(1408085MF133) ; Anhui Province Natural Science Foundation(1408085MF133) ; Anhui Province Natural Science Foundation(1408085MF133) ; Shanghai Aerospace Science and Technology Innovation Fund Projects(SAST201425) ; Shanghai Aerospace Science and Technology Innovation Fund Projects(SAST201425) ; Shanghai Aerospace Science and Technology Innovation Fund Projects(SAST201425) ; Shanghai Aerospace Science and Technology Innovation Fund Projects(SAST201425) ; state key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Special Research Funding ; state key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Special Research Funding ; state key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Special Research Funding ; state key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Special Research Funding ; K.C. Wong education foundation ; K.C. Wong education foundation ; K.C. Wong education foundation ; K.C. Wong education foundation ; 61272339 ; 61272339 ; 61272339 ; 61272339 ; 61402010 ; 61402010 ; 61402010 ; 61402010 ; 61572372 ; 61572372 ; 61572372 ; 61572372 ; 41271398) ; 41271398) ; 41271398) ; 41271398)
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/30755]  
专题合肥物质科学研究院_中科院合肥智能机械研究所
作者单位1.Chinese Acad Sci, Inst Intelligent Machines, Machine Intelligence & Computat Biol Lab, POB 1130, Hefei 230031, Peoples R China
2.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, 129 Luoyu Rd, Wuhan 430079, Peoples R China
推荐引用方式
GB/T 7714		 Shi, Ming,Shen, Weiming,Wang, Hong-Qiang,et al. Adaptive modelling of gene regulatory network using Bayesian information criterion-guided sparse regression approach[J]. IET SYSTEMS BIOLOGY,2016,10(6):252-259.
APA Shi, Ming,Shen, Weiming,Wang, Hong-Qiang,&Chong, Yanwen.(2016).Adaptive modelling of gene regulatory network using Bayesian information criterion-guided sparse regression approach.IET SYSTEMS BIOLOGY,10(6),252-259.
MLA Shi, Ming,et al."Adaptive modelling of gene regulatory network using Bayesian information criterion-guided sparse regression approach".IET SYSTEMS BIOLOGY 10.6(2016):252-259.

入库方式： OAI收割

来源：合肥物质科学研究院

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