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Article: A new multiple regression approach for the construction of genetic regulatory networks
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TitleA new multiple regression approach for the construction of genetic regulatory networks
 
AuthorsZhang, SQ2
Ching, WK1
Tsing, NK1
Leung, HY1
Guo, D3
 
KeywordsGene regulatory network
Multiple regression
Power-law
Statistical tests
 
Issue Date2010
 
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/artmed
 
CitationArtificial Intelligence In Medicine, 2010, v. 48 n. 2-3, p. 153-160 [How to Cite?]
DOI: http://dx.doi.org/10.1016/j.artmed.2009.11.001
 
AbstractObjective: Re-construction of a genetic regulatory network from a given time-series gene expression data is an important research topic in systems biology. One of the main difficulties in building a genetic regulatory network lies in the fact that practical data set has a huge number of genes vs. a small number of sampling time points. In this paper, we propose a new linear regression model that may overcome this difficulty for uncovering the regulatory relationship in a genetic network. Methods: The proposed multiple regression model makes use of the scale-free property of a real biological network. In particular, a filter is constructed by using this scale-free property and some appropriate statistical tests to remove redundant interactions among the genes. A model is then constructed by minimizing the gap between the observed and the predicted data. Results: Numerical examples based on yeast gene expression data are given to demonstrate that the proposed model fits the practical data very well. Some interesting properties of the genes and the underlying network are also observed. Conclusions: In conclusion, we propose a new multiple regression model based on the scale-free property of real biological network for genetic regulatory network inference. Numerical results using yeast cell cycle gene expression dataset show the effectiveness of our method. We expect that the proposed method can be widely used for genetic network inference using high-throughput gene expression data from various species for systems biology discovery. © 2009 Elsevier B.V.
 
ISSN0933-3657
2013 Impact Factor: 1.356
2013 SCImago Journal Rankings: 0.865
 
DOIhttp://dx.doi.org/10.1016/j.artmed.2009.11.001
 
ISI Accession Number IDWOS:000275844400010
Funding AgencyGrant Number
HKRGC7017/07P
HKUCRGC
HKU Strategy Research Theme fund on Computational Sciences
Hung Hing Ying Physical Research Sciences Research
National Natural Science Foundation of China10971075
10901042
National Natural Science Foundation of Guangdong915102240-1000002
Doctoral Fund of Ministry of Education of China
Shanghai Municipal Education Commission
Shanghai Education Development Foundation
Funding Information:

The authors would like to thank the anonymous referees and the editor for their helpful suggestions and corrections. Research supported in part by HKRGC Grant No. 7017/07P, HKUCRGC Grants, HKU Strategy Research Theme fund on Computational Sciences, Hung Hing Ying Physical Research Sciences Research Grant, National Natural Science Foundation of China Grant No. 10971075 and National Natural Science Foundation of Guangdong Grant No. 915102240-1000002. SQ is supported by National Natural Science Foundation of China Grant No. 10901042, 10971075, Doctoral Fund of Ministry of Education of China, 'Chen Guang' project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation.

 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorZhang, SQ
 
dc.contributor.authorChing, WK
 
dc.contributor.authorTsing, NK
 
dc.contributor.authorLeung, HY
 
dc.contributor.authorGuo, D
 
dc.date.accessioned2010-09-06T07:08:47Z
 
dc.date.available2010-09-06T07:08:47Z
 
dc.date.issued2010
 
dc.description.abstractObjective: Re-construction of a genetic regulatory network from a given time-series gene expression data is an important research topic in systems biology. One of the main difficulties in building a genetic regulatory network lies in the fact that practical data set has a huge number of genes vs. a small number of sampling time points. In this paper, we propose a new linear regression model that may overcome this difficulty for uncovering the regulatory relationship in a genetic network. Methods: The proposed multiple regression model makes use of the scale-free property of a real biological network. In particular, a filter is constructed by using this scale-free property and some appropriate statistical tests to remove redundant interactions among the genes. A model is then constructed by minimizing the gap between the observed and the predicted data. Results: Numerical examples based on yeast gene expression data are given to demonstrate that the proposed model fits the practical data very well. Some interesting properties of the genes and the underlying network are also observed. Conclusions: In conclusion, we propose a new multiple regression model based on the scale-free property of real biological network for genetic regulatory network inference. Numerical results using yeast cell cycle gene expression dataset show the effectiveness of our method. We expect that the proposed method can be widely used for genetic network inference using high-throughput gene expression data from various species for systems biology discovery. © 2009 Elsevier B.V.
 
dc.description.naturepostprint
 
dc.identifier.citationArtificial Intelligence In Medicine, 2010, v. 48 n. 2-3, p. 153-160 [How to Cite?]
DOI: http://dx.doi.org/10.1016/j.artmed.2009.11.001
 
dc.identifier.citeulike6358303
 
dc.identifier.doihttp://dx.doi.org/10.1016/j.artmed.2009.11.001
 
dc.identifier.eissn1873-2860
 
dc.identifier.epage160
 
dc.identifier.hkuros168941
 
dc.identifier.isiWOS:000275844400010
Funding AgencyGrant Number
HKRGC7017/07P
HKUCRGC
HKU Strategy Research Theme fund on Computational Sciences
Hung Hing Ying Physical Research Sciences Research
National Natural Science Foundation of China10971075
10901042
National Natural Science Foundation of Guangdong915102240-1000002
Doctoral Fund of Ministry of Education of China
Shanghai Municipal Education Commission
Shanghai Education Development Foundation
Funding Information:

The authors would like to thank the anonymous referees and the editor for their helpful suggestions and corrections. Research supported in part by HKRGC Grant No. 7017/07P, HKUCRGC Grants, HKU Strategy Research Theme fund on Computational Sciences, Hung Hing Ying Physical Research Sciences Research Grant, National Natural Science Foundation of China Grant No. 10971075 and National Natural Science Foundation of Guangdong Grant No. 915102240-1000002. SQ is supported by National Natural Science Foundation of China Grant No. 10901042, 10971075, Doctoral Fund of Ministry of Education of China, 'Chen Guang' project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation.

 
dc.identifier.issn0933-3657
2013 Impact Factor: 1.356
2013 SCImago Journal Rankings: 0.865
 
dc.identifier.issue2-3
 
dc.identifier.openurl
 
dc.identifier.pmid19963359
 
dc.identifier.scopuseid_2-s2.0-77951623857
 
dc.identifier.spage153
 
dc.identifier.urihttp://hdl.handle.net/10722/75188
 
dc.identifier.volume48
 
dc.languageeng
 
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/artmed
 
dc.publisher.placeNetherlands
 
dc.relation.ispartofArtificial Intelligence in Medicine
 
dc.relation.referencesReferences in Scopus
 
dc.rightsArtificial Intelligence in Medicine. Copyright © Elsevier BV.
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.subject.meshArtificial Intelligence
 
dc.subject.meshGene Expression Regulation, Fungal
 
dc.subject.meshGene Regulatory Networks
 
dc.subject.meshModels, Genetic
 
dc.subject.meshModels, Statistical
 
dc.subjectGene regulatory network
 
dc.subjectMultiple regression
 
dc.subjectPower-law
 
dc.subjectStatistical tests
 
dc.titleA new multiple regression approach for the construction of genetic regulatory networks
 
dc.typeArticle
 
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Author Affiliations
  1. The University of Hong Kong
  2. Fudan University
  3. Chinese University of Hong Kong