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Article: A new multiple regression approach for the construction of genetic regulatory networks

TitleA new multiple regression approach for the construction of genetic regulatory networks
Authors
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
Citation
Artificial Intelligence In Medicine, 2010, v. 48 n. 2-3, p. 153-160 How to Cite?
Abstract
Objective: 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.
Persistent Identifierhttp://hdl.handle.net/10722/75188
ISSN
2013 Impact Factor: 1.356
2013 SCImago Journal Rankings: 0.865
ISI Accession Number ID
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.

References

 

Author Affiliations
  1. The University of Hong Kong
  2. Fudan University
  3. Chinese University of Hong Kong
DC FieldValueLanguage
dc.contributor.authorZhang, SQen_HK
dc.contributor.authorChing, WKen_HK
dc.contributor.authorTsing, NKen_HK
dc.contributor.authorLeung, HYen_HK
dc.contributor.authorGuo, Den_HK
dc.date.accessioned2010-09-06T07:08:47Z-
dc.date.available2010-09-06T07:08:47Z-
dc.date.issued2010en_HK
dc.identifier.citationArtificial Intelligence In Medicine, 2010, v. 48 n. 2-3, p. 153-160en_HK
dc.identifier.issn0933-3657en_HK
dc.identifier.urihttp://hdl.handle.net/10722/75188-
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.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/artmeden_HK
dc.relation.ispartofArtificial Intelligence in Medicineen_HK
dc.rightsArtificial Intelligence in Medicine. Copyright © Elsevier BV.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectGene regulatory networken_HK
dc.subjectMultiple regressionen_HK
dc.subjectPower-lawen_HK
dc.subjectStatistical testsen_HK
dc.subject.meshArtificial Intelligence-
dc.subject.meshGene Expression Regulation, Fungal-
dc.subject.meshGene Regulatory Networks-
dc.subject.meshModels, Genetic-
dc.subject.meshModels, Statistical-
dc.titleA new multiple regression approach for the construction of genetic regulatory networksen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0933-3657&volume=48&issue=2-3&spage=153&epage=160&date=2010&atitle=A+new+multiple+regression+approach+for+the+construction+of+genetic+regulatory+networks-
dc.identifier.emailChing, WK:wching@hku.hken_HK
dc.identifier.emailTsing, NK:nktsing@hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.identifier.authorityTsing, NK=rp00794en_HK
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.artmed.2009.11.001en_HK
dc.identifier.pmid19963359en_HK
dc.identifier.scopuseid_2-s2.0-77951623857en_HK
dc.identifier.hkuros168941en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77951623857&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume48en_HK
dc.identifier.issue2-3en_HK
dc.identifier.spage153en_HK
dc.identifier.epage160en_HK
dc.identifier.eissn1873-2860-
dc.identifier.isiWOS:000275844400010-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridZhang, SQ=35235892900en_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridTsing, NK=6602663351en_HK
dc.identifier.scopusauthoridLeung, HY=24780941800en_HK
dc.identifier.scopusauthoridGuo, D=24780473500en_HK
dc.identifier.citeulike6358303-

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