Article: A new multiple regression approach for the construction of genetic regulatory networks
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- Publisher Website: 10.1016/j.artmed.2009.11.001
- Scopus: eid_2-s2.0-77951623857
- PMID: 19963359
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| Title | A new multiple regression approach for the construction of genetic regulatory networks | ||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Authors | Zhang, SQ2 Ching, WK1 Tsing, NK1 Leung, HY1 Guo, D3 | ||||||||||||||||||||
| Keywords | Gene regulatory network Multiple regression Power-law Statistical tests | ||||||||||||||||||||
| Issue Date | 2010 | ||||||||||||||||||||
| Publisher | Elsevier 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?] DOI: http://dx.doi.org/10.1016/j.artmed.2009.11.001 | ||||||||||||||||||||
| 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. | ||||||||||||||||||||
| ISSN | 0933-3657 2011 Impact Factor: 1.345 2011 SCImago Journal Rankings: 0.091 | ||||||||||||||||||||
| DOI | http://dx.doi.org/10.1016/j.artmed.2009.11.001 | ||||||||||||||||||||
| ISI Accession Number ID | WOS:000275844400010
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 | References in Scopus |
| dc.contributor.author | Zhang, SQ | ||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| dc.contributor.author | Ching, WK | ||||||||||||||||||||
| dc.contributor.author | Tsing, NK | ||||||||||||||||||||
| dc.contributor.author | Leung, HY | ||||||||||||||||||||
| dc.contributor.author | Guo, D | ||||||||||||||||||||
| dc.date.accessioned | 2010-09-06T07:08:47Z | ||||||||||||||||||||
| dc.date.available | 2010-09-06T07:08:47Z | ||||||||||||||||||||
| dc.date.issued | 2010 | ||||||||||||||||||||
| dc.description.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. | ||||||||||||||||||||
| dc.description.nature | postprint | ||||||||||||||||||||
| dc.identifier.citation | Artificial 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.citeulike | 6358303 | ||||||||||||||||||||
| dc.identifier.doi | http://dx.doi.org/10.1016/j.artmed.2009.11.001 | ||||||||||||||||||||
| dc.identifier.epage | 160 | ||||||||||||||||||||
| dc.identifier.hkuros | 168941 | ||||||||||||||||||||
| dc.identifier.isi | WOS:000275844400010
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.issn | 0933-3657 2011 Impact Factor: 1.345 2011 SCImago Journal Rankings: 0.091 | ||||||||||||||||||||
| dc.identifier.issue | 2-3 | ||||||||||||||||||||
| dc.identifier.openurl | | ||||||||||||||||||||
| dc.identifier.pmid | 19963359 | ||||||||||||||||||||
| dc.identifier.scopus | eid_2-s2.0-77951623857 | ||||||||||||||||||||
| dc.identifier.spage | 153 | ||||||||||||||||||||
| dc.identifier.uri | http://hdl.handle.net/10722/75188 | ||||||||||||||||||||
| dc.identifier.volume | 48 | ||||||||||||||||||||
| dc.language | eng | ||||||||||||||||||||
| dc.publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/artmed | ||||||||||||||||||||
| dc.publisher.place | Netherlands | ||||||||||||||||||||
| dc.relation.ispartof | Artificial Intelligence in Medicine | ||||||||||||||||||||
| dc.relation.references | References in Scopus | ||||||||||||||||||||
| dc.rights | Artificial Intelligence in Medicine. Copyright © Elsevier BV. | ||||||||||||||||||||
| dc.rights | Creative Commons: Attribution 3.0 Hong Kong License | ||||||||||||||||||||
| dc.subject.mesh | Artificial Intelligence | ||||||||||||||||||||
| dc.subject.mesh | Gene Expression Regulation, Fungal | ||||||||||||||||||||
| dc.subject.mesh | Gene Regulatory Networks | ||||||||||||||||||||
| dc.subject.mesh | Models, Genetic | ||||||||||||||||||||
| dc.subject.mesh | Models, Statistical | ||||||||||||||||||||
| dc.subject | Gene regulatory network | ||||||||||||||||||||
| dc.subject | Multiple regression | ||||||||||||||||||||
| dc.subject | Power-law | ||||||||||||||||||||
| dc.subject | Statistical tests | ||||||||||||||||||||
| dc.title | A new multiple regression approach for the construction of genetic regulatory networks | ||||||||||||||||||||
| dc.type | Article |
Author Affiliations
- The University of Hong Kong
- Fudan University
- Chinese University of Hong Kong