Bioinformatics Advance Access first published online on July 24, 2006
This version published online on July 24, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl396
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1 Department of Electrical Engineering and Electronics, Osaka Sangyo University, Osaka 574-8530, Japan; Academy of Mathematics and Systems Science, CAS, Beijing 100080, China
* To whom correspondence should be addressed.
Motivation: Microarray gene expression data become increasingly common data source that can provide insights into biological processes at a system-wide level. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to a large number of genes, which makes the problem of inferring gene regulatory network an ill-posed one. On the other hand, gene expression data generated by different groups worldwide are increasingly accumulated on many species and can be accessed from public databases or individual web-sites, although each experiment has only a limited number of time-points. Results: This paper proposes a novel method to combine multiple time-course microarray datasets from different conditions for inferring gene regulatory networks. We have developed GNR (Gene Network Reconstruction tool) based on linear programming and a decomposition procedure. The proposed method theoretically ensures the derivation of the most consistent network structure with respect to all of the datasets, thereby not only significantly alleviating the problem of data scarcity but also remarkably improving the prediction reliability. We tested GNR using both simulated data and experimental data in yeast and Arabidopsis. The result demonstrates the effectiveness of GNR and predicts new gene regulatory relationship in yeast and Arabidopsis. Availability: The software is available from http://zhangorup.aporc.org/bioinfo/grninfer/, http://digbio.missouri.edu/grninfer/ or upon request from the authors.
Received March 5, 2006
Revised June 25, 2006
Accepted July 17, 2006
Article
Inferring gene regulatory networks from multiple microarray datasets
Yong Wang 1, Trupti Joshi 2, Xiang-Sun Zhang 3, Dong Xu 2, and Luonan Chen 4 *
2 Computer Science Department and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
3 Academy of Mathematics and Systems Science, CAS, Beijing 100080, China
4 Department of Electrical Engineering and Electronics, Osaka Sangyo University, Osaka 574-8530, Japan; Institute of systems biology, Shanghai University, Shanghai 200444, China
Luonan Chen, E-mail: chen{at}elec.osaka-sandai.ac.jp
Abstract
Associate Editor: Jonathan Wren
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  S L Rodriguez-Zas, Y Ko, H A Adams, and B R Southey Advancing the understanding of the embryo transcriptome co-regulation using meta-, functional, and gene network analysis tools Reproduction, February 1, 2008; 135(2): 213 - 224. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R.-S. Wang, Y. Wang, X.-S. Zhang, and L. Chen Inferring transcriptional regulatory networks from high-throughput data Bioinformatics, November 15, 2007; 23(22): 3056 - 3064. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z. Li, S. Zhang, Y. Wang, X.-S. Zhang, and L. Chen Alignment of molecular networks by integer quadratic programming Bioinformatics, July 1, 2007; 23(13): 1631 - 1639. [Abstract] [Full Text] [PDF]
|
|