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This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2011: Bioinformatics

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Inference of gene regulatory subnetworks from time course gene expression data

Xi-Jun Liang1, Zhonghang Xia2*, Li-Wei Zhang1 and Fang-Xiang Wu3

Author Affiliations

1 School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China

2 Dept. of Mathematics and Computer Science, Western Kentucky University, Bowling Green, KY 42101, USA

3 Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Dr., Saskatoon, SK S7N 5A9, Canada

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BMC Bioinformatics 2012, 13(Suppl 9):S3  doi:10.1186/1471-2105-13-S9-S3

Published: 11 June 2012



Identifying gene regulatory network (GRN) from time course gene expression data has attracted more and more attentions. Due to the computational complexity, most approaches for GRN reconstruction are limited on a small number of genes and low connectivity of the underlying networks. These approaches can only identify a single network for a given set of genes. However, for a large-scale gene network, there might exist multiple potential sub-networks, in which genes are only functionally related to others in the sub-networks.


We propose the network and community identification (NCI) method for identifying multiple subnetworks from gene expression data by incorporating community structure information into GRN inference. The proposed algorithm iteratively solves two optimization problems, and can promisingly be applied to large-scale GRNs. Furthermore, we present the efficient Block PCA method for searching communities in GRNs.


The NCI method is effective in identifying multiple subnetworks in a large-scale GRN. With the splitting algorithm, the Block PCA method shows a promosing attempt for exploring communities in a large-scale GRN.