Research article

Rank-based edge reconstruction for scale-free genetic regulatory networks

Guanrao Chen¹ , Peter Larsen² , Eyad Almasri³ and Yang Dai³

¹Department of Computer Science (MC152), University of Illinois at Chicago, 851 South Morgan Street, Chicago, IL 60607, USA

²Core Genomics Laboratory, Research Resource Center (MC937), University of Illinois at Chicago, 835 South Wolcott Avenue, Chicago, IL 60612, USA

³Department of Bioengineering (MC063), University of Illinois at Chicago, 851 South Morgan Street, Chicago, IL 60607, USA

author email corresponding author email

BMC Bioinformatics 2008, 9:75doi:10.1186/1471-2105-9-75

Published:	31 January 2008

Abstract

Background

The reconstruction of genetic regulatory networks from microarray gene expression data has been a challenging task in bioinformatics. Various approaches to this problem have been proposed, however, they do not take into account the topological characteristics of the targeted networks while reconstructing them.

Results

In this study, an algorithm that explores the scale-free topology of networks was proposed based on the modification of a rank-based algorithm for network reconstruction. The new algorithm was evaluated with the use of both simulated and microarray gene expression data. The results demonstrated that the proposed algorithm outperforms the original rank-based algorithm. In addition, in comparison with the Bayesian Network approach, the results show that the proposed algorithm gives much better recovery of the underlying network when sample size is much smaller relative to the number of genes.

Conclusion

The proposed algorithm is expected to be useful in the reconstruction of biological networks whose degree distributions follow the scale-free topology.