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Bioinformatics Advance Access originally published online on May 6, 2005
Bioinformatics 2005 21(14):3131-3137; doi:10.1093/bioinformatics/bti487
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

A Bayesian regression approach to the inference of regulatory networks from gene expression data

Simon Rogers * and Mark Girolami

Department of Computing Science, Bioinformatics Research Centre, University of Glasgow Glasgow, UK

*To whom correspondence should be addressed.

Motivation: There is currently much interest in reverse-engineering regulatory relationships between genes from microarray expression data. We propose a new algorithmic method for inferring such interactions between genes using data from gene knockout experiments. The algorithm we use is the Sparse Bayesian regression algorithm of Tipping and Faul. This method is highly suited to this problem as it does not require the data to be discretized, overcomes the need for an explicit topology search and, most importantly, requires no heuristic thresholding of the discovered connections.

Results: Using simulated expression data, we are able to show that this algorithm outperforms a recently published correlation-based approach. Crucially, it does this without the need to set any ad hoc threshold on possible connections.

Availability: Matlab code which allows all experimental results to be reproduced is available at http://www.dcs.gla.ac.uk/~srogers/reg_nets.html

Contact: srogers{at}dcs.gla.ac.uk

Supplementary information: Appendices and supplementary figures mentioned in the text can be found at http://www.dcs.gla.ac.uk/~srogers/reg_nets.html

Received on December 6, 2004; revised on March 8, 2005; accepted on May 3, 2005

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