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Bioinformatics Advance Access originally published online on February 2, 2009
Bioinformatics 2009 25(6):801-807; doi:10.1093/bioinformatics/btp068
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Benchmarking regulatory network reconstruction with GRENDEL

Brian C. Haynes 1,2 and Michael R. Brent 1,2,*

1Center for Genome Sciences and 2Department of Computer Science, Washington University, St Louis, MO, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Over the past decade, the prospect of inferring networks of gene regulation from high-throughput experimental data has received a great deal of attention. In contrast to the massive effort that has gone into automated deconvolution of biological networks, relatively little effort has been invested in benchmarking the proposed algorithms. The rate at which new network inference methods are being proposed far outpaces our ability to objectively evaluate and compare them. This is largely due to a lack of fully understood biological networks to use as gold standards.

Results: We have developed the most realistic system to date that generates synthetic regulatory networks for benchmarking reconstruction algorithms. The improved biological realism of our benchmark leads to conclusions about the relative accuracies of reconstruction algorithms that are significantly different from those obtained with A-BIOCHEM, an established in silico benchmark.

Availability: The synthetic benchmark utility and the specific benchmark networks that were used in our analyses are available at http://mblab.wustl.edu/software/grendel/

Contact: brent{at}cse.wustl.edu

Associate Editor: Limsoon Wong

Received on October 24, 2008; revised on December 31, 2008; accepted on January 28, 2009

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