title: Benchmarking regulatory network reconstruction with GRENDEL.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  The synthetic benchmark utility and the specific benchmark networks that were used in our analyses are available at http://mblab.wustl.edu/software/grendel/.
authors.affiliation: Center for Genome Sciences and Department of Computer Science, Washington University, St Louis, MO, USA.
authors.forename: Brian C
authors.initials: BC
authors.lastname: Haynes
authors.forename: Michael R
authors.initials: MR
authors.lastname: Brent
mesh_terms: D000465:Algorithms; D019985:Benchmarking; D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks