title: Artificial gene networks for objective comparison of analysis algorithms.
abstract: BACKGROUND
  Large-scale gene expression profiling generates data sets that are rich in observed features but poor in numbers of observations. The analysis of such data sets is a challenge that has been object of vigorous research. The algorithms in use for this purpose have been poorly documented and rarely compared objectively, posing a problem of uncertainty about the outcomes of the analyses. One way to objectively test such analysis algorithms is to apply them on computational gene network models for which the mechanisms are completely know.
  
  
  RESULTS
  We present a system that generates random artificial gene networks according to well-defined topological and kinetic properties. These are used to run in silico experiments simulating real laboratory microarray experiments. Noise with controlled properties is added to the simulation results several times emulating measurement replicates, before expression ratios are calculated.
  
  
  BACKGROUND
  The data sets and kinetic models described here are available from http://www.vbi.vt.edu/~mendes/AGN/as biochemical dynamic models in SBML and Gepasi formats.
authors.affiliation: Virginia Bioinformatics Institute, USA. mendes@vt.edu
authors.forename: Pedro
authors.initials: P
authors.lastname: Mendes
authors.forename: Wei
authors.initials: W
authors.lastname: Sha
authors.forename: Keying
authors.initials: K
authors.lastname: Ye
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D012680:Sensitivity and Specificity; D015398:Signal Transduction / Q000502:physiology*; D012984:Software; D012986:Software Validation
chemical_list: D020543:Proteome