Artificial gene networks for objective comparison of analysis algorithms.

TitleArtificial gene networks for objective comparison of analysis algorithms.
Publication TypeJournal Article
Year of Publication2003
AuthorsMendes P, Sha W, Ye K
Volume19 Suppl 2
Date Published2003 Oct
KeywordsAlgorithms, Computer Simulation, Gene Expression, Gene Expression Profiling, Models, Biological, Proteome, Sensitivity and Specificity, Signal Transduction, Software, Software Validation

<p><b>MOTIVATION: </b>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.</p><p><b>RESULTS: </b>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.</p><p><b>AVAILABILITY: </b>The data sets and kinetic models described here are available from biochemical dynamic models in SBML and Gepasi formats.</p>

Alternate JournalBioinformatics
PubMed ID14534181
Grant List1 R01 GM068947-01 / GM / NIGMS NIH HHS / United States