SynTReN: Synthetic Transcriptional Regulatory Networks.
A generator of synthetic gene expression data for design and analysis of structure learning algorithms.
A network generator that creates synthetic transcriptional regulatory networks and produces simulated gene expression data that approximates experimental data. Network topologies are generated by selecting subnetworks from previously described regulatory networks. Interaction kinetics are modeled by equations based on Michaelis-Menten and Hill kinetics. The statistical properties of these topologies more closely approximate those of genuine biological networks than do those of different types of random graph models. Several user-definable parameters adjust the complexity of the resulting data set with respect to the structure learning algorithms.
SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms.Tim Van den Bulcke, Koenraad Van Leemput, Bart Naudts, Piet van Remortel, Hongwu Ma, Alain Verschoren, Bart De Moor and Kathleen Marchal. BMC Bioinformatics 2006, 7:43 doi:10.1186/1471-2105-7-43