Abstract
Many researchers have used microarray gene expression data to investigate gene regulatory networks in specific life stages. In these analyses, Bayesian network was widely applied to regulatory network building from expression profiles because of its solid mathematical foundation and its robust analysis ability in noisy data. However, the building of Bayesian network is time consuming and the searching space is really large. Considering the biological feature of transcription factors (TFs) and targets (TGs), the regulatory network is possible to be separated into core TFs networks and the interactions from TFs to TGs. We developed an R package named ModuleNet which used Bayesian network model to the inner TFs network building and genetic algorithm on TF-TG interactions prediction. With determined number of transcription factors, the searching space and time requirements of ModuleNet is linear increasing according to the number of targets. After application to yeast cell-cycle expression profile, the results demonstrated the prediction accuracy of ModuleNet. Furthermore, significantly enriched Gene Ontology (GO) terms with similar expression behaviors were detected automatically by ModuleNet from expression profile, and the relationships from TFs to GO terms were figured out. The source code is available by asking for the author.
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Zhou, D., He, D., Luo, Q. et al. ModuleNet: An R package on regulatory network building. Chin. Sci. Bull. 55, 3430–3435 (2010). https://doi.org/10.1007/s11434-010-3278-1
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DOI: https://doi.org/10.1007/s11434-010-3278-1