<i><b>Motivation:</b></i> Although gene expression data has been continuously accumulated and meta-analysis approaches have been developed to integrate independent expression profiles into larger datasets, the amount of information is still insufficient to infer large scale genetic networks. In addition, global optimization such as Bayesian network inference, one of the most representative techniques for genetic network inference, requires tremendous computational load far beyond the capacity of moderate workstations. <br><br><i><b>Results:</b></i> MONET is a Cytoscape plugin to infer genome-scale networks from gene expression profiles. It alleviates the shortage of information by incorporating pre-existing annotations. The current version of MONET utilizes thousands of parallel computational cores in the supercomputing center in KISTI, Korea, to cope with the computational requirement for large scale genetic network inference.<br><br><i><b>Reference:</b></i> Lee et al., Modularized learning of genetic interaction networks from biological annotations and mRNA expression data. <i>Bioinformatics</i> (2005) 21, 2739-2747
