title: Operon prediction by Markov clustering.
abstract: The prediction of operons is a critical step for the reconstruction of biochemical and regulatory networks at the whole genome level. In this paper, a novel operon prediction model is proposed based on Markov Clustering (MCL). The model employs a graph-clustering method by MCL for prediction and does not need a classifier. In the cross-species validation, the accuracies of E. coli K12, Bacillus subtilis and P. furiosus are 92.1, 86.9 and 87.3%, respectively. Experimental results show that the proposed method has a powerful capability of operon prediction. The compiled program and test data sets are publicly available at http://ccst.jlu.edu.cn/JCSB/OPMC/.
authors.forename: Wei
authors.initials: W
authors.lastname: Du
authors.forename: Zhongbo
authors.initials: Z
authors.lastname: Cao
authors.forename: Yan
authors.initials: Y
authors.lastname: Wang
authors.forename: Enrico
authors.initials: E
authors.lastname: Blanzieri
authors.forename: Chen
authors.initials: C
authors.lastname: Zhang
authors.forename: Yanchun
authors.initials: Y
authors.lastname: Liang
mesh_terms: D000465:Algorithms; D001412:Bacillus subtilis / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000235:genetics; D053263:Gene Regulatory Networks; D016680:Genome, Bacterial; D008390:Markov Chains; D015233:Models, Statistical; D005810:Multigene Family; D009876:Operon; D020027:Pyrococcus furiosus / Q000235:genetics; D012372:ROC Curve