The operon is a specific functional organization of genes found in bacterial 
genomes. Most genes within operons share common features. The support vector 
machine (SVM) approach is here used to predict operons at the genomic level. 
Four features were chosen as SVM input vectors: the intergenic distances, the 
number of common pathways, the number of conserved gene pairs and the mutual 
information of phylogenetic profiles. The analysis reveals that these common 
properties are indeed characteristic of the genes within operons and are 
different from that of non-operonic genes. Jackknife testing indicates that 
these input feature vectors, employed with RBF kernel SVM, achieve high 
accuracy. To validate the method, Escherichia coli K12 and Bacillus subtilis 
were taken as benchmark genomes of known operon structure, and the prediction 
results in both show that the SVM can detect operon genes in target genomes 
efficiently and offers a satisfactory balance between sensitivity and 
specificity.