Che et al. BMC Genomics 2010, 11(Suppl 2):S1 http://www.biomedcentral.com/1471-2164/11/S2/S1 Page 5 of 9 Figure 2 ROC curves of decision tree based ensembles For each dataset of Streptococcus, Staphylococcus, Salmonella, and all-three. ROC curves of adaBoost, bagging, multiBoost, random forest, and single J48 are calculated. We also compared the bagging with the classification results of the RVM method in a previous study [15], and found that the AUC values of the bagging algorithm are 9% (0.92 versus 0.83), 7% (0.89 versus 0.82), and 10% (0.94 versus 0.84) higher in the models of Salmonella, Staphylococcus, and mixed-up three, respectively, and is same (0.94 versus 0.94) in the model of Streptococcus. Application of trained models for the whole-genome scale GI detection The trained models based on J48 decision tree based ensemble algorithms can be used to detect the whole genome scale GIs of prokaryotic genomes. We have devel- oped an automated software package that contains the functionalities of downloading genome data, extracting GI associated feature values, and predicting GIs based on our trained models from this study. The software package was written in C# and tested on Windows, and it is available at http://www.esu.edu/cpsc/che_lab/software/GIDetector/. The detailed description, as well as the usage, of our soft- ware package will be addressed elsewhere (in preparation). Currently, the software contains models for the genera of Salmonella, Staphylococcus, Streptococcus. Thus, it is advisable to use the models of the same genus to detect