Che et al. BMC Genomics 2010, 11(Suppl 2):S1 http://www.biomedcentral.com/1471-2164/11/S2/S1 where μGI and μnon_GI are the mean feature values from the GI dataset and non-GI dataset, respectively. sGI and snon_GI are their standard deviations from the GI dataset and non-GI dataset. We analyzed the feature analyses for the genera of Salmonella, Staphylococcus, Streptococcus and their all mixed-up datasets. The evaluation of the eight features on these four datasets shows that Integrase, Phage and Repeats are the most informative features. This can be easily to see in the datasets of Streptococcus, where the G2N values of Integrase, Repeats and Phages are 1.02, 0.94 and 0.82 respectively (See Table 2). The effective- ness of these features in both individual genera and their mixed-up datasets strongly suggests the existence of mobile elements and flanking repeats in all GI families (Table 2 and Additional file 1). The effectiveness of some features is genus-specific. For instance, the feature of Insertion point in the CDS is very informative in the genera of Streptococcus and Staphylococcus, but not in Salmonella. The feature of RNA is also genomic-specific. Interestingly, unlike the feature of Insertion point, RNA is informative for the genus of Salmonella, with small contribution for the genera of Streptococcus and Staphylococcus. The feature of Density seems to be uninformative to the genus of Salmonella, but is informative to the genus of Staphylococcus. Out of eight features analyzed, the feature ‘Size’ is the least informative. This can be explained by the random sampling process of non-GI datasets, whose genomic region size distribution was roughly the same as that of GIs. However, in many cases, an uninformative single feature does not imply that that feature will not contri- bute to the whole model when multiple features are applied. Previous studies have shown that the ‘Size’ fea- ture was indeed contributive in the RVM model [18]. Decision tree approaches outperform other machine learning algorithms Decision tree classification is one of most widely used machine learning methods. A decision tree classification Table 2 Feature quality analysis on dataset of Streptococcus Feature IVOM IP Size Density Repeats Integrase Phage RNA G2N 0.51 0.89 0.06 0.26 0.94 1.02 0.82 0.23 µGI 21.06 0.44 19879 1.15 0.74 0.65 0.41 0.17 sGI 12.97 0.50 16390 0.40 0.44 0.48 0.50 0.38 µnon_GI 9.96 0 17811 0.99 0.08 0.02 0 0.04 snon_GI 8.73 0 15890 0.20 0.27 0.14 0 0.19 Page 3 of 9 model is represented by a tree-like structure, where each internal node represents a test of feature, with each branch representing one of the possible test results, and each leaf node represents the classification. Due to the explosive growth of biological data in the past dec- ade, the decision tree approach has many successful bio- logical applications, including coding and noncoding DNA classification [19], protein secondary structure pre- diction [20], and operon structure classification [21]. In this study, we used two decision tree methods, Classifi- cation and Regression Tree (CART) and J48 (an extended Java implementation version of C4.5 algo- rithm), for the GI classification. Figure 1 demonstrates a J48 decision tree model built based on the dataset of the genus of Streptococcus. For performance comparision, we also used other five machine learning algorithms, including naive Bayesian, Bayesian networks, logistic regression, neural network, and SVMs. We used the WEKA machine learning pack- age [22] because all these algorithms have been imple- mented in the package. For the classification of each algorithm, default parameters provided in the package were used. Table 3 lists five performance evaluation metrics (see Methods for more details) of each algorithm on the mixed-up dataset of three genera. As we can see from Table 3, the J48 decision tree approach has the highest sensitivity value (0.858), though its specificity is a little bit lower than the other five algorithms. Since F-Mea- sure, accuracy, and AUC take both sensitivity and speci- ficity into consideration, they reflect the performance more accurately than sensitivity and specificity do sepa- rately. The J48 decision tree approach unanimously showed the best performance using the metrics of Figure 1 Decision tree model An example of a decision tree model for the genomic island classification of Streptcoccus. Each interior node is one of the features (i.e.,Density, Insertion point, Integrase, IVOM, and Repeat), while each leaf node is the classification (i.e., “Yes” indicates the test sequence segment is GI, while “No” indicates it is a nonGI).