Che et al. BMC Genomics 2010, 11(Suppl 2):S1 http://www.biomedcentral.com/1471-2164/11/S2/S1 process will be repeated until there are no further dis- tinguishing features can be determined. Bagging Bootstrap Aggregating [29], better known as bagging, is a method whose classification takes the majority votes of multiple classifiers thus forming a hypothetical “com- mittee”. Each classifier in the committee is un-weighted and each classifier is a decision tree model in this study. Each decision tree classifier model is trained on a subset of the initial training set. The training set of each classi- fier model can be sampled by bootstrap sampling, i.e., randomly selecting a subset of given dataset with repla- cement, allowing for sample values to be independent of one another. AdaBoost Adaptive boosting (adaBoosting) [30] uses the weighted data sampling and voting scheme. The algorithm starts by building the first base classifier, which is trained on the dataset with equal weights. For the construction of subsequent classifiers, the instances misclassified by the previous classifier are assigned higher weights, while the weights of the instances that are correctly classified remain the same. The weights of all instances in the whole dataset are then normalized so that all weights add up to 1, and then used for sampling for the next classifier. The final classification for an instance is based on the classifications by all classifiers, with each classi- fier weighted also. The class with the highest weighted votes is the final classification. MultiBoost In the MultiBoost algorithm [31], the classification is the weighted aggregate of multiple committees, rather than un-weighted aggregating as in bagging. Furthermore, each committee of MultiBoost itself is a decision tree ensemble known as AdaBoost [32]. AdaBoost is a weighted aggregating of committees, where each com- mittee member is a basic decision tree model. Each decision tree classifier is constructed on a subset of given dataset. The sampling of the subset of dataset is a random sampling with replacement for the first classifier construction. In later classifier construction, however, the instances misclassified in previous classifiers are assigned high weights. The detailed algorithm and the determination of weights can be referred to in [32]. Random forest Random forest [33] is similar to bagging in that both use the bootstrap sampling technique to select the sub- set of the base training dataset to train decision tree models, and both use the un-weighted aggregating of committees for the final classification. However, the selection of the best feature in the process of the deci- sion tree structure is different. In the random forest approach, m features out of M features are randomly Page 8 of 9 selected, and the optimal value of m is usually the square root of M. The best feature out of m features is determined based on the calculated information gain. In addition, each tree is fully grown and not pruned in ran- dom forest. Performance evaluation A ten-fold cross-validation scheme is used to evaluate the classification accuracy of all classifiers. In particular, the known GI and non-GI datasets are evenly separated into ten parts, and the first part is evaluated based on the model trained from the remaining nine parts. This process continues until all ten parts have been evalu- ated. The overall performance metric is the average of all ten separate evaluations. True positives (TP) are the number of GIs predicted to be GIs. False negatives (FN) are the number of GIs predicted to be non-GIs. True Negatives (TN) are the number of non-GIs predicted to be non-GIs. False positives (FP) are the number of non- GIs predicted to be GIs. We focus on the following vali- dation measures: Sensitivity = TP/(TP + FN) Specificity = TN/(TN + FP) Accuracy = (TP + TN)/(TP + TN + FN + FP) F – measure = 2 * Sen * Spec/(Sen + Spec) (4) (5) (6) (7) We have also used the area under the ROC (Receiver Operating Characteristic) curve (AUC) to measure the classification performance. The AUC value is the per- centage of correctly classified one pair of samples, with each from one class. The AUC takes the value between 0 and 1, and a random classifier has the AUC value of 0.5. Theoretically, a well-performing classifier should have a high AUC value. Additional file 1: Feature quality analysisFeature quality analysis. This file contains feature analysis on three datasets, Staphylococcus, Salmonella, and all-three mixed. Additional file 2: ROC curves for the “leave-one-feature-out” models using adaBoost. Acknowledgements This research was partially supported by the Pennsylvania KISZK grant (C000032549), Graduate College and College of Arts & Sciences at East Stroudsburg University. The authors are also grateful to anonymous referees for their helpful insights during the review process. Publication of this supplement was made possible with support from the International Society of Intelligent Biological Medicine (ISIBM). This article has been published as part of BMC Genomics Volume 11 Supplement 2, 2010: Proceedings of the 2009 International Conference on Bioinformatics & Computational Biology (BioComp 2009). The full contents of the supplement are available online at http://www.biomedcentral.com/1471- 2164/11?issue=S2.