Performance of algorithms (area under ROC curve, AUC) in the rediscovery experiment using only NEM316 genome.

This analysis evaluated the relative performance of each algorithm to rediscover virulence genes by applying stratified n-fold cross-validations with of the entire set of S. agalactiae NEM316 genes serving as test-set in each fold. Each fold of training set comprised positive and negative examples.n: number of virulence genes in the category. Singleton virulence gene categories were excluded from this analysis, as it is not possible to perform cross-validations on training sets with n = 1. All but one (labeled*) AUCs reached the statistical significance level at α = 0.05 (two-tailed Mann-Whitley U-test). At least 3 out of 4 algorithms were still significant after adjustment for multiple testing (across the family of 4 algorithms) by the Bonferroni method. Abbreviations: ADTree: alternating decision tree; IBk: nearest neighbor classifier; SVM: support vector machine; RBF: SVM with radial basis function; Poly: SVM with polynomial kernel. Refer to the methods section for the parameters used to train the machine learning algorithms. The numbers in bold face indicate the best performing algorithm for a given category.