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.