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Table 4 The enrichment factors, BEDROC, and AUC ROC scores evaluated for the anti-HIV dataset.

The quality of the classifier can be evaluated by calculating the area-under-the-curve (AUC) (the "ROC score"), in which a random classifier achieves the ROC score of 0.5 and a perfect classifier achieves the ROC score of 1.0.

Additional file 1 provides detailed information about the performance curves and the necessary specifics for benchmarking novelty scores with ROC curves as well as significance testing of AUC ROC with a permutation test (see also Additional file 2).

The results for the four AUC ROC outcome measures are shown in Fig. 1.

Values are AUC ROC determinations.

To assess the expression array performance for each sample, we used the ROC AUC summarization score, which is a measure of overall sensitivity and specificity of the expression profile.

Discrimination was excellent for Child-Pugh (ROC AUC: 0.859) and APACHE III (ROC AUC: 0.816) scores, and acceptable for APACHE II score (ROC AUC: 0.759).

Also it is interesting to see that using FI-CENTER and selecting itemsets which have a (P_1) or (P'_1) position item, always increase accuracy and ROC-AUC score.

We generated reasonably noisy datasets, estimated the parameters using different methods, ordered the model coefficients according to their estimated values and assessed the ROC AUC score of such ordering.

The average ROC AUC score was 0.88 (Supplementary Fig. S5A).

In the no closure group, kininogen had the most significant ROC AUC score and its abundance ratio was ca 1.6× higher than in the closure group.