To evaluate whether GDC captures statistically significant biological signal and outperforms other centrality measures irrespective of the chosen thresholds k, for each centrality measure, we compute the area under precision-recall curve (AUPR) as the threshold is varied between 0% and 100% in increments of 1%. The results obtained from AUPRs corresponding to different centrality measures are mostly consistent with the results obtained at selected thresholds where precision and recall cross (described above): for HIV and PI genes, AUPRs for GDC are the highest, followed by AUPRs for DC, SC, and BWC, respectively; for A and C genes, AUPRs for SC are the highest, followed by AUPRs for GDC, DC, and BWC, respectively. Hence, as was the case for individual thresholds (see above), GDC always outperforms DC, while SC outperforms DC only for A and C genes. Hence, GDC is always more discriminative than DC, while SC is not always more discriminative than DC, even though SC captures a deeper network topology compared to DC. The values of AUPRs for GDC are: 0.27 for A, 0.2 for C, 0.34 for HIV, and 0.2 for PI genes. These somewhat law values are not surprising, since in biological applications, the number of positive examples (here, the known BC genes) is much smaller than the number of negative examples (here, all proteins in the network that are currently not known to be BC genes). Furthermore, we do not know true negatives (genes that are true non-BC genes). Since we expect that many currently unreported BC genes will turn out to be BC genes in the future, AUPRs are likely to increase as this happens. Moreover, the observed AUPRs are statistically significant: we compute, at each value of recall, the probability of observing a given precision and we find that the probabilities of observing a given number of BC genes among k% of randomly chosen genes are in the range 0:03{10 {13 for k up to 90% (clearly, for k close to 100%, results become statistically insignificant, which is expected, since we choose as GDC-central all genes in the network).