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Note that when semantic-similarity based networks are added, all the integration methods improves their check details precision/recall results (the scale of the ordinate, that is the precision is equal in Fig. 7(a) and (b)). For instance WA, the best performing network integration methods, improves its average precision at 20% recall from 0.26 to 0.30 with a relative increment of about 15% in precision. As a final observation, note that all the considered network integration methods (except MIN integration) significantly outperform the results obtained with the best single network, confirming that also simple unweighted integration algorithms are sufficient to boost the performance of gene prioritization methods. The common usage of genes ranking scores in gene-disease prioritization experiments consists in the selection of the top ranked unannotated genes and in the their further characterization as possible ��candidate�� genes actually implied in the onset and progression of the considered disease. To this end we provide for each of the 708 MeSH diseases the AUC obtained by five-fold cross-validation, the p-value achieved through a non parametric randomized test (see below), and the 10 top ranked genes currently not annotated for the MeSH disease under study. Table summarizing these information is available at http://homes.di.unimi.it/re/suppmat/genesmeshnetwpred/supmatTBL1.html (accessed 30 November 2013). Moreover, we also provide a preliminary analysis of the top ranked most reliable unannotated genes for the MeSH diseases predicted with high robustness learn more and accuracy by the best network integration, i.e. WA integrating all the available nets using five steps SAV to prioritize genes. To evaluate the robustness of the method we performed a non-parametric statistical test by randomly shuffling 1000 times the labels for each MeSH disease and counting how many times m ? the AUC computed with randomly Sitaxentan shuffled labels is larger than the AUC computed with the true labels. The resulting p-value is just the ratio m1000. Interestingly enough, we achieve a p-value?