A means of predicting additional protein interactions is to probabilistically integrate many different lines of evidence into a single classifier42-47. Along these lines, we applied a machine-learning-based classifier for predicting interactions that combined many relevant features including gene expression, domain-domain interactions, conserved protein-protein interactions, genetic interactions, and shared gene annotations (Supplementary Methods). We used this approach to generate 24,798 high confidence predictions. We randomly selected 2,047 of these for testing using forward-orientation Y2H and, as above, retested the negative pairs using reverse-orientation Y2H (for which clones were available). In total, this procedure added 279 new high-confidence interactions to the map for a 13.6% success rate. Combined over both conservation-based and multiple-evidence-based predictions, 450 new protein-protein interactions were added to the Drosophila map using 47 96-well plates (Fig.  3a,b) . To establish the background rate of interaction, we also tested 2,354 randomly chosen pairs, 72 of which were positive yielding a 3% background rate (Fig. 4b) . These results show that both types of prediction are highly enriched for true interactions. Note that even if all predicted interactions were true, the expected confirmation rate would be limited by the false negative rate of the Y2H assay, equal to 1-FNR =33% in our model.