Figure 7From: PepDist: A New Framework for Protein-Peptide Binding Prediction based on Learning Peptide Distance Functions(a) ROC curves on test data from the HLA-A2 supertype. DistBoost is compared to the following algorithms: the SVMHC web server [11], the NetMHC web server [12], the RANKPEP resource [13] and the Euclidean distance metric in ℝ 45 MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciGacaGaaeqabaqabeGadaaakeaacqWIDesOdaahaaWcbeqaaiabisda0iabiwda1aaaaaa@3039@ . (b) DistBoost and the Euclidean affinity ROC curves on test data from the entire MHCclass1BN dataset. The rest of the methods are not presented since they were not trained in this multi-protein scenario. In both cases, DistBoost was trained on 70% of the data and tested on the remaining 30%. Results are best seen in color.Back to article page