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Table 3 Predictive performance in terms of the area under the ROC curve (AUC) of the different methods evaluated on six data sets.

From: Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

  AntiJen IEDB
  DRB1*0101 DRB1*0401 DRB1*1501 DRB1*0101 DRB1*0401 DRB1*1501
ISC-PLC 0.709 0.757 0.609    
SMM-align 0.718 0.806 0.691 0.702 0.741 0.715
TEPITOPE 0.667 0.744 0.665 0.647 0.754 0.726
Chang 0.770 0.757 0.677    
SMM-regr 0.807 0.819 0.741 0.744 0.750 0.718
SMM-regr-alter 0.616 0.785 0.669 0.645 0.721 0.712
SMM-PFR 0.742 0.814 0.726 0.716 0.756 0.733
  1. The methods are; ISC-PLS [15], SMM-align, TEPITOPE, Chang [11], SMM-regr (SMM with peptide length regression correction from training data set), SMM-regr-alter (SMM with peptide length regression correction from alternative AntiJen/IEDB dataset), and SMM-PFR (The SMM-PRF method refers to the extended SMM align method including penalties for long peptides and short amino terminal peptide flanking residues). The data sets consist of peptides binding data from two sources (IEDB and AntiJen) covering three HLA-DR alleles (1*0101, 1*0401, and 1*1501). Performance value for the ISC-PLS and Chang methods, are taken from Chang et al. [11]. These values are only available for the AntiJen data set.