Sensitivity and specificity for random forest tests applied to peptide-MHC binding scores for vaccine classification of Benchmark dataset.

Abbreviations: (R)  =  target variable e.g. 1 or 0 in training data randomly changed for each protein, HE  =  hold-out dataset error (%) i.e. error when predicting 30% of training data, OE  =  overall error (%) i.e. percentage of incorrect predictions, SN  =  sensitivity (%)  =  true positives/(true positives+false negatives), SP  =  specificity (%)  =  true negatives/(true negatives+false positives).
aCross-validation involved a random sample of 70% from training dataset to build predictive model and remaining 30% used for testing. This was repeated 10 times and predictions averaged (predictions for the same input data fluctuate unless a random seed is set initially).
bBenchmark are proteins from published studies with known or expected T-cell responses (source species: T. gondii) –100% from training data used to build predictive model.
Note: Number of input variables used to build predictive model  = 304 (i.e. number of allele-peptide length combinations derived from 76 common alleles).
Sensitivity and specificity for random forest tests applied to peptide-MHC binding scores for vaccine classification of Benchmark dataset.