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Table 3 The average performance of A-cell epitope prediction models on training and independent dataset

From: Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants

Features Threshold Sensitivity (%) Specificity (%) Accuracy (%) MCC Parameters
Internal validation: performance on training dataset, evaluated using fivefold cross-validation
 DPC − 0.2 87.49 ± 1.41 98.70 ± 0.16 97.68 ± 0.22 0.86 ± 0.01 g: 0.0005, c: 1, j: 4
 DPC + Motif − 0.2 87.81 ± 1.01 99.30 ± 0.10 98.25 ± 0.17 0.89 ± 0.01 g: 0.0005, c: 1, j: 4
External validation: performance on independent dataset
 DPC − 0.2 87.54 ± 4.31 98.87 ± 0.28 97.84 ± 0.41 0.87 ± 0.02 g: 0.0005, c: 1, j: 4
 DPC + Motif − 0.2 77.86 ± 5.84 99.28 ± 0.30 97.33 ± 0.58 0.83 ± 0.04 g: 0.0005, c: 1, j: 4
  1. These training and independent datasets were created from alternate datasets using bagging. In alternate dataset, negative or non-epitopes were derived from human proteins. The performance values have been reported as mean ± standard deviation for each model
  2. MCC Matthews correlation coefficient, DPC dipeptide composition, DPC + motif dipeptide composition with MERCI motif score, SVM parameters g gamma parameter of the radial basis function, c trade-off between training error and margin, j regularization parameter (cost-factor, by which training errors on positive examples outweigh errors on negative examples)