|
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
|
- 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
- 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)
