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Table 1 The performance of SVM-based models developed using various features; models were evaluated on training dataset using fivefold cross-validation (internal cross-validation)

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

Features

Threshold

Sensitivity (%)

Specificity (%)

Accuracy (%)

MCC

AUROC

Parameters

AAC

− 0.1

94.49 ± 0.80

92.38 ± 1.33

93.30 ± 0.84

0.87 ± 0.01

0.98 ± 0.00

g = 0.001, c = 3, j = 3

N5 AAC

0

88.54 ± 0.75

90.25 ± 1.87

89.44 ± 1.26

0.79 ± 0.02

0.94 ± 0.00

g = 0.0005, c = 2, j = 1

C5 AAC

0

91.13 ± 1.42

92.94 ± 1.20

92.08 ± 1.05

0.84 ± 0.02

0.97 ± 0.00

g = 0.001, c = 9, j = 1

N5C5 AAC

− 0.2

93.73 ± 0.60

92.83 ± 0.76

93.26 ± 0.40

0.87 ± 0.00

0.98 ± 0.00

g = 0.0005, c = 1, j = 1

DPC

0

93.79 ± 1.12

95.68 ± 0.78

94.84 ± 0.72

0.90 ± 0.01

0.99 ± 0.01

g = 0.0005, c = 1, j = 2

N5 DPC

− 0.1

83.42 ± 1.77

87.73 ± 2.00

85.69 ± 1.10

0.71 ± 0.02

0.93 ± 0.00

g = 1e−05, c = 9, j = 1

C5 DPC

− 0.1

90.21 ± 0.91

93.62 ± 0.96

92.00 ± 0.50

0.84 ± 0.01

0.97 ± 0.00

g = 0.0005, c = 1, j = 2

N5C5 DPC

− 0.2

93.60 ± 0.72

92.67 ± 1.16

93.11 ± 0.70

0.86 ± 0.01

0.98 ± 0.00

g = 0.0001, c = 1, j = 1

N5 bin

− 0.1

86.91 ± 0.82

88.81 ± 1.47

87.91 ± 0.73

0.76 ± 0.01

0.94 ± 0.00

g = 0.5, c = 2, j = 1

C5 bin

− 0.2

91.18 ± 0.92

86.61 ± 1.68

88.80 ± 1.14

0.78 ± 0.02

0.96 ± 0.00

g = 0.5, c = 1, j = 2

N5C5 bin

0.2

89.20 ± 1.11

91.14 ± 1.61

90.22 ± 1.05

0.80 ± 0.02

0.96 ± 0.00

g = 0.05, c = 1, j = 4

N10 bin

− 0.2

86.39 ± 2.73

89.68 ± 1.79

88.42 ± 1.05

0.76 ± 0.02

0.94 ± 0.01

g = 0.1, c = 2, j = 2

C10 bin

− 0.2

79.87 ± 2.30

86.49 ± 2.43

83.96 ± 1.91

0.66 ± 0.03

0.90 ± 0.01

g = 0.05, c = 3, j = 1

N10C10 bin

− 0.4

86.89 ± 2.70

91.62 ± 2.92

89.83 ± 1.31

0.79 ± 0.02

0.96 ± 0.00

g = 0.1, c = 1, j = 1

AAC + motif

− 0.1

95.51 ± 0.86

95.35 ± 0.85

95.42 ± 0.77

0.91 ± 0.01

0.99 ± 0.00

g = 0.001, c = 6, j = 1

DPC + motif

0

94.15 ± 0.92

96.94 ± 0.49

95.71 ± 0.38

0.91 ± 0.00

0.99 ± 0.00

g = 0.0005, c = 1, j = 2

  1. This table shows average performance (mean ± standard deviation) of models on randomly generated training datasets (bagging)
  2. MCC Matthews correlation coefficient, AAC amino acid composition, DPC dipeptide composition, N5 first 5 residues from N terminus, C5 first 5 residues from C terminus, N5C5 first 5 residues from N and C terminus respectively, bin binary profile, AAC + motif amino acid composition with MERCI motif score, 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)