|
Features
|
Threshold
|
Sensitivity (%)
|
Specificity (%)
|
Accuracy (%)
|
MCC
|
AUROC
|
Parameters
|
|---|
|
AAC*
|
− 0.1
|
94.10 ± 2.70
|
93.77 ± 3.28
|
93.91 ± 2.00
|
0.88 ± 0.03
|
0.98 ± 0.00
|
g = 0.001, c = 3, j = 3
|
|
N5 AAC
|
0
|
89.75 ± 3.61
|
90.88 ± 3.67
|
90.32 ± 2.27
|
0.81 ± 0.04
|
0.95 ± 0.01
|
g = 0.0005, c = 2, j = 1
|
|
C5 AAC
|
0
|
91.12 ± 3.53
|
91.64 ± 2.90
|
91.40 ± 2.13
|
0.83 ± 0.04
|
0.97 ± 0.01
|
g = 0.001, c = 9, j = 1
|
|
N5C5 AAC
|
− 0.2
|
94.61 ± 3.35
|
93.59 ± 3.23
|
94.07 ± 2.16
|
0.88 ± 0.04
|
0.98 ± 0.00
|
g = 0.0005, c = 1, j = 1
|
|
DPC
|
0
|
93.77 ± 2.76
|
95.32 ± 1.40
|
94.64 ± 1.24
|
0.89 ± 0.02
|
0.99 ± 0.00
|
g = 0.0005, c = 1, j = 2
|
|
N5 DPC
|
− 0.1
|
81.68 ± 4.25
|
87.36 ± 2.78
|
84.62 ± 2.65
|
0.69 ± 0.05
|
0.93 ± 0.01
|
g = 1e−05, c = 9, j = 1
|
|
C5 DPC
|
− 0.1
|
92.31 ± 3.36
|
94.71 ± 2.45
|
93.55 ± 1.40
|
0.87 ± 0.02
|
0.98 ± 0.01
|
g = 0.0005, c = 1, j = 2
|
|
N5C5 DPC
|
− 0.2
|
94.10 ± 3.06
|
93.75 ± 2.33
|
93.90 ± 1.49
|
0.88 ± 0.03
|
0.98 ± 0.01
|
g = 0.0001, c = 1, j = 1
|
|
N5 bin
|
− 0.1
|
88.46 ± 2.90
|
89.43 ± 3.32
|
88.98 ± 2.36
|
0.78 ± 0.04
|
0.95 ± 0.01
|
g = 0.5, c = 2, j = 1
|
|
C5 bin
|
− 0.2
|
93.70 ± 3.03
|
87.88 ± 4.25
|
90.63 ± 2.43
|
0.82 ± 0.04
|
0.97 ± 0.01
|
g = 0.5, c = 1, j = 2
|
|
N5C5 bin
|
0.2
|
90.95 ± 3.18
|
91.13 ± 3.44
|
91.03 ± 2.77
|
0.82 ± 0.05
|
0.97 ± 0.01
|
g = 0.05, c = 1, j = 4
|
|
N10 bin
|
− 0.2
|
89.38 ± 6.68
|
90.46 ± 4.67
|
90.01 ± 3.26
|
0.79 ± 0.06
|
0.95 ± 0.03
|
g = 0.1, c = 2, j = 2
|
|
C10 bin
|
− 0.2
|
85.02 ± 8.02
|
85.24 ± 5.15
|
85.19 ± 4.09
|
0.69 ± 0.09
|
0.93 ± 0.03
|
g = 0.05, c = 3, j = 1
|
|
N10C10 bin
|
− 0.4
|
88.73 ± 5.95
|
92.33 ± 5.69
|
91.04 ± 2.52
|
0.81 ± 0.05
|
0.97 ± 0.02
|
g = 0.1, c = 1, j = 1
|
|
AAC + motif
|
− 0.1
|
93.11 ± 1.86
|
95.33 ± 3.13
|
94.35 ± 1.67
|
0.89 ± 0.03
|
0.99 ± 0.00
|
g = 0.001, c = 6, j = 1
|
|
DPC + motif
|
0
|
93.28 ± 2.38
|
96.36 ± 1.70
|
95.00 ± 1.25
|
0.90 ± 0.02
|
0.99 ± 0.00
|
g = 0.0005, c = 1, j = 2
|
- This table shows average performance (mean ± standard deviation) of models on randomly generated independent datasets (bagging)
- 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)
