Table 1 The performance of SVM model (learning parameter: g: 0.1 c: 2 j: 3) using amino acid sequence (The SVM parameter g (in RBF kernel), c: parameter for trade-off between training error & margin, j: cost-factor)
From: Identification of ATP binding residues of a protein from its primary sequence
Thres | Sen | Spec | Accuracy | MCC |
|---|---|---|---|---|
-1 | 100 | 1.73 | 50.87 | 0.09 |
-0.9 | 99.87 | 2.88 | 51.37 | 0.11 |
-0.8 | 99.67 | 4.39 | 52.03 | 0.13 |
-0.7 | 99.25 | 6.51 | 52.88 | 0.15 |
-0.6 | 98.36 | 10.31 | 54.34 | 0.18 |
-0.5 | 96.89 | 15.78 | 56.33 | 0.22 |
-0.4 | 93.75 | 23.54 | 58.64 | 0.24 |
-0.3 | 88.94 | 32.9 | 60.92 | 0.26 |
-0.2 | 83.4 | 43.31 | 63.36 | 0.29 |
0 | 65.53 | 66.97 | 66.25 | 0.33 |
0.1 | 54.6 | 76.99 | 65.79 | 0.32 |
0.2 | 43.11 | 84.98 | 64.04 | 0.31 |
0.3 | 33.94 | 91.16 | 62.55 | 0.31 |
0.4 | 25.7 | 94.7 | 60.2 | 0.28 |
0.5 | 18.07 | 97.15 | 57.61 | 0.25 |
0.6 | 12.64 | 98.53 | 55.58 | 0.22 |
0.7 | 8.71 | 99.18 | 53.94 | 0.19 |
0.8 | 6.22 | 99.54 | 52.88 | 0.16 |
0.9 | 3.93 | 99.77 | 51.85 | 0.13 |
1 | 1.87 | 99.84 | 50.85 | 0.08 |