Table 2 Results
From: Prediction of DNA-binding proteins from relational features
Accuracy | AUC | |||||
|---|---|---|---|---|---|---|
PD138 vs. NB110 | PF | SF | PSF | PF | SF | PSF |
Simple logistic regression | 83.4 (1) | 82.2 (2) | 80.7 (3) | 0.91 (2) | 0.90 (3) | 0.94 (1) |
L2-regularized log. regression | 81.4 (3) | 83.5 (2) | 85.5 (1) | 0.92 (1) | 0.91 (2) | 0.91 (2) |
SVM with radial basis kernel | 81.8 (2) | 79.9 (3) | 85.1 (1) | 0.92 (2) | 0.90 (3) | 0.93 (1) |
Linear SVM | 81.4 (3) | 83.6 (2) | 83.9 (1) | 0.92 (2) | 0.89 (3) | 0.93 (1) |
Ada-boost w. decision stamps | 80.6 (2) | 78.6 (3) | 81.4 (1) | 0.90 (1) | 0.90 (1) | 0.90 (1) |
Random forest | 81.8 (3) | 83.5 (1) | 82.3 (2) | 0.90 (3) | 0.91 (2) | 0.93 (1) |
Average ranking | 2.33 | 2.17 | 1.5 | 1.83 | 2.33 | 1.17 |
UD54 vs. NB110 | PF | SF | PSF | PF | SF | PSF |
Simple logistic regression | 81.0 (3) | 86.0 (1) | 82.8 (2) | 0.91 (1) | 0.89 (2) | 0.89 (2) |
L2-regularized log. regression | 82.2 (3) | 82.4 (2) | 84.1 (1) | 0.89 (3) | 0.91 (1) | 0.90 (2) |
SVM with radial basis kernel | 81.0 (2) | 84.0 (1) | 80.4 (3) | 0.92 (1) | 0.88 (3) | 0.91 (2) |
Linear SVM | 81.7 (2) | 82.4 (1) | 82.4 (1) | 0.90 (2) | 0.91 (1) | 0.87 (3) |
Ada-boost w. decision stamps | 76.2 (3) | 78.0 (2) | 79.3 (1) | 0.88 (3) | 0.89 (2) | 0.90 (1) |
Random forest | 78.6 (3) | 79.3 (1) | 79.2 (2) | 0.88 (3) | 0.89 (2) | 0.90 (1) |
Average ranking | 2.67 | 1.34 | 1.67 | 2.17 | 1.67 | 2 |
BD54 vs. NB110 | PF | SF | PSF | PF | SF | PSF |
Simple logistic regression | 80 (3) | 80.5 (2) | 81.8 (1) | 0.91 (1) | 0.85 (2) | 0.91 (1) |
L2-regularized log. regres | 83.1 (1) | 81.9 (2) | 81.7 (3) | 0.92 (1) | 0.88 (3) | 0.91 (2) |
SVM with radial basis kernel | 82.5 (2) | 82.5 (2) | 83.6 (1) | 0.91 (1) | 0.90 (2) | 0.90 (2) |
Linear SVM | 81.4 (3) | 82.3 (2) | 82.9 (1) | 0.93 (2) | 0.90 (3) | 0.94 (1) |
Ada-boost w. decision stamps | 84.2 (1) | 73.8 (3) | 79.8 (2) | 0.91 (1) | 0.88 (2) | 0.88 (2) |
Random forest | 82.4 (1) | 75.0 (3) | 79.4 (2) | 0.89 (2) | 0.89 (2) | 0.91 (1) |
Average ranking | 1.83 | 2.33 | 1.67 | 1.33 | 2.33 | 1.5 |
APO104 vs. NB110 | PF | SF | PSF | PF | SF | PSF |
Simple logistic regression | 80.7 (3) | 85.0 (1) | 80.8 (2) | 0.89 (3) | 0.92 (1) | 0.91 (2) |
L2-regularized log. regression | 82.6 (3) | 84.5 (1) | 83.1 (2) | 0.90 (2) | 0.91 (1) | 0.91 (1) |
SVM with radial basis kernel | 79.4 (3) | 83.2 (2) | 84.1 (1) | 0.88 (3) | 0.90 (2) | 0.91 (1) |
Linear SVM | 79.4 (3) | 84.5 (1) | 84.1 (2) | 0.89 (2) | 0.89 (2) | 0.92 (1) |
Ada-boost w. decision stamps | 77.6 (3) | 78.1 (2) | 79.1 (1) | 0.87 (2) | 0.87 (2) | 0.89 (1) |
Random forest | 81.7 (1) | 78.5 (3) | 79.4 (2) | 0.88 (2) | 0.87 (3) | 0.89 (1) |
Average ranking | 2.67 | 1.67 | 1.67 | 2.33 | 1.83 | 1.17 |
ZF vs. NB110 | PF | SF | PSF | PF | SF | PSF |
Simple logistic regression | 95.1 (3) | 98.7 (1) | 97.2 (2) | 0.99 (2) | 1.0 (1) | 1.0 (1) |
L2-regularized log. regres | 95.9 (3) | 99.3 (2) | 100 (1) | 0.99 (2) | 1.0 (1) | 1.0 (1) |
SVM with radial basis kernel | 95.8 (3) | 99.3 (1) | 98.6 (2) | 0.99 (2) | 1.0 (1) | 1.0 (1) |
Linear SVM | 81.4 (3) | 99.3 (1) | 97.8 (2) | 1.0 (1) | 1.0 (1) | 1.0 (1) |
Ada-boost w. decision stamps | 95.9 (3) | 99.3 (2) | 100 (1) | 0.98 (2) | 1.0 (1) | 1.0 (1) |
Random forest | 96.5 (3) | 97.9 (1) | 97.2 (2) | 0.99 (2) | 1.0 (1) | 1.0 (1) |
Average ranking | 3 | 1.33 | 1.67 | 1.83 | 1 | 1 |