Table 2 Prediction performance of PredRBR and other machine learning methods on the RBP170 dataset
From: A boosting approach for prediction of protein-RNA binding residues
Method | ACC | SN | SP | Precision | F-measure | MCC | AUC |
|---|---|---|---|---|---|---|---|
PredRBR | 0.84 ± 0.01 | 0.85 ± 0.02 | 0.84 ± 0.01 | 0.47 ± 0.02 | 0.60 ± 0.02 | 0.55 ± 0.02 | 0.92 ± 0.01 |
RF | 0.82 ± 0.01 | 0.81 ± 0.01 | 0.83 ± 0.01 | 0.44 ± 0.02 | 0.57 ± 0.02 | 0.51 ± 0.02 | 0.90 ± 0.01 |
SVM | 0.81 ± 0.01 | 0.81 ± 0.02 | 0.81 ± 0.02 | 0.42 ± 0.01 | 0.55 ± 0.01 | 0.49 ± 0.01 | 0.89 ± 0.01 |
Adaboost | 0.79 ± 0.01 | 0.80 ± 0.01 | 0.79 ± 0.01 | 0.40 ± 0.01 | 0.53 ± 0.01 | 0.46 ± 0.01 | 0.87 ± 0.01 |