Table 3 The test data performance for the best classifiers developed using various types of peptide features for the main dataset (only the best models are shown).
From: In Silico tool for predicting, designing and scanning IL-2 inducing peptides
Features | Models | Sens | Spec | ACC | AUC | MCC |
|---|---|---|---|---|---|---|
AAB | ET | 0.63 | 0.66 | 64.10 | 0.70 | 0.26 |
AAC | ET | 0.72 | 0.67 | 70.09 | 0.78 | 0.39 |
CeTD | XGB | 0.70 | 0.63 | 67.00 | 0.73 | 0.33 |
DDR | ET | 0.64 | 0.67 | 66.00 | 0.72 | 0.31 |
DPC_LEN | ET | 0.75 | 0.73 | 73.76 | 0.82 | 0.48 |
DPC | ET | 0.76 | 0.69 | 72.00 | 0.81 | 0.45 |
PCP | ET | 0.70 | 0.62 | 64.00 | 0.73 | 0.33 |
RRI | RF | 0.67 | 0.62 | 65.00 | 0.70 | 0.29 |
