Results of the 10-fold CV on the NetOGlyc-4.0 extracellular training dataset using various models when training datasets are encoded with ProtT5 PLM. The highest value in each column have been highlighted in bold.
| Models . | MCC ± 1 SD . | ACC ± 1 SD . | SN ± 1 SD . | SP ± 1 SD . |
|---|---|---|---|---|
| LR | 0.582 ± 0.058 | 0.790 ± 0.029 | 0.824 ± 0.040 | 0.756 ± 0.038 |
| XGBoost | 0.609 ± 0.052 | 0.803 ± 0.026 | 0.828 ± 0.042 | 0.778 ± 0.052 |
| Random forest | 0.574 ± 0.075 | 0.785 ± 0.038 | 0.771 ± 0.077 | 0.799 ± 0.061 |
| SVM | 0.623 ± 0.034 | 0.810 ± 0.018 | 0.850 ± 0.030 | 0.769 ± 0.043 |
| MLP | 0.635 ± 0.067 | 0.815 ± 0.032 | 0.848 ± 0.067 | 0.781 ± 0.050 |
| Models . | MCC ± 1 SD . | ACC ± 1 SD . | SN ± 1 SD . | SP ± 1 SD . |
|---|---|---|---|---|
| LR | 0.582 ± 0.058 | 0.790 ± 0.029 | 0.824 ± 0.040 | 0.756 ± 0.038 |
| XGBoost | 0.609 ± 0.052 | 0.803 ± 0.026 | 0.828 ± 0.042 | 0.778 ± 0.052 |
| Random forest | 0.574 ± 0.075 | 0.785 ± 0.038 | 0.771 ± 0.077 | 0.799 ± 0.061 |
| SVM | 0.623 ± 0.034 | 0.810 ± 0.018 | 0.850 ± 0.030 | 0.769 ± 0.043 |
| MLP | 0.635 ± 0.067 | 0.815 ± 0.032 | 0.848 ± 0.067 | 0.781 ± 0.050 |
Results of the 10-fold CV on the NetOGlyc-4.0 extracellular training dataset using various models when training datasets are encoded with ProtT5 PLM. The highest value in each column have been highlighted in bold.
| Models . | MCC ± 1 SD . | ACC ± 1 SD . | SN ± 1 SD . | SP ± 1 SD . |
|---|---|---|---|---|
| LR | 0.582 ± 0.058 | 0.790 ± 0.029 | 0.824 ± 0.040 | 0.756 ± 0.038 |
| XGBoost | 0.609 ± 0.052 | 0.803 ± 0.026 | 0.828 ± 0.042 | 0.778 ± 0.052 |
| Random forest | 0.574 ± 0.075 | 0.785 ± 0.038 | 0.771 ± 0.077 | 0.799 ± 0.061 |
| SVM | 0.623 ± 0.034 | 0.810 ± 0.018 | 0.850 ± 0.030 | 0.769 ± 0.043 |
| MLP | 0.635 ± 0.067 | 0.815 ± 0.032 | 0.848 ± 0.067 | 0.781 ± 0.050 |
| Models . | MCC ± 1 SD . | ACC ± 1 SD . | SN ± 1 SD . | SP ± 1 SD . |
|---|---|---|---|---|
| LR | 0.582 ± 0.058 | 0.790 ± 0.029 | 0.824 ± 0.040 | 0.756 ± 0.038 |
| XGBoost | 0.609 ± 0.052 | 0.803 ± 0.026 | 0.828 ± 0.042 | 0.778 ± 0.052 |
| Random forest | 0.574 ± 0.075 | 0.785 ± 0.038 | 0.771 ± 0.077 | 0.799 ± 0.061 |
| SVM | 0.623 ± 0.034 | 0.810 ± 0.018 | 0.850 ± 0.030 | 0.769 ± 0.043 |
| MLP | 0.635 ± 0.067 | 0.815 ± 0.032 | 0.848 ± 0.067 | 0.781 ± 0.050 |
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