Table 2 10-Fold cross-validation results on the training set of Embedding module, ProtT5 module with different ML and DL models.
From: Improving protein succinylation sites prediction using embeddings from protein language model
Encoding approach | Architecture | ACC | MCC | Sn | Sp |
|---|---|---|---|---|---|
Embedding | CNN2D | 0.73 ± 0.02 | 0.47 ± 0.05 | 0.76 ± 0.01 | 0.70 ± 0.01 |
LSTM | 0.71 ± 0.01 | 0.43 ± 0.02 | 0.77 ± 0.04 | 0.66 ± 0.03 | |
ProtT5 | RF | 0.62 ± 0.01 | 0.25 ± 0.01 | 0.59 ± 0.01 | 0.65 ± 0.01 |
SVM | 0.73 ± 0.01 | 0.46 ± 0.01 | 0.75 ± 0.02 | 0.71 ± 0.01 | |
XGBoost | 0.70 ± 0.01 | 0.41 ± 0.01 | 0.76 ± 0.01 | 0.65 ± 0.01 | |
CNN1D | 0.69 ± 0.01 | 0.38 ± 0.03 | 0.78 ± 0.08 | 0.59 ± 0.09 | |
ANN | 0.74 ± 0.01 | 0.47 ± 0.02 | 0.76 ± 0.02 | 0.71 ± 0.02 |
