Figure 1.
Autoencoder-based two-level classifier
Table 2.
Comparison of the trainings of the First Level
| Loss Function | Loss Function (Validation) | |
|---|---|---|
| Without SMOTE | 0.0202 | 0.0223 |
| SMOTE | 0.0127 | 0.0162 |
| Hyperparameter optimization. (SMOTE) | 0.0025 | 0.0074 |
Table 3.
Comparison of the training of the Second Level
| Loss Function | Loss Function (Validation) | |
|---|---|---|
| Without SMOTE | 0.0328 | 0.1163 |
| SMOTE | 0.0518 | 0.0330 |
| Hyperparameter optimization. (SMOTE) | 0.0392 | 0.0350 |
Table 4.
Comparison of different softwares for the classification of sequences into enzymes or non-enzymes (precision)
| EzyPred | ECPred | Proteinfer | AE | |
|---|---|---|---|---|
| Not Enzyme | 0.59 | 0.57 | 0.47 | 0.91 |
| Enzyme | 1.00 | 0.82 | 0.95 | 1.00 |
Figure 2.
Loss function (MSE) of the first level classifier using SMOTE
Figure 3.
Descriptive diagram of the workflow of the second level of the classifier. The processes AE1, AE2, …, AEn represent the Autoencoders of the corresponding enzyme families F1, F2,…, Fn
Figure 4.
Architecture of the two-level classifier (the enzyme family classifier) once implemented using the TensorFlow library
Figure 5.
Loss function (categorical cross-entropy) of the second level classifier using SMOTE
Table 5.
Comparison of different softwares for the classification of sequences into enzymes or non-enzymes (recall)
| EzyPred | ECPred | Proteinfer | AE | |
|---|---|---|---|---|
| Not Enzyme | 1.00 | 0.40 | 0.90 | 1.00 |
| Enzyme | 0.77 | 0.90 | 0.67 | 0.97 |
Table 6.
Comparison of different softwares for the classification of sequences into enzymes or non-enzymes (F1-score)
| EzyPred | ECPred | Proteinfer | AE | |
|---|---|---|---|---|
| Not Enzyme | 0.74 | 0.47 | 0.62 | 0.95 |
| Enzyme | 0.87 | 0.86 | 0.78 | 0.98 |
Figure 6.
Comparison of Accuracy by different software for enzyme classification or not