From: Deep learning-based classification model for GPR151 activator activity prediction
Feature | Classifier | Accuracy | Precision | Recall | F1 | Trend |
---|---|---|---|---|---|---|
MorganFP processed | CNN | 0.8898 | 0.7124 | 0.6625 | 0.6866 | - |
LSTM | 0.8981 | 0.7082 | 0.749 | 0.728 | \(\uparrow\) | |
Bi-LSTM | 0.8793 | 0.683 | 0.6296 | 0.6552 | \(\downarrow\) | |
CNN+LR | 0.8883 | 0.7238 | 0.6255 | 0.6711 | \(\downarrow\) | |
CNN+KNN | 0.8756 | 0.6954 | 0.5638 | 0.6227 | \(\uparrow\) | |
CNN+RF | 0.8853 | 0.7368 | 0.5761 | 0.6467 | \(\uparrow\) | |
CNN+DT | 0.8748 | 0.7639 | 0.4527 | 0.5685 | - | |
CNN+SVM | 0.8906 | 0.7389 | 0.6173 | 0.6726 | \(\downarrow\) | |
LSTM+LR | 0.8973 | 0.707 | 0.7449 | 0.7255 | \(\uparrow\) | |
LSTM+KNN | 0.8958 | 0.7114 | 0.7202 | 0.7157 | \(\uparrow\) | |
LSTM+RF | 0.8921 | 0.6926 | 0.7325 | 0.712 | \(\uparrow\) | |
LSTM+DT | 0.8831 | 0.6835 | 0.6667 | 0.675 | \(\uparrow\) | |
LSTM+SVM | 0.8973 | 0.707 | 0.7449 | 0.7255 | \(\uparrow\) | |
Bi-LSTM+LR | 0.8748 | 0.6667 | 0.6255 | 0.6454 | \(\downarrow\) | |
Bi-LSTM+KNN | 0.8763 | 0.6639 | 0.6502 | 0.657 | \(\uparrow\) | |
Bi-LSTM+RF | 0.8763 | 0.6639 | 0.6502 | 0.657 | \(\uparrow\) | |
Bi-LSTM+DT | 0.8778 | 0.677 | 0.6296 | 0.6525 | \(\uparrow\) | |
Bi-LSTM+SVM | 0.8741 | 0.6623 | 0.6296 | 0.6456 | \(\downarrow\) |