Table 3 The overall performance of compared to the existing methods
From: Predicting disease genes based on multi-head attention fusion
Method | AUC (%) | Accuracy (%) | F1-score (%) | Precision (%) | Recall (%) | AUPRC (%) |
|---|---|---|---|---|---|---|
LR | 82.21 | 82.11 | 79.62 | 76.08 | 83.51 | 86.49 |
RF | 84.38 | 84.39 | 81.79 | 78.89 | 84.91 | 88.24 |
SVM | 82.98 | 82.98 | 80.62 | 77.04 | 84.56 | 87.13 |
DT | 70.35 | 78.25 | 66.39 | 64.48 | 68.42 | 77.69 |
KNN | 79.67 | 79.67 | 78.64 | 72.78 | 85.54 | 84.75 |
GB | 85.09 | 85.09 | 82.80 | 79.56 | 86.32 | 88.70 |
MLP | 83.51 | 83.51 | 80.16 | 78.31 | 82.11 | 87.77 |
GAT | 82.35 | 91.42 | 85.36 | 82.03 | 88.97 | 82.00 |
HAN | 90.57 | 94.75 | 85.68 | 82.6 | 88.99 | 89.79 |
PINDeL | 81.42 | 83.45 | 81.84 | 79.67 | 84.13 | 86.05 |
dgMDL | 87.82 | 87.82 | 85.46 | 83.22 | 87.82 | 90.87 |
DGHNE | 78.94 | 78.94 | 66.79 | 78.94 | 57.89 | 89.47 |
MHAGP | 93.84 | 90.64 | 90.84 | 86.48 | 95.66 | 92.86 |