Table 4.
Performance comparison with existing method over ACPAlter independent dataset.
| Predictor | Acc (%) | Sen (%) | Spe (%) | MCC |
|---|---|---|---|---|
| ACP-MHCNN | 90.00 | 86.50 | 93.30 | 0.800 |
| iACP-DRLF | 77.60 | 78.40 | 96.40 | 0.550 |
| AntiCP_2.0 | 92.00 | 92.30 | 91.80 | 0.840 |
| AntiCP | 90.00 | 89.70 | 90.20 | 0.800 |
| ACPred | 85.30 | 87.10 | 83.50 | 0.710 |
| ACPred-FL | 43.80 | 60.20 | 25.60 | − 0.150 |
| ACPpred-Fuse | 78.90 | 64.40 | 93.30 | 0.600 |
| iACP | 77.60 | 78.40 | 96.40 | 0.550 |
| ACP-DL | 88.10 | 86.00 | 90.20 | 0.762 |
| ACP-check | 93.00 | 93.00 | 93.00 | 0.860 |
| ME-ACP | 93.30 | 91.70 | 94.80 | 0.866 |
| TriNet | 96.60 | 98.50 | 94.70 | 0.930 |
| ACPPfel | 93.56 | 94.33 | 94.33 | 0.871 |
| PLMACPreda(Proposed) | 99.00 | 99.70 | 99.50 | 0.989 |
Best results highlighted in bold.