Table 2 Comparison with the state-of-the-art methods
From: Antimicrobial peptide identification using multi-scale convolutional network
Method | SENS (%) | SPEC (%) | ACC (%) | MCC | auROC (%) | P value |
|---|---|---|---|---|---|---|
AntiBP2 | 87.91 | 90.8 | 89.37 | 0.7876 | 89.36 | < 0.001 |
CAMP-ANN | 82.98 | 85.09 | 84.04 | 0.6809 | 84.06 | < 0.001 |
CAMP-DA | 87.08 | 80.76 | 83.92 | 0.6797 | 89.97 | < 0.001 |
CAMP-RF | 92.7 | 82.44 | 87.57 | 0.7554 | 93.63 | < 0.001 |
CAMP-SVM | 88.9 | 79.92 | 84.41 | 0.691 | 90.63 | < 0.001 |
iAMP-2 L | 83.99 | 85.86 | 84.9 | 0.6983 | 84.9 | < 0.001 |
iAMPpred | 89.33 | 87.22 | 88.27 | 0.7656 | 94.44 | < 0.001 |
gkmSVM | 88.34 | 90.59 | 89.46 | 0.7895 | 94.98 | < 0.001 |
DNN | 89.89 | 92.13 | 91.01 | 0.8204 | 96.48 | < 0.001 |
proposed model | 91.01 | 93.64 | 92.41 | 0.8486 | 97.23 | < 0.001 |
fusion model with DNN | 88.48 | 93.26 | 90.87 | 0.8183 | 96.24 | < 0.001 |
proposed fusion model | 89.89 | 94.96 | 92.55 | 0.8523 | 97.3 | < 0.001 |