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Table 2 Performance comparison on the AMP dataset testing partition

From: ACEP: improving antimicrobial peptides recognition through automatic feature fusion and amino acid embedding

Method SENS(%) SPEC(%) ACC(%) MCC AUC(%)
AntiBP2 87.91 90.80 89.37 0.7876 89.36
CAMPr3-ANN 83.00 85.11 84.05 0.6813 84.05
CAMPr3-DA 87.07 80.75 83.91 0.6797 89.97
CAMPr3-RF 92.69 82.44 87.57 0.7553 93.63
CAMPr3-SVM 88.62 80.47 84.55 0.6933 90.62
iAMP-2L 83.99 85.86 84.90 0.6983 84.90
iAMPpred 89.33 87.22 88.27 0.7656 94.44
gkmSVM 88.34 90.59 89.46 0.7895 94.98
AMPScanner 89.88 92.69 91.29 0.8261 96.30
ACEP 92.41 93.67 93.04 0.8610 97.78
  1. Note: Recognition performance on the testing dataset is shown for state-of-the-art methods (listed in column 1) on the metrics listed in columns 2-6. The best performance on a metric is marked in bold. Our deep neural network is shown in row 10