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Table 3 Performance comparison with existing methods on the benchmark test dataset

From: Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features

Method

Acc

AUROC

AUCPR

Kappa

Sen

Spe

MCC

References

iAMP-2L

65.4%

0.318

82.9%

47.9%

0.329

Xiao et al. [25]

iAMPpred

70.7%

0.415

80.8%

60.6%

0.424

Meher et al. [26]

AmPEP

68.0%

0.751

0.686

0.362

93.6%

42.5%

0.421

Bhadra et al. [27]

AMP Scanner DNN

73.4%

0.806

0.777

0.468

80.8%

65.9%

0.473

Veltri et al. [28]

RF-AmPEP30

77.1%

0.854

0.868

0.543

77.6%

76.6%

0.542

Yan et al. [32]

Deep-AmPEP30

77.1%

0.853

0.853

0.543

76.6%

77.7%

0.543

Yan et al. [32]

Co-AMPpred

80.8%

0.871

0.890

0.606

79.7%

81.9%

0.606

This study

Co-AMPpred70

78.6%

0.861

0.860

0.553

80.9%

74.5%

0.554

This study

Co-AMPpred80

76.6%

0.851

0.840

0.532

78.7%

74.5%

0.532

This study

Co-AMPpred90

70.2%

0.843

0.860

0.404

89.4%

51.1%

0.438

This study

  1. The given data in bold font indicates the top performance of the model on the test dataset
  2. Acc., accuracy; AUROC, area under the receiver operating characteristics curve; AUCPR, area under the precision-recall curve; Sen., sensitivity; Spe., specificity; MCC, Matthew's correlation coefficient; SD, standard deviation