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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
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BMC Genomics

ISSN: 1471-2164

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