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Table 1 Model performance on different training and evaluation data partitions

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

Training set

Evaluation set

SENS(%)

SPEC(%)

ACC(%)

MCC

AUC (%)

Train

Tune

95.76

83.85

87.80

0.7582

96.67

Train

Test

93.39

90.44

91.92

0.8388

97.22

Train+Tune

Test

92.42

94.17

93.39

0.8663

97.63

All Data

All Data

98.26

99.66

98.96

0.9793

99.94

All Data

10-fold CV

91.37(± 1.05)

93.32(± 1.73)

92.46(± 0.87)

0.8474(± 0.01)

96.79(± 0.47)

  1. Note: Performance is shown for ACEP model built and evaluated on the datasets listed in columns 1 and 2, respectively, on metrics listed in columns 3-7. The bottom line shows 10-fold CV performance, and the standard deviation is shown in parentheses
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BMC Genomics

ISSN: 1471-2164

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