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Table 7 The experimental results of different models based on 10-fold cross-validation

From: Predicting potential microbe-disease associations based on auto-encoder and graph convolution network

Dataset: MDAID

Dataset: HMDAD

Methods

AUC (%)

AUPR (%)

AUC (%)

AUPR (%)

NTSHMDA [56]

75.67

18.56

74.97

18.19

NCPHMDA [57]

79.89

17.86

79.01

17.43

LRLSHMDA [58]

79.92

18.19

79.99

18.21

KATZHMDA [25]

81.35

19.78

81.44

19.89

ABHMDA [33]

94.78

92.89

94.11

94.61

KGNMDA [40]

93.87

94.07

93.15

94.13

DSAE_RF [34]

94.48

94.31

94.49

94.69

DAEGCNDF(our)

97.00

96.90

96.32

96.71

  1. The bold result indicates the best one in each column
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BMC Bioinformatics

ISSN: 1471-2105

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