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Table 2 Classification performance on BRCA and KIPAN datasets

From: Amogel: a multi-omics classification framework using associative graph neural networks with prior knowledge for biomarker identification

Experiment

BRCA

KIPAN

Acc (STD)

F1 (STD)

AUROC (STD)

Acc (STD)

F1 (STD)

AUROC (STD)

KNN

72.12(1.6)

45.56(1.1)

79.19(1.5)

93.03(1.6)

92.24(1.5)

96.80(0.9)

SVM

72.47(2.7)

44.39(4.5)

90.56(1.0)

92.89(1.9)

91.86(2.3)

98.45(0.5)

NB

81.39(3.1)

74.87(4.9)

92.50(2.5)

95.86(1.8)

94.58(2.2)

97.21(0.7)

DNN

75.58(2.7)

62.37(5.4)

90.05(1.0)

95.15(1.5)

94.12(2.2)

98.81(0.7)

MOGONET [5]

75.50(2.1)

60.96(5.9)

87.97(2.9)

95.65(1.2)

94.90(1.6)

97.38(1.8)

MOGLAM [11]

76.88(2.3)

64.97(4.1)

92.13(1.0)

94.75(1.5)

93.72(1.4)

98.64(0.6)

AMOGEL

86.32(1.7)

75.67(4.4)

94.36(0.6)

96.06(1.4)

95.08(1.4)

99.37(0.4)

  1. Bold indicates the highest value and the bracket values are the standard deviation