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Table 1 Mean values and standard deviations of AUROC and AUPR on Dataset1 and Dataset2, compared with different methods

From: A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations

Method Dataset1 Dataset2
AUROC AUPR AUROC AUPR
LRLSLDA 0.8157 ± 0.0005 0.2035 ± 0.0001 0.8627 ± 0.0017 0.1812 ± 0.0021
SIMCLDA 0.8293 ± 0.0023 0.5357 ± 0.0011 0.8146 ± 0.0042 0.1189 ± 0.0076
TPGLDA 0.7936 ± 0.0054 0.5308 ± 0.0028 0.8771 ± 0.0053 0.3192 ± 0.0058
SKFLDA 0.9154 ± 0.0013 0.4024 ± 0.0017 0.8524 ± 0.0066 0.2831 ± 0.0085
GAMCLDA 0.9299 ± 0.0033 0.5794 ± 0.0143 0.8841 ± 0.0110 0.3798 ± 0.0154
VGAELDA 0.9680 ± 0.0042 0.8380 ± 0.0041 0.9692 ± 0.0080 0.8203 ± 0.0139
  1. The bold number is the highest value of each column, which is achieved by our method, VGAELDA. The bold clarifies the superiority of our method