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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
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BMC Bioinformatics

ISSN: 1471-2105

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