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Table 2 Experiment results of six methods on Dataset2 under CVP setting

From: gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network

 

gGATLDA

BiWalkLDA

SIMCLDA

MFLDA

BiGAN

GCRFLDA

AUC

0.9870 ± 0.0024

0.6499 ± 0.0022

0.8433 ± 0.0035

0.8270 ± 0.0033

0.8932 ± 0.0118

0.9548 \(\pm\) 0.0055

Precision

0.9098 ± 0.0136

0.4958 ± 0.0040

0.6979 ± 0.0114

0.9261 ± 0.0368

0.8031 ± 0.0129

0.8840 \(\pm\) 0.0063

Recall

0.9759 ± 0.0068

0.8466 ± 0.0264

0.8997 ± 0.0103

0.5905 ± 0.0646

0.7990 ± 0.0443

0.8689 \(\pm\) 0.0208

AUPR

0.9864 ± 0.0025

0.7419 ± 0.0036

0.8824 ± 0.0053

0.8720 ± 0.0027

0.8857 ± 0.0200

0.9512 \(\pm\) 0.0088

Accuracy

0.9395 ± 0.0083

0.4930 ± 0.0065

0.7549 ± 0.0080

0.7698 ± 0.0166

0.8016 ± 0.0214

0.8859 \(\pm\) 0.0077

F1-Score

0.9416 ± 0.0076

0.6253 ± 0.0104

0.7859 ± 0.0041

0.7174 ± 0.0358

0.8005 ± 0.0261

0.8755 \(\pm\) 0.0125

  1. The best results in each row are represented in bold