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Table 1 Experiment results of six methods on Dataset1 under CVP setting

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

 

gGATLDA

BiWalkLDA

SIMCLDA

MFLDA

BiGAN

GCRFLDA

AUC

0.9442 \(\pm\) 0.0025

0.8435 \(\pm\) 0.0028

0.7836 \(\pm\) 0.0113

0.7223 \(\pm\) 0.0118

0.5246 \(\pm\) 0.0475

0.8120 \(\pm\) 0.0174

Precision

0.8124 \(\pm\) 0.0346

0.7538 \(\pm\) 0.0325

0.6822 \(\pm\) 0.0832

0.6928 \(\pm\) 0.1351

0.4972 \(\pm\) 0.0555

0.7273 \(\pm\) 0.0197

Recall

0.9029 \(\pm\) 0.0276

0.7968 \(\pm\) 0.0135

0.7591 \(\pm\) 0.0861

0.6705 \(\pm\) 0.1342

0.5025 \(\pm\) 0.0759

0.7025 \(\pm\) 0.0473

AUPR

0.9493 \(\pm\) 0.0022

0.8727 \(\pm\) 0.0079

0.8203 \(\pm\) 0.0125

0.7895 \(\pm\) 0.0100

0.5029 \(\pm\) 0.0422

0.7806 \(\pm\) 0.0236

Accuracy

0.8455 \(\pm\) 0.0150

0.7768 \(\pm\) 0.0216

0.6866 \(\pm\) 0.0546

0.6432 \(\pm\) 0.0941

0.4992 \(\pm\) 0.0551

0.7473 \(\pm\) 0.0153

F1-Score

0.8541 \(\pm\) 0.0093

0.7740 \(\pm\) 0.0128

0.7087 \(\pm\) 0.0200

0.6552 \(\pm\) 0.0205

0.4995 \(\pm\) 0.0651

0.7127 \(\pm\) 0.0323

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