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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