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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