Skip to main content

Table 3 Experiment results of six methods on Dataset3 under CVP setting

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

  gGATLDA BiWalkLDA SIMCLDA MFLDA BiGAN GCRFLDA
AUC 0.9888 ± 0.0065 0.8185 ± 0.0024 0.8465 ± 0.0030 0.8478 ± 0.0048 0.9045 ± 0.0185 0.9583 \(\pm\) 0.0055
Precision 0.7980 ± 0.0367 0.6370 ± 0.0033 0.7247 ± 0.0142 0.8667 ± 0.1310 0.6572 ± 0.0073 0.9020 \(\pm\) 0.0052
Recall 0.9913 ± 0.0078 0.7297 ± 0.0121 0.8475 ± 0.0162 0.6942 ± 0.1089 0.9495 ± 0.0132 0.8632 \(\pm\) 00,202
AUPR 0.9890 ± 0.0060 0.8416 ± 0.0031 0.8450 ± 0.0053 0.8860 ± 0.0032 0.9058 ± 0.0192 0.9548 \(\pm\) 0.0090
Accuracy 0.8670 ± 0.0271 0.6568 ± 0.0032 0.7623 ± 0.0065 0.7652 ± 0.0867 0.7270 ± 0.0088 0.9103 \(\pm\) 0.0044
F1-Score 0.8830 ± 0.0217 0.6801 ± 0.0049 0.7810 ± 0.0022 0.7523 ± 0.0324 0.7767 ± 0.0068 0.8817 \(\pm\) 0.0130
  1. The best results in each row are represented in bold