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Table 1 Prediction results for simulation setting 1

From: Network-based logistic regression integration method for biomarker identification

  Prediction
Method Sensitivity Specificity Accuracy AUC
LASSO 0.63(0.05) 0.62(0.04) 0.62(0.02) 0.66(0.02)
Enet 0.65(0.05) 0.64(0.05) 0.63(0.02) 0.68(0.02)
Network 0.82(0.06) 0.82(0.06) 0.81(0.05) 0.89(0.05)
Abs-Network 0.82(0.05) 0.82(0.06) 0.81(0.04) 0.89(0.04)
Merge-LASSO 0.65(0.04) 0.65(0.06) 0.63(0.02) 0.68(0.02)
Merge-Enet 0.65(0.05) 0.64(0.05) 0.63(0.02) 0.68(0.02)
Merge-Network 0.87(0.04) 0.88(0.03) 0.88(0.03) 0.95(0.02)
Merge-Abs-Network 0.88(0.04) 0.88(0.03) 0.88(0.02) 0.95(0.02)
Int-LASSO 0.88(0.02) 0.88(0.02) 0.88(0.02) 0.96(0.01)
Int-Enet 0.88(0.02) 0.88(0.02) 0.88(0.02) 0.96(0.01)
Int-Network 0.89(0.02) 0.90(0.02) 0.89(0.01) 0.96(0.01)
Int-Abs-Network 0.90(0.02) 0.90(0.02) 0.90(0.01) 0.97(0.01)
MetaLasso 0.75(0.05) 0.76(0.04) 0.76(0.04) 0.84(0.04)
  1. β is shown in (9), \(\left (\bar \beta _{0}^{1},\bar \beta _{0}^{2},\bar \beta _{0}^{3},\bar \beta _{0}^{4}\right)=(-3,-1,1,3)\)
  2. The maximum value for each measure is highlighted using boldface font