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Table 3 Prediction results for simulation setting 2

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

  Prediction
Method Sensitivity Specificity Accuracy AUC
LASSO 0.63(0.05) 0.64(0.08) 0.62(0.02) 0.66(0.03)
Enet 0.61(0.04) 0.63(0.06) 0.61(0.03) 0.65(0.03)
Network 0.83(0.04) 0.85(0.06) 0.84(0.04) 0.92(0.04)
Abs-Network 0.85(0.05) 0.84(0.05) 0.84(0.03) 0.92(0.03)
Merge-LASSO 0.63(0.05) 0.63(0.06) 0.61(0.02) 0.66(0.02)
Merge-Enet 0.62(0.04) 0.63(0.07) 0.61(0.02) 0.66(0.02)
Merge-Network 0.82(0.04) 0.87(0.03) 0.84(0.03) 0.93(0.02)
Merge-Abs-Network 0.81(0.04) 0.86(0.04) 0.83(0.03) 0.92(0.03)
Int-LASSO 0.82(0.03) 0.89(0.03) 0.85(0.03) 0.93(0.02)
Int-Enet 0.82(0.04) 0.89(0.03) 0.85(0.03) 0.94(0.02)
Int-Network 0.88(0.04) 0.87(0.03) 0.87(0.02) 0.95(0.02)
Int-Abs-Network 0.89(0.04) 0.87(0.04) 0.88(0.02) 0.95(0.02)
MetaLasso 0.81(0.03) 0.82(0.04) 0.81(0.04) 0.90(0.03)
  1. The sign of β is shown in (10), \(\left (\bar \beta _{0}^{1},\bar \beta _{0}^{2},\bar \beta _{0}^{3},\bar \beta _{0}^{4}\right)=(-3,-1,1,3)\)
  2. The maximum value for each measure is highlighted using boldface font