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Table 4 Variable selection results for simulation setting 2

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

  Variable selection
Method Precision Recall F1 Score
LASSO 0.91(0.04) 0.28(0.06) 0.60(0.07)
Enet 0.91(0.04) 0.35(0.07) 0.62(0.06)
Network 0.85(0.03) 0.74(0.12) 0.84(0.05)
Abs-Network 0.83(0.03) 0.77(0.13) 0.84(0.03)
Merge-LASSO 0.95(0.01) 0.42(0.08) 0.60(0.06)
Merge-Enet 0.95(0.01) 0.50(0.07) 0.61(0.05)
Merge-Network 0.98(0.01) 0.74(0.08) 0.84(0.03)
Merge-Abs-Network 0.98(0.01) 0.77(0.08) 0.84(0.03)
Int-LASSO 0.95(0.01) 0.43(0.09) 0.85(0.02)
Int-Enet 0.96(0.01) 0.64(0.07) 0.86(0.03)
Int-Network 0.93(0.03) 0.83(0.07) 0.87(0.03)
Int-Abs-Network 0.92(0.04) 0.84(0.09) 0.88(0.03)
MetaLasso 0.94(0.01) 0.04(0.02) 0.81(0.04)
  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