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Table 2 Variable selection results for simulation setting 1

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

  Variable selection
Method Precision Recall F1 Score
LASSO 0.93(0.02) 0.26(0.06) 0.60(0.06)
Enet 0.90(0.04) 0.41(0.06) 0.61(0.06)
Network 0.85(0.02) 0.91(0.05) 0.80(0.06)
Abs-Network 0.82(0.02) 0.95(0.05) 0.81(0.06)
Merge-LASSO 0.94(0.02) 0.49(0.05) 0.62(0.05)
Merge-Enet 0.94(0.02) 0.56(0.04) 0.61(0.07)
Merge-Network 0.99(0.01) 0.94(0.03) 0.87(0.03)
Merge-Abs-Network 0.99(0.01) 0.98(0.02) 0.88(0.03)
Int-LASSO 0.95(0.01) 0.49(0.05) 0.88(0.02)
Int-Enet 0.96(0.01) 0.65(0.04) 0.88(0.02)
Int-Network 0.94(0.04) 0.96(0.03) 0.89(0.01)
Int-Abs-Network 0.91(0.05) 0.98(0.02) 0.90(0.01)
MetaLasso 0.94(0.01) 0.05(0.02) 0.75(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