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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