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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