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Table 1 Prediction results for simulation setting 1

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

 

Prediction

Method

Sensitivity

Specificity

Accuracy

AUC

LASSO

0.63(0.05)

0.62(0.04)

0.62(0.02)

0.66(0.02)

Enet

0.65(0.05)

0.64(0.05)

0.63(0.02)

0.68(0.02)

Network

0.82(0.06)

0.82(0.06)

0.81(0.05)

0.89(0.05)

Abs-Network

0.82(0.05)

0.82(0.06)

0.81(0.04)

0.89(0.04)

Merge-LASSO

0.65(0.04)

0.65(0.06)

0.63(0.02)

0.68(0.02)

Merge-Enet

0.65(0.05)

0.64(0.05)

0.63(0.02)

0.68(0.02)

Merge-Network

0.87(0.04)

0.88(0.03)

0.88(0.03)

0.95(0.02)

Merge-Abs-Network

0.88(0.04)

0.88(0.03)

0.88(0.02)

0.95(0.02)

Int-LASSO

0.88(0.02)

0.88(0.02)

0.88(0.02)

0.96(0.01)

Int-Enet

0.88(0.02)

0.88(0.02)

0.88(0.02)

0.96(0.01)

Int-Network

0.89(0.02)

0.90(0.02)

0.89(0.01)

0.96(0.01)

Int-Abs-Network

0.90(0.02)

0.90(0.02)

0.90(0.01)

0.97(0.01)

MetaLasso

0.75(0.05)

0.76(0.04)

0.76(0.04)

0.84(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