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Table 1 The comparison between Multi-TGDR frameworks and PLS-DA using simulated data

From: Multi-TGDR, a multi-class regularization method, identifies the metabolic profiles of hepatocellular carcinoma and cirrhosis infected with hepatitis B or hepatitis C virus

  # metabolites Error on the data (%) GBS 5-fold CV Error (%)
A. Simulation 1     
Multi-TGDR: global No Bagging 105 0.76 0.0100 12.21
Global + Bagging (freq > 30%) 35 10.69 0.0773 12.21
local No Bagging 54 (14, 46)1 2.29 0.0301 14.50
Local + Bagging (freq > 40%) 24 (14, 15) 8.40 0.0539 12.98
PLS-DA + Naïve Bayes as a classifier 89 14.50 0.1313 19.84
B. Simulation 2
Multi-TGDR: global No Bagging 110 0 0.0165 11.45
Global + Bagging (freq > 50%) 21 3.82 0.0237 7.63
local No Bagging 106(12, 95) 0 0.0067 9.16
Local + Bagging (freq > 40%) 25(9, 18) 3.82 0.0254 8.40
PLS-DA + Naïve Bayes as a classifier 97 6.87 0.1556 16.03
  1. A. The performance of multi-TGDR frameworks and PLS-DA on the first simulated data. B. The performance of multi-TGDR and PLS-DA on the second simulated data.
  2. 1(No.1, No.2): No.1 represents the number of metabolites selected in the first comparison (class 2 versus class 1) by multi-TGDR local. No.2 represents the number of metabolites selected in the second comparison (class 3 versus class 1).