Skip to main content

Table 2 The predictive performance of Multi-TGDR frameworks and PLS-DA

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. (without filtering)     
Multi-TGDR global No Bagging 45 0 4.24e-05 3.82
Bagging (freq > 40%) 30 0 3.68e-05 3.82
Multi-TGDR local No Bagging 48 0 7.57e-05 5.34
Bagging (freq > 40%) 29 0 5.97e-04 6.11
B. (after moderated t-test filtering)
Multi-TGDR global No Bagging 42 0 1.03e-04 4.58
Bagging (freq > 25%) 37 0 1.13e-04 4.58
Bagging (freq > 40%) 26 0 3.58e-04 4.58
Multi-TGDR local No Bagging 42 0 6.18e-04 6.11
Bagging (freq > 25%) 38 0 6.87e-04 5.34
Bagging (freq > 40%) 25 0 2.24e-03 6.11
C. the performance of PLS-DA on the whole data
Naïve Bayes as the extra classifier 42 4.58 4.63e-02 7.63
  1. A. The performance of multi-TGDR frameworks on the whole data: without moderated t-test filtering. B. The performance of multi-TGDR frameworks on the reduced data: with t-test filtering and 72 metabolites were filtered out. C. The performance of PLS-DA with naïve Bayes as the classifier. 42 metabolites selected by original analysis in Zhou’s study ref. [19] were used.
  2. Note: For the reduced data, the optimal cutoff of bagging frequencies is 25%. However, in order to make a fair comparison with the results from the whole data, we analyzed the reduced data with bagging frequencies as 40% as well.