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Table 3 Comparison of the performance of our SVM model with that of Zhang’s SVM model [8]

From: Predicting lymph node metastasis and prognosis of individual cancer patients based on miRNA-mediated RNA interactions

Cancer Method_feature Train_score Test_score
BRCA Our model_\(\Delta\)PCC 0.972 0.787
Zhang_mRNA 0.798 0.680
Zhang_miRNA 0.764 0.737
Zhang_lncRNA 0.793 0.696
COAD Our model_\(\Delta\)PCC 0.984 0.905
Zhang_mRNA 0.849 0.871
Zhang_miRNA 0.902 0.886
Zhang_lncRNA 0.869 0.871
LUAD Our model_\(\Delta\)PCC 0.996 0.850
Zhang_mRNA 0.808 0.849
Zhang_miRNA 0.885 0.795
Zhang_lncRNA 0.798 0.849
LUSC Our model_\(\Delta\)PCC 0.999 0.904
Zhang_mRNA 0.871 0.900
Zhang_miRNA 0.939 0.847
Zhang_lncRNA 0.861 0.900
  1. In comparison of two types of features (RNA expression vs. deltaPCC), the better performances are shown in bold
  2. Among the seven types of cancer used in our study, comparison was made in four types of cancer because they are the only common cancer types in both studies. The train_score and test_score were obtained using the scikit-learn package, which was used by Zhang’s study. In all four caner types, our model showed the better performance in both training and testing. our model_\(\Delta\)PCC: SVM model using \(\Delta\)PCCs as features. Zhang_X: SVM model using the expression levels of RNA type X as features