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Table 1 Performance of the prediction model with different types of features in the fivefold cross validation

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

Cancer Feature #Features #PCs SN SP ACC PPV NPV AUC
BRCA EXP 5119 430 0.674 0.709 0.692 0.694 0.689 0.691
\(\Delta\)PCC 1563 480 0.773 0.806 0.790 0.796 0.784 0.789
COAD EXP 835 100 0.360 0.935 0.758 0.711 0.767 0.647
\(\Delta\)PCC 1969 80 0.760 0.965 0.902 0.905 0.901 0.862
HNSC EXP 292 10 0.750 0.684 0.720 0.739 0.696 0.717
\(\Delta\)PCC 800 100 0.956 0.877 0.920 0.903 0.943 0.917
LUAD EXP 6193 110 0.477 0.882 0.741 0.683 0.759 0.679
\(\Delta\)PCC 12,981 200 0.593 0.944 0.822 0.850 0.813 0.769
LUSC EXP 1371 190 0.644 0.867 0.786 0.736 0.809 0.756
\(\Delta\)PCC 2436 200 0.875 0.934 0.912 0.884 0.929 0.904
STAD EXP 476 120 0.905 0.472 0.763 0.778 0.708 0.688
\(\Delta\)PCC 17,445 60 0.973 0.903 0.950 0.953 0.942 0.938
THCA EXP 4205 30 0.663 0.663 0.663 0.634 0.691 0.663
\(\Delta\)PCC 3397 150 0.674 0.723 0.700 0.682 0.716 0.698
  1. In comparison of two types of features (RNA expression vs. deltaPCC), the better performances are shown in bold
  2. In all cancer types, prediction with \(\Delta\)PCCs showed a better performance than that with RNA expression levels
  3. PC, principal component; SN, sensitivity; SP, specificity; ACC, accuracy; PPV, positive predictive value; NPV, negative predictive value; AUC, area under the curve; EXP, RNA expression level