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Table 6 Performance statistics of selected genes on NSCLC RNA-seq data (stage segmentation)

From: Weighted-SAMGSR: combining significance analysis of microarray-gene set reduction algorithm with pathway topology-based weights to select relevant genes

  Training set (10-fold CV results) Test set
Method (n) Error (%) GBS BCM AUPR Error (%) GBS BCM AUPR
SAMGSR (9) 35.2 0.242 0.539 0. 575 50 0.279 0.507 0.531
W-SAMGSR (8) 32.8 0.231 0.556 0.584 49.3 0.276 0.513 0.580
LASSO (30) 36 0.219 0.558 0.610 50 0.453 0.500 0.509
Penalized SVM (34) 36.8 0.255 0.562 0.603 50 0.329 0.501 0.518
gelnet (252) 36.8 0.231 0.517 0.547 50 0.465 0.499 0.475
RRFE (93) 35.2 0.185 0.545 0.578 50 0.471 0.500 0.506
  1. Note: W-SAMGSR weighted-SAMGSR, gelnet generalized elastic net, RRFE reweighted recursive feature elimination