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Table 8 Performance statistics on the test set for the weighted-SAMGSR algorithm (PPI information retrieved from the STRING database)

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

  No. Error (%) GBS BMC AUPR Rand (gene) Rand (GS)
MS (b) 22 43.3 0.279 0.581 0.847 15.3 % 27.1 %
MS (c) 20 28.3 0.179 0.613 0.828 15.5 % 25.4 %
Stage for LC (b) 32 45.3 0.318 0.520 0.552 36.3 % 40.1 %
Stage for LC (c) 26 45.3 0.274 0.525 0.566 35.8 % 40.4 %
MC for LC (b) 22a 47.3 0.337 0.411 0.510 -- --
MC for LC (c) 31a 51.3 0.334 0.410 0.512 -- --
  1. Note: (b): using the binary values indicating if two genes are connected or not; (c): using the confidence scores for the gene connectivity. MS: the multiple sclerosis application; Stage for LC: the NSCLC stage application trained on the RNA-Seq data; MC for LC: the NSCLC multiple-class application. Rand (gene): the rand index at the gene level, across the gene lists obtained from 10-fold cross-validation data; Rand (GS): the rand index at the gene set level
  2. ais the number of selected genes for the stage segmentation, the number of selected genes for the subtype segmentation > 300