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Table 1 Deviance and C-index results for models chosen by 5-fold cross-validation and tested on all 3 datasets (including 2 that were hidden from the training phase). The LASSO and RIDGE methods do not use network information so the values for GCN and GFM are the same, they are only shown in both networks when they are better than DEGREECOX and NET-COX

From: DegreeCox – a network-based regularization method for survival analysis

  Train Bonome    TCGA    Tothill   
  Test Bonome TCGA Tothill Bonome TCGA Tothill Bonome TCGA Tothill
  Network GCN GFM GCN GFM GCN GFM GCN GFM GCN GFM GCN GFM GCN GFM GCN GFM GCN GFM
RMSE DegreeCox 0 . 5 5 8 1 0 . 7 7 2 4 1.3189 1.1538 1.2139 1.1027 1 . 2 3 6 7 1.3619 0.9201 0.8043 1 . 0 5 7 3 1.1083 1.6326 1.2975 1.3749 1.1679 0 . 5 1 1 6 0.7013
  Net-Cox 0.8131 0.8353 1.1438 1 . 1 2 8 5 1.0992 1 . 0 8 8 6 1.3514 1 . 3 0 4 5 0.8361 0.8508 1.1003 1 . 0 8 0 2 1 . 2 9 1 7 1 . 2 5 9 1 1 . 1 6 1 2 1 . 1 4 0 3 0.7363 0.7606
  Ridge 0.7807 1 . 1 4 1 3   1 . 0 9 8 6   1.3755 0 . 7 2 1 5 1.1769 1.5649 1.3252   0 . 5 4 3 2
  Lasso 0.7887 1.4619 1.2586 1.7419 0.8105 1.3019 1.9595 1.4208 0.5444
C-Index DegreeCox 0 . 9 7 9 5 0.9401 0.6020 0.6037 0.6455 0.6494 0.6444 0.6427 0.8476 0.9089 0 . 6 7 1 1 0.6695 0.6011 0.6088 0.6100 0.6215 0 . 9 8 3 4 0.9519
  Net-Cox 0.9260 0.9202 0.6079 0.6054 0.6483 0.6506 0.6416 0.6439 0.8918 0.8892 0.6633 0 . 6 7 0 5 0 . 6 1 5 2 0 . 6 1 0 6 0 . 6 2 4 4 0 . 6 2 5 0 0.9389 0.9352
  Ridge   0 . 9 4 1 0 0 . 6 1 7 7 0 . 6 5 6 9 0 . 6 4 9 2 0 . 9 3 9 4 0.6579 0.6000 0.5926   0 . 9 8 2 9
  Lasso 0.9309 0.5615 0.6124 0.6405 0.9043 0.6399 0.5075 0.5728 0.9784
  1. Values in bold represent the best performing method for the dataset/network combination (per RMSE and C-Index)