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Table 3 Comparisons with Negatome and TissueNet by considering cancer types and normal counterparts (see Additional file 1: Table S5). The overlap is reported in percentage with respect to the number of total unique predictions per cancer/tissue. For each tissue, we report in the first row the percentage of INBIA’s overlapping, while in the second PERA’s one

From: INBIA: a boosting methodology for proteomic network inference

Cancer type Negatome (%) TissueNet (%)
BLCA 0.745 51.565
  1.463 44.390
BRCA 0.247 47.654
  0.830 29.461
COAD 0.609 44.901
  0.806 34.274
   30.323
GBM 0 36.452
   28.064
   25.484
   19.636
  0.727 26.909
   20.727
   15.636
HNSC 0.699 43.706
  0.820 31.967
KIRC 1.608 48.231
  2.137 40.171
LGG 0.62 32.812
  1.020 25.510
LUAD 0.711 46.373
  2.367 41.420
LUSC 0.895 46.418
  3.297 43.407
OV 0.665 25.942
  1.060 22.261
PRAD 0.602 39.458
  0.395 32.411
READ 1.064 43.769
  1.431 38.998
SKCM 0.446 54.018
  0.704 42.958
STAD 0.315 43.218
  0.772 35.135
THCA 0.308 26.769
   40.308
  0.763 19.466
   26.336
UCEC 0.615 48.615
  1.463 37.073
  1. Text highlighted in italic refers to our method (INBIA), the second one to PERA