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Table 5 (i) Impact of introducing different levels of artificial noise on MCL and MCL-CAw (ii) Role of affinity scoring in reducing the impact of noise

From: MCL-CAw: a refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Method PPI
Network
#Predicted complexes #Matched predictions Precisions #Derivable benchmarks #Derived benchmarks Recall
MCL G+K 242 55 0.226 182 62 0.338
  G+K+Rand2k 265 56 0.215 182 64 0.352
  G+K+Rand5k 274 61 0.223 182 68 0.379
  G+K+Rand10k 316 64 0.202 182 69 0.379
  ICD(G+K) 119 73 0.613 153 73 0.477
  ICD(G+K+Rand2k) 104 59 0.567 153 66 0.431
  ICD(G+K+Rand5k) 108 60 0.546 151 65 0.430
  ICD(G+K+Rand10k) 112 60 0.546 150 65 0.433
MCL-CAw G+K 310 77 0.248 182 77 0.423
  G+K+Rand2k 140 59 0.421 182 68 0.374
  G+K+Rand5k 116 62 0.534 182 70 0.384
  G+K+Rand10k 176 64 0.363 182 68 0.373
  ICD(G+K) 129 80 0.620 153 80 0.523
  ICD(G+K+Rand2k) 102 62 0.608 153 73 0.477
  ICD(G+K+Rand5k) 102 64 0.627 151 76 0.503
  ICD(G+K+Rand10k) 106 64 0.603 150 76 0.506
  1. The Gavin+Krogan network was introduced with 2000 - 10000 (10% to 75%) random interactions. Following this, these noisy networks were scored using the ICD scheme. With the aid of scoring, MCL-CAw was able to perform better than MCL even at 50% random noise.