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Table 18 Impact of augmenting inferred interactions on the performance of MCL, MCL-CAw, CMC and HACO

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 Precision #Derivable benchmarks #Derived benchmarks Recall
MCL G+K 242 55 0.226 182 62 0.338
  I 50 2 0.040 31 3 0.097
  G+K+I 249 55 0.221 189 58 0.307
  ICD(G+K+I) 115 53 0.461 156 58 0.372
  FSW(G+K+I) 89 54 0.607 141 61 0.433
MCL-Caw G+K 310 77 0.248 182 77 0.423
  I 42 2 0.048 31 3 0.097
  G+K+I 315 78 0.247 189 78 0.412
  ICD(G+K+I) 118 82 0.694 156 82 0.525
  FSW(G+K+I) 95 84 0.884 141 84 0.596
CMC G+K 113 60 0.531 182 60 0.330
  I 10 3 0.300 31 5 0.161
  G+K+I 119 60 0.504 189 63 0.333
  ICD(G+K+I) 184 77 0.418 156 83 0.532
  FSW(G+K+I) 186 74 0.398 141 80 0.567
HACO G+K 278 78 0.281 182 85 0.467
  I 12 2 0.167 31 2 0.064
  G+K+I 309 78 0.252 189 84 0.444
  ICD(G+K+I) 119 66 0.589 156 75 0.481
  FSW(G+K+I) 98 61 0.622 141 70 0.496
  1. Most algorithms showed marginal dip in performance on Gavin+Krogan+Inferred compared to Gavin+Krogan. However, upon scoring the augmented network, their performance was better compared to Gavin+Krogan. This indicated that inferred interactions were useful for complex detection provided affinity scoring is employed to reduce the impact of the noise present in them.