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Table 3 (i) Impact of core-attachment refinement on MCL; (ii) Role of affinity scoring in reducing the impact of natural noise on MCL and MCL-CAw

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

Benchmark Method PPI Network #Predicted complexes #Matched prediction Precision #Derivable benchmarks #Derived benchmarks Recall
Wodak MCL G+K 242 55 0.226 182 62 0.338
   ICD(G+K) 136 68 0.500 153 76 0.497
   FSW(G+K) 120 69 0.575 153 78 0.510
   Consol3.19 116 70 0.603 145 79 0.545
   Boot0.094 203 76 0.374 172 85 0.494
  MCL-CAw G+K 310 77 0.248 182 77 0.423
   ICD(G+K) 129 80 0.620 153 80 0.523
   FSW(G+K) 117 72 0.615 153 83 0.542
   Consol3.19 122 82 0.672 145 82 0.566
   Boot0.094 199 79 0.397 172 88 0.512
MIPS MCL G+K 242 35 0.143 177 40 0.226
   ICD(G+K) 136 47 0.346 151 60 0.397
   FSW(G+K) 120 46 0.383 151 61 0.404
   Consol3.19 116 48 0.414 157 63 0.401
   Boot0.094 203 44 0.271 168 56 0.333
  MCL-CAw G+K 310 53 0.171 177 53 0.300
   ICD(G+K) 129 63 0.488 151 63 0.417
   FSW(G+K) 117 48 0.410 151 66 0.437
   Consol3.19 122 68 0.557 157 68 0.433
   Boot0.094 199 47 0.236 168 59 0.351
Aloy MCL G+K 242 43 0.179 76 42 0.556
   ICD(G+K) 136 58 0.426 75 56 0.747
   FSW(G+K) 120 57 0.475 75 57 0.760
   Consol3.19 116 54 0.466 76 55 0.724
   Boot0.094 203 56 0.276 76 55 0.724
  MCL-CAw G+K 310 52 0.168 76 52 0.684
   ICD(G+K) 129 59 0.457 75 59 0.787
   FSW(G+K) 117 60 0.513 75 60 0.800
   Consol3.19 122 57 0.467 76 57 0.750
   Boot0.094 199 57 0.286 76 58 0.763
  1. Affinity scoring of PPI networks improved the performance of MCL and MCL-CAw. Affinity scoring followed by CA refinement had a compounded effect in improving the performance of MCL.