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

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

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

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.

ISSN: 1471-2105