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

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

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

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.

ISSN: 1471-2105