HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks

Table 1

Comparison with MCODE. P, R, and F stand for precision, recall, and F-measure, respectively, and their definitions are given in Section 3.1. MIPS data set contains 4,554 proteins and 12,526 interactions, and SGD-MC data set contains 4,448 proteins and 29,068 interactions. AC is the number of all clusters predicted by the algorithm; EC is the number of effective clusters (with a least one matching complex above overlap ratio 0.4) found by the algorithm; MC is the number of matched complexes in the benchmark set. The sizes of complexcat benchmark and Gavin benchmark are 217 and 204, respectively. For HKC the optimized parameters are 𝑇₁, 𝑇₂, and 𝑇₃, respectively, and for MCODE the optimized parameters are NodeScoreCutoff, fluff (T for true, F for false), haircut (T for true, F for false), and other unspecified parameters adopt the default values.

Algorithm	Data set	Benchmark	P	R	F	AC	EC	MC	Optimized parameters
MCODE	MIPS		0.455	0.194	0.271	66	30	42	0.05, F, F
HKC		complexcat	0.380	0.429	0.403	237	90	93	0.6, 10, 0.5
MCODE	SGD-MC		0.213	0.221	0.217	197	42	48	0.05, F, T
HKC			0.275	0.580	0.373	498	137	126	0.6, 10, 0.8
MCODE	MIPS		0.303	0.098	0.148	66	20	20	0.05, F, T
HKC		Gavin	0.237	0.235	0.236	245	58	48	0.6, 20, 0.5
MCODE	SGD- MC		0.283	0.152	0.198	106	30	31	0, F, T
HKC			0.271	0.402	0.324	487	132	82	0.5, 5, 0.5