Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC

Table 2

The performance comparison of several typical algorithms on four datasets.

Dataset	Algorithms	SN	SP	-measure	PC	MPC	MSC	Perfect	AS
DIP	MCODE	0.2318	0.6182	0.3372	165	102	70	6	6.7212
	MCL	0.7031	0.2505	0.3694	1541	386	245	14	4.4361
	CORE	0.7381	0.2769	0.4027	1517	420	259	39	2.443
	CSO	0.4403	0.6257	0.5169	342	214	136	11	4.652
	ClusterONE	0.6093	0.3385	0.4352	972	329	197	15	3.5422
	COACH	0.5009	0.5591	0.5284	474	265	144	13	4.9789
	ICSC	0.8385	0.4186	0.5585	1997	836	247	64	3.5613
Krogan	MCODE	0.2749	0.7937	0.4084	160	127	73	10	5.125
	MCL	0.566	0.4559	0.5051	658	300	178	40	3.9544
	CORE	0.5417	0.4121	0.4681	677	279	172	39	2.6041
	CSO	0.3284	0.8254	0.4699	189	156	89	10	5.2646
	ClusterONE	0.5232	0.4632	0.4914	585	271	161	28	3.935
	COACH	0.3566	0.81	0.4952	221	179	85	11	5.3575
	ICSC	0.6314	0.5966	0.6135	761	454	143	23	3.3338
MIPS	MCODE	0.1714	0.5333	0.2595	135	72	60	4	5.437
	MCL	0.5451	0.2017	0.2945	1259	254	196	17	4.7434
	CORE	0.6235	0.249	0.3558	1217	303	225	29	2.5859
	CSO	0.2835	0.5163	0.366	246	127	87	6	4.5528
	ClusterONE	0.4483	0.2796	0.3444	744	208	152	17	3.1317
	COACH	0.3145	0.3662	0.3384	396	145	92	5	6.5253
	ICSC	0.7181	0.3028	0.4260	1691	512	207	50	3.7534
Gavin	MCODE	0.2612	0.7548	0.3881	155	117	77	6	5.3484
	MCL	0.4411	0.6417	0.5228	321	206	147	25	5.0312
	CORE	0.4336	0.5735	0.4938	347	199	148	26	2.8184
	CSO	0.3109	0.773	0.4434	185	143	91	6	5.9405
	ClusterONE	0.4797	0.6413	0.5488	368	236	152	19	5.2826
	COACH	0.3477	0.6966	0.4585	234	163	94	5	6.312
	ICSC	0.5033	0.5704	0.5347	540	308	104	10	3.6093