Large-Scale Protein-Protein Interactions Detection by Integrating Big Biosensing Data with Computational Model

Table 2

Comparison of the prediction performance by the proposed method and state-of-the-art SVM classifier on the human dataset.

Method	Kernel	Mean/std	Time (s)	ACC	SN	SP	PPV	NPV	F1	MCC	AUC
			Testing
ELM	Sigmoid	Mean	72.7901	0.8480	0.8408	0.8553	0.8547	0.8415	0.8477	0.7422	0.9232
		Variance	1.9062	0.0022	0.0019	0.0028	0.0040	0.0038	0.0029	0.0030	0.0028
	Hardlim	Mean	77.4139	0.8206	0.8171	0.8242	0.8227	0.8185	0.8199	0.7056	0.9020
		Variance	3.7710	0.0050	0.0040	0.0063	0.0088	0.0026	0.0063	0.0064	0.0031
	Gaussian	Mean	76.9615	0.7257	0.7328	0.7186	0.7232	0.7283	0.7279	0.6018	0.7624
		Variance	4.1012	0.0036	0.0048	0.0054	0.0085	0.0077	0.0044	0.0033	0.0017
			Training
ELM	Sigmoid	Mean	1282.12	0.8887	0.8831	0.8944	0.8933	0.8843	0.8882	0.8022	0.9561
		Variance	17.25	0.0006	0.0010	0.0018	0.0014	0.0001	0.0008	0.0010	0.0012
	Hardlim	Mean	1330.33	0.8668	0.8655	0.8682	0.8683	0.8654	0.8669	0.7691	0.9397
		Variance	46.28	0.0027	0.0021	0.0033	0.0027	0.0027	0.0024	0.0039	0.0031
	Gaussian	Mean	1435.45	0.7824	0.7896	0.7753	0.7790	0.7860	0.7843	0.6595	0.8626
		Variance	94.85	0.0033	0.0022	0.0053	0.0040	0.0026	0.0029	0.0037	0.0038
			Testing
SVM	Sigmoid	Mean	2794.29	0.8177	0.8119	0.8232	0.8215	0.8144	0.8165	0.7018	0.8878
		Variance	16.71	0.0127	0.0266	0.0128	0.0067	0.0200	0.0155	0.0160	0.0143
	Gaussian	Mean	5237.89	0.6947	0.4714	0.9191	0.8535	0.6348	0.6064	0.5320	0.8997
		Variance	67.82	0.0228	0.0412	0.0112	0.0178	0.0265	0.0340	0.0276	0.0364
	Polynomial	Mean	3612.98	0.8019	0.8219	0.7819	0.7903	0.8144	0.8057	0.6820	0.8838
		Variance	20.16	0.0101	0.0126	0.0117	0.0165	0.0114	0.0125	0.0122	0.0138