Predicting RNA-binding sites of proteins using support vector machines and evolutionary information

BMC Bioinformatics

Table 5 Performance comparison of different approaches using five-fold cross-validation for the benchmark data sets.

Data set	Method	Spec. (%)	Sens. (%)	Acc (%)	MCC	Threshold
RBP86	Jeong 2006	91.04	43.4	80.2	0.39 (0.41)*	--
	PPRint	89.55	53.05	81.16	0.45	--
	RNAProB ^§	90.36	79.95	87.99	0.68	0.36
	RNAProB ^#	90.01	79.64	87.65	0.67	0.36
RBP109	RNABindR	93.00	38.00	84.80	0.35	--
	RNAProB ^§	93.88	64.62	89.70	0.58	0.35
	RNAProB ^#	94.14	60.63	89.36	0.56	0.35
RBP107	BindN-PCP^&	69.84	66.28	69.32	0.27	--
	BindN-ALL^&	75.70	65.78	74.25	--	--
	PPRint	75.54	70.09	75.43	0.32	--
	RNAProB ^§	80.87	77.14	80.44	0.42	0.11
	RNAProB ^#	80.65	73.62	79.84	0.40	0.12

^§ presents the performance by five-fold cross-validation.
^# denotes the performance by a three-way data split procedure.
* indicates the performance of weighted profiles by Jeong and Miyano [7].
^&BindN-PCP represents the results based only on physicochemical properties, while BindN-ALL shows the performance using physicochemical properties, relative solvent accessible surface area, and BLAST results.

ISSN: 1471-2105