BMC Bioinformatics

Table 2 Feature mapping method performance in RBF-epsilon regression SVM modeling, alternatively training and testing between dataset₂₄₃₁ and datatset₅₇₉ and 10 × cross validation within dataset₂₄₃₁ or datatset₅₇₉

From: Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

		train₂₄₃₁				train₅₇₉
		test₅₇₉		test₂₄₃₁ 10 × cross validation		test₂₄₃₁		test₅₇₉ 10 × cross validation
Method	FN₂₄₃₁ FN₅₇₉	R	MSE	R	MSE	R	MSE	R	MSE
1-	84	0.510	0.095	0.711	0.026	0.485	0.054	0.562	0.079
2-	23	0.379	0.105	0.640	0.029	0.367	0.069	0.500	0.087
3-	23	0.130	0.115	0.094	0.046	0.017^†	0.138	0.026^†	0.118
4-	24	0.202	0.115	0.293	0.041	0.214	0.073	0.214	0.041
5-	32	0.214	0.112	0.243	0.042	0.164	0.046	0.194	0.107
11-	1360	0.247	0.109	0.559	0.033	0.192	0.055	0.469	0.088
13-	43	0.045^†	0.111	0.277	0.042	0.071	0.104	0.262	0.105
14-	22	0.022^†	0.107	0.272	0.045	0.020^†	0.067	0.182	0.118

Method numbers are from Table 1.
FN_dataset = Feature Number count from dataset
R = Pearson correlation coefficient, values with a ^† are not able to reject the H_O: R = 0.
MSE = mean squared error

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ISSN: 1471-2105

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