Table 1

Feature mapping methods performance in RBF-epsilon regression SVM model training and testing within dataset2431

train2431

test2431 10 × cross validation


Feature mapping method

FN2431

R

MSE

R

MSE


1-Position specific base

84

0.784

0.016

0.711

0.026

2-Thermodynamics

23

0.915

0.007

0.640

0.029

3-Entropy

23

0.730

0.021

0.094

0.046

4-Guide strand structure

24

0.430

0.033

0.293

0.041

5-Guide strand features

32

0.266

0.037

0.243

0.042

6-N-Grams N = 2

16

0.408

0.033

0.291

0.041

7-N-Grams N = 3

64

0.656

0.024

0.435

0.037

8-N-Grams N = 4

256

0.590

0.027

0.532

0.034

9-N-Grams N = 5

1024*

0.590

0.029

0.487

0.036

10-N-Grams N = 6

4096*

0.621

0.036

0.439

0.036

11-N-Grams N = 2–5

1360*

0.614

0.026

0.559

0.033

12-Target strand structure-nondirectional

22

0.646

0.024

0.257

0.045

13-Target strand structure-directional

43

0.607

0.025

0.277

0.042

14-Target imprecise thermo

22

0.932

0.007

0.272

0.045


FNdataset = Feature Number count from dataset, R = Pearson correlation coefficient, MSE = mean squared error * Theoretical, but five 5-grams and several 6-grams are absent in the present dataset, reducing the effective feature set size.

Peek BMC Bioinformatics 2007 8:182   doi:10.1186/1471-2105-8-182

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