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Table 1 Feature mapping methods performance in RBF-epsilon regression SVM model training and testing within dataset2431

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

     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
  1. 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.