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Table 18 Combining position specific composition (Method 1) and thermodynamics (Method 2) for training RBF-epsilon regression SVM model

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

   train579 train579 train579
   test579 test2431 test579 10 × cross val
t- test FN579 R MSE R MSE R MSE
0 107(107) 0.874 0.026 0.493 0.083 0.533 0.083
1 62(61.8) 0.889 0.023 0.486 0.083 0.521 0.085
2 33(32.1) 0.856 0.028 0.466 0.069 0.537 0.083
3 16(15.3) 0.816 0.034 0.388 0.087 0.559 0.081
4 12(10.7) 0.796 0.036 0.412 0.072 0.527 0.084
5 7(6.2) 0.537 0.069 0.288 0.051 0.497 0.087
6 5(4.8) 0.525 0.071 0.442 0.052 0.424 0.094
7 3(1.6) 0.374 0.086 0.435 0.050 0.405 0.095
8 1(0.6) 0.339 0.089 0.421 0.051 - -
9 0(0) - - - - - -
  1. Models trained on dataset579 and testing performed with dataset579, dataset2431 and 10 × cross validation on dataset579, features removed by increasing stringency of t-test of individual feature to activity from dataset579.
  2. Feature numbers in parentheses are the average number of features in cross validations.
  3. Entries in bold are column maximums and are simply visual landmarks.
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