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Table 1 Average prediction performance of the Rounds 1 and 2 of our method on test sets of the 10-fold CV.

From: Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels

Test Performance of Round 1: WD with s = 1 & m = 1

d

Runtime

RMSE

Pearson Cor

Spearman Cor

2

572

20.06

0.74

0.46

3

1034

19.99

0.74

0.47

4

1448

19.87

0.75

0.48

5

1834

19.79

0.75

0.49

6

2221

19.77

0.75

0.49

7

2430

19.76

0.75

0.50

8

2908

19.75

0.75

0.50

9

3193

19.74

0.75

0.50

Test Performance of Round 2: WD with s = 0 & m = 0

d

Runtime

RMSE

Pearson Cor

Spearman Cor

2

47

18.82

0.78

0.55

3

90

18.09

0.80

0.59

4

128

17.65

0.81

0.62

5

166

17.34

0.82

0.65

6

200

17.09

0.83

0.66

7

235

16.89

0.84

0.68

8

268

16.89

0.84

0.65

9

302

16.87

0.84

0.65

  1. Round 1 uses all k-mers up to length d, with shift = 1 and mismatch = 1. Round 2 uses only selected k-mers from Round 1, with shift = 0 and mismatch = 0. 'Runtime' includes both training and testing, in seconds. The values for the parameters selected on training data are in bold.