Table 5.
Residue-wise Contact Order Estimation Performance
| w | f = 1 | f = 3 | f = 5 | f = 7 | f = 9 | f = 11 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CC | RMSE | CC | RMSE | CC | RMSE | CC | RMSE | CC | RMSE | CC | RMSE | ||
 |
3 | 0.704 | 0.696 | 0.708 | 0.692 | - | - | - | - | - | - | - | - |
| 7 | 0.712 | 0.683 | 0.719 | 0.677 | 0.723 | 0.672 | 0.722 | 0.672 | - | - | - | - | |
| 11 | 0.711 | 0.681 | 0.720 | 0.673 | 0.725 | 0.667 | 0.725 | 0.666 | 0.724 | 0.666 | 0.722 | 0.667 | |
| 15 | 0.709 | 0.680 | 0.719 | 0.672 | 0.726** | 0.665 | 0.726 | 0.664 | 0.725 | 0.664 | 0.723 | 0.664 | |
CC and RMSE denotes the average correlation coefficient and RMSE values. The numbers in bold show the best models as measured by CC for a fixed w parameter. 
, and 
represent the PSI-BLAST profile and YASSPP scoring matrices, respectively. soe, rbf, and lin represent the three different kernels studied using the 
as the base kernel. * denotes the best regression results in the sub-tables, and ** denotes the best regression results achieved on this dataset. For the best results the se rate for the CC values is 0.003. The published results [15] uses the default rbf kernel to give CC = 0.600 and RMSE = 0.78.