Table 6 Classification Performance on the Protein-DNA Interaction Site Prediction.
From: svm PRAT: SVM-based Protein Residue Annotation Toolkit
| Â | w= f= 3 | w= f= 7 | w= f= 11 | |||
|---|---|---|---|---|---|---|
| Â | ROC | F1 | ROC | F1 | ROC | F1 |
| 0.756 | 0.463 | 0.758* | 0.469 | 0.748 | 0.452 |
| 0.753 | 0.465 | 0.754 | 0.462 | 0.759 | 0.466 |
| 0.754 | 0.466 | 0.756 | 0.468 | 0.763 | 0.468 |
- The numbers in bold show the best models for a fixed w parameter, as measured by ROC.
, 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 classification results in the sub-tables, and ** denotes the best classification results achieved on this dataset. For the best model we report a Q2 accuracy of 83.0% with an se rate of 0.34.
, 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 classification results in the sub-tables, and ** denotes the best classification results achieved on this dataset. For the best model we report a Q2 accuracy of 83.0% with an se rate of 0.34.