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Table 2 Classification Performance on the Disorder Dataset.

From: svm PRAT: SVM-based Protein Residue Annotation Toolkit

  w f= 1 f= 3 f= 5 f= 7 f= 9 f= 11
   ROC F1 ROC F1 ROC F1 ROC F1 ROC F1 ROC F1
3 0.775 0.312 0.800 0.350 - - - - - - - -
  7 0.815 0.366 0.817 0.380 0.816 0.384 0.816 0.383 - - - -
  11 0.821 0.378 0.826 0.391 0.828 0.396 0.826 0.400 0.824 0.404 0.823 0.403
  13 0.823 0.384 0.829 0.398 0.832* 0.405 0.830 0.404 0.828 0.407 0.826 0.409
3 0.811 0.370 0.811 0.369 - - - - - - - -
  7 0.845 0.442 0.849 0.450 0.848 0.445 0.845 0.442 - - - -
  11 0.848 0.464 0.855 0.478 0.858 0.482 0.858 0.480 0.855 0.470 0.853 0.468
  13 0.848 0.473 0.855 0.484 0.859 0.490 0.861* 0.492 0.860 0.487 0.857 0.478
3 0.815 0.377 0.816 0.379 - - - - - - - -
  7 0.847 0.446 0.852 0.461 0.852 0.454 0.851 0.454 - - - -
  11 0.848 0.469 0.856 0.482 0.860 0.491 0.862 0.491 0.861 0.485 0.862 0.485
  13 0.847 0.473 0.856 0.485 0.861 0.491 0.864 0.495 0.865* 0.494 0.864 0.492
3 0.836 0.418 0.838 0.423 - - - - - - - -
  7 0.860 0.472 0.862 0.476 0.860 0.473 0.859 0.468 - - - -
  11 0.861 0.490 0.867 0.496 0.868 0.498 0.868 0.495 0.866 0.488 0.865 0.485
  13 0.860 0.497 0.867 0.503 0.870 0.503 0.871* 0.503 0.870 0.498 0.868 0.492
3 0.842 0.428 0.841 0.428 - - - - - - - -
  7 0.869 0.497 0.870 0.499 0.869 0.494 0.867 0.489 - - - -
  11 0.871 0.516 0.875 0.518 0.877 0.517 0.877 0.512 0.874 0.508 0.873 0.507
  13 0.869 0.519 0.875 0.522 0.878 0.521 0.879** 0.519 0.879 0.518 0.876 0.514
  1. DISPro [7] reports a ROC score of 0.878. The numbers in bold show the best models for a fixed w parameter, as measured by ROC. , , and represent the PSI-BLAST profile, BLOSUM62, and YASSPP scoring matrices, respectively. soe, rbf, and lin represent the three different kernels studied using the Ww, fas 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 84.60% with an se rate of 0.33.