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Table 5 Residue-wise Contact Order Estimation Performance

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

  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
  1. 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.
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BMC Bioinformatics

ISSN: 1471-2105

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