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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.