Table 4 Disorder Prediction Performance at CASP8.
From: svm PRAT: SVM-based Protein Residue Annotation Toolkit
| Method | ROC | Q_2 | S w |
|---|---|---|---|
| MULTICOM | 0.92 | 0.81 | 0.61 |
| CBRC-DP_DR | 0.91 | 0.81 | 0.62 |
| GS-MetaServer2 | 0.91 | 0.83 | 0.66 |
| McGuffin | 0.91 | 0.82 | 0.64 |
| DISOclust | 0.91 | 0.82 | 0.64 |
| GeneSilicoMeta | 0.90 | 0.83 | 0.655 |
| Poodle | 0.90 | 0.80 | 0.61 |
| CaspIta | 0.89 | 0.78 | 0.571 |
| fais-server | 0.89 | 0.78 | 0.56 |
| MULTICOM-CMFR | 0.89 | 0.82 | 0.64 |
| MARINER* | 0.88 | 0.80 | 0.61 |
- * - MARINER used svm PRAT to train models for disorder prediction in participation at CASP8 using the
kernel with w = f = 11. We used the 723 sequences with disordered residues from the DisPro [7] dataset. The results are the official results from the CASP organizers and were presented by Dr. Joel Sussman at the Weizmann Institute of Science. Q2 denotes the 2-state accuracy for the prediction and S
w
is a weighted accuracy rewarding the prediction of disordered residue.
kernel with w = f = 11. We used the 723 sequences with disordered residues from the DisPro [