Poodle
Poodle
Poodle is a set of tools for the prediction of the secondary structures. The different programs uses different machine learning approaches.
Poodle-L
| Author | Hirose S, Shimizu K, Kanai S, Kuroda Y, Noguchi T. |
|---|---|
| Year | 2007 |
| Reference | PubMed 17545177 |
| ML Method | two levels of SVMs |
Poodle-L is specialized on the prediction of long disordered regions (> 40 residues). It uses two SVMs. Each amino acid is represented by ten different physikochemical properties (10 dimensions). The first SVM predicts the probability of a 40-residue segment to be disordered. This output is used by the second SVM to predict predict the probability of a single residue to be disordered.
Poodle-S
| Author | Shimizu K, Hirose S, Noguchi T. |
|---|---|
| Year | 2007 |
| Reference | PubMed 17599940 |
Poodle-S is specialized on the prediction of short disordered regions. This method uses physicochemical features and a reduced amino acid set of a position-specific scoring matrix.
Poodle-W
| Author | Shimizu K, Muraoka Y, Hirose S, Tomii K, Noguchi T. |
|---|---|
| Year | 2007 |
| Reference | PubMed 17338828 |
Poodle-W predicts which protein of a set of proteins is the most disordered one.
Poodle-I
| Author | Hirose S, Shimizu K, Inoue N, Kanai S, Noguchi T. |
|---|---|
| Year | 2008 |
| Reference | CASP 8 Proceedings |
Poodle-I seems to predict the disorder of a protein by combining different tools in a workflow.
