Difference between revisions of "Jpred"
From Bioinformatikpedia
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| + | Jnet uses two neural networks for its prediction. |
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| − | <<under construction>> |
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| + | The first network is feeded with a window of 17 residues over each amino acid in the alignment plus a conservation number. |
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| + | It uses a hidden layer of nine nodes and has three output nodes, one for each secondary structure element. |
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| + | The second network is feeded with a window of 19 residues (the result of first network) plus the conservation number. |
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| − | Jnet uses neural networks for its prediction. |
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| + | It has a hidden layer with nine nodes and has three output nodes. |
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| − | first, input: window of 17 residues over each amino acid in the alignment plus the |
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| − | addition of a conservation number; nine hidden nodes; three output nodes |
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| − | |||
| − | second, input: window of 19 residues (result of first network) plus the conservation |
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| − | number; nine hidden nodes; three output nodes |
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| − | |||
| − | input: multiple sequence alignment |
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| − | output: for each amino-acid three scores for three secondary structure elements |
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| − | (Helix, Sheet, Coil) |
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Revision as of 22:21, 6 June 2011
Jpred
Basic Information
Jpred itself is just a Protein Secondary Structure Prediction server, which exists since 1998 in different versions. JPred predicts Solvent Accessibility and Coiled-coil regions with the Lupas method and the secondary structure with the Jnet algorithm.
Jnet
| Jnet | |
| Author | James A. Cuff, Geoffrey J. Barton |
|---|---|
| Year | 2000 |
| Reference | PubMed 10861942 |
| ML Method | Neural Network |
Jnet uses two neural networks for its prediction. The first network is feeded with a window of 17 residues over each amino acid in the alignment plus a conservation number. It uses a hidden layer of nine nodes and has three output nodes, one for each secondary structure element.
The second network is feeded with a window of 19 residues (the result of first network) plus the conservation number. It has a hidden layer with nine nodes and has three output nodes.
