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  An Automated Combination of Sequence Motif Kernels for Protein Subcellular Localization

Zien, A., & Ong, C. (2006). An Automated Combination of Sequence Motif Kernels for Protein Subcellular Localization. Poster presented at 14th International Conference on Intelligent Systems for Molecular Biology (ISMB 2006), Fortaleza, Brazil.

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 Creators:
Zien, A1, 2, Author              
Ong, CS1, 2, 3, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Friedrich Miescher Laboratory, Max Planck Society, ou_2575692              

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 Abstract: We propose an elegant multiclass prediction approach for protein subcellular localization. First we define a family of protein sequence kernels which consider variable length motifs with gaps. Second, we generalize the multiclass SVM to automatically optimize over multiple kernels. We compare to other subcellular localization predictors on different protein datasets.

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 Dates: 2006-08
 Publication Status: Published online
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Title: 14th International Conference on Intelligent Systems for Molecular Biology (ISMB 2006)
Place of Event: Fortaleza, Brazil
Start-/End Date: 2006-08-06 - 2006-08-10

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Title: 14th International Conference on Intelligent Systems for Molecular Biology (ISMB 2006)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: H-58 Start / End Page: - Identifier: -