An Automated Combination of Sequence Motif Kernels for Protein Subcellular 
Localization

Zien, A; Ong, CS

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An Automated Combination of Sequence Motif Kernels for Protein Subcellular Localization

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Zien, A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Ong, CS
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Friedrich Miescher Laboratory, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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http://www.iscb.org/cms_addon/conferences/ismb2006/archive/ismb2006.cbi.cnptia.embrapa.br/posters_list.php
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Citation

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.

Cite as: https://hdl.handle.net/21.11116/0000-0004-B620-A

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.