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  An Automated Combination of Kernels for Predicting Protein Subcellular Localization

Ong, C., & Zien, A. (2008). An Automated Combination of Kernels for Predicting Protein Subcellular Localization. In K. Krandall, & J. Lagergren (Eds.), Algorithms in Bioinformatics: 8th International Workshop, WABI 2008, Karlsruhe, Germany, September 15-19, 2008 (pp. 186-197). Berlin, Germany: Springer.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C729-F Version Permalink: https://hdl.handle.net/21.11116/0000-0003-37FD-3
Genre: Conference Paper

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https://link.springer.com/content/pdf/10.1007%2F978-3-540-87361-7_16.pdf (Publisher version)
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 Creators:
Ong, CS1, 2, 3, Author           
Zien, A3, 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, Max-Planck-Ring 9, 72076 Tübingen, DE, ou_2575692              

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 Abstract: Protein subcellular localization is a crucial ingredient to many important
inferences about cellular processes, including prediction of protein function
and protein interactions. While many predictive computational tools have been
proposed, they tend to have complicated architectures and require many design
decisions from the developer.
Here we utilize the multiclass support vector machine (m-SVM) method to directly
solve protein subcellular localization without resorting to the common approach
of splitting the problem into several binary classification problems. We
further propose a general class of protein sequence kernels which considers all
motifs, including motifs with gaps. Instead of heuristically selecting one or a few
kernels from this family, we utilize a recent extension of SVMs that optimizes
over multiple kernels simultaneously. This way, we automatically search over
families of possible amino acid motifs.
We compare our automated approach to three other predictors on four different
datasets, and show that we perform better than the current state of the art. Further, our method provides some insights as to which sequence motifs are most useful for determining subcellular ocalization, which are in agreement with biological
reasoning.

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 Dates: 2008-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-540-87361-7_16
BibTex Citekey: 5256
 Degree: -

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Title: 8th Workshop on Algorithms in Bioinformatics (WABI 2008)
Place of Event: Karlsruhe, Germany
Start-/End Date: 2008-09-15 - 2008-09-19

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Source 1

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Title: Algorithms in Bioinformatics: 8th International Workshop, WABI 2008, Karlsruhe, Germany, September 15-19, 2008
Source Genre: Proceedings
 Creator(s):
Krandall, KA, Editor
Lagergren, J, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 186 - 197 Identifier: ISBN: 978-3-540-87360-0

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 5251 Sequence Number: - Start / End Page: - Identifier: -