English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

An Automated Combination of Kernels for Predicting Protein Subcellular Localization

MPS-Authors
/persons/resource/persons84118

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

/persons/resource/persons84331

Zien,  A
Friedrich Miescher Laboratory, Max Planck Society;

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C729-F
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.