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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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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C729-F Versions-Permalink: https://hdl.handle.net/21.11116/0000-0010-5A54-B
Genre: Konferenzbeitrag

Externe Referenzen

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externe Referenz:
https://link.springer.com/content/pdf/10.1007%2F978-3-540-87361-7_16.pdf (Verlagsversion)
Beschreibung:
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OA-Status:
Keine Angabe

Urheber

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 Urheber:
Ong, CS1, Autor           
Zien, A, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

Inhalt

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Schlagwörter: -
 Zusammenfassung: 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.

Details

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Sprache(n):
 Datum: 2008-09
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1007/978-3-540-87361-7_16
 Art des Abschluß: -

Veranstaltung

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Titel: 8th Workshop on Algorithms in Bioinformatics (WABI 2008)
Veranstaltungsort: Karlsruhe, Germany
Start-/Enddatum: 2008-09-15 - 2008-09-19

Entscheidung

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Projektinformation

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Quelle 1

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Titel: Algorithms in Bioinformatics: 8th International Workshop, WABI 2008, Karlsruhe, Germany, September 15-19, 2008
Genre der Quelle: Konferenzband
 Urheber:
Krandall, KA, Herausgeber
Lagergren, J, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Berlin, Germany : Springer
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 186 - 197 Identifikator: ISBN: 978-3-540-87360-0

Quelle 2

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Titel: Lecture Notes in Computer Science
Genre der Quelle: Reihe
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 5251 Artikelnummer: - Start- / Endseite: - Identifikator: -