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  Support vector machines for protein fold class prediction

Markowetz, F., Edler, L., & Vingron, M. (2003). Support vector machines for protein fold class prediction. Biometrical Journal, 45(3), 377-389. doi:10.1002/bimj.200390019.

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Genre: Zeitschriftenartikel
Alternativer Titel : Biom. J.

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 Urheber:
Markowetz, Florian1, Autor
Edler, Lutz, Autor
Vingron, Martin2, Autor           
Affiliations:
1Max Planck Society, ou_persistent13              
2Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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Schlagwörter: protein fold class prediction, support vector machines, statistical classification methods, neural networks, confusion matrix
 Zusammenfassung: Knowledge of the three-dimensional structure of a protein is essential for describing and understanding its function. Today, a large number of known protein sequences faces a small number of identified structures. Thus, the need arises to predict structure from sequence without using time-consuming experimental identification. In this paper the performance of Support Vector Machines (SVMs) is compared to Neural Networks and to standard statistical classification methods as Discriminant Analysis and Nearest Neighbor Classification. We show that SVMs can beat the competing methods on a dataset of 268 protein sequences to be classified into a set of 42 fold classes. We discuss misclassification with respect to biological function and similarity. In a second step we examine the performance of SVMs if the embedding is varied from frequencies of single amino acids to frequencies of tripletts of amino acids. This work shows that SVMs provide a promising alternative to standard statistical classification and prediction methods in functional genomics.

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Sprache(n): eng - English
 Datum: 2003
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: eDoc: 175056
ISI: 000182686400009
DOI: 10.1002/bimj.200390019
 Art des Abschluß: -

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Titel: Biometrical Journal
  Alternativer Titel : Biom. J.
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 45 (3) Artikelnummer: - Start- / Endseite: 377 - 389 Identifikator: ISSN: 0323-3847