InforLorV4, Hal, Corpus, bibRecord, 000270

A Kernel for Protein Secondary Structure Prediction

Identifieur interne : 000270 ( Hal/Corpus ); précédent : 000269; suivant : 000271

A Kernel for Protein Secondary Structure Prediction

Auteurs : Yann Guermeur ; Alain Lifchitz ; Régis Vert

Source :

RBID : Hal:hal-00012701

English descriptors

mix :
- Bioinformatics, Kernel Methods, Multiclass SVM, Optimal Segmentation, Protein Secondary Structure.

Abstract

Multi-class support vector machines have already proved efficient in protein secondary structure prediction as ensemble methods, to combine the outputs of sets of classifiers based on different principles. In this chapter, their implementation as basic prediction methods, processing the primary structure or the profile of multiple alignments, is investigated. A kernel devoted to the task is introduced, which incorporates high-level pieces of knowledge. Initial experimental results illustrate the potential of this approach.

Url:

https://hal.archives-ouvertes.fr/hal-00012701

Links to Exploration step

Hal:hal-00012701

Le document en format XML

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<front><div type="abstract" xml:lang="en">Multi-class support vector machines have already proved efficient in protein secondary structure prediction as ensemble methods, to combine the outputs of sets of classifiers based on different principles. In this chapter, their implementation as basic prediction methods, processing the primary structure or the profile of multiple alignments, is investigated. A kernel devoted to the task is introduced, which incorporates high-level pieces of knowledge. Initial experimental results illustrate the potential of this approach.</div>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">A Kernel for Protein Secondary Structure Prediction</title>
<author role="aut"><persName><forename type="first">Yann</forename>
<surname>Guermeur</surname>
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<email>Yann.Guermeur@loria.fr</email>
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<author role="aut"><persName><forename type="first">Alain</forename>
<surname>Lifchitz</surname>
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<email>alain.lifchitz@lip6.fr</email>
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<author role="aut"><persName><forename type="first">Régis</forename>
<surname>Vert</surname>
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<email>Regis.Vert@lri.fr</email>
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<monogr><title level="m">Kernel Methods in Computational Biology</title>
<editor>Bernhard Schölkopf, Koji Tsuda, Jean-Philippe Vert</editor>
<imprint><publisher>The MIT Press, Cambridge, Massachussetts</publisher>
<biblScope unit="serie">Chap. 9 - ISBN 0-262-19509-7</biblScope>
<biblScope unit="pp">193-206</biblScope>
<date type="datePub">2004</date>
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<textClass><keywords scheme="author"><term xml:lang="en">Bioinformatics</term>
<term xml:lang="en">Kernel Methods</term>
<term xml:lang="en">Multiclass SVM</term>
<term xml:lang="en">Optimal Segmentation</term>
<term xml:lang="en">Protein Secondary Structure</term>
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<abstract xml:lang="en">Multi-class support vector machines have already proved efficient in protein secondary structure prediction as ensemble methods, to combine the outputs of sets of classifiers based on different principles. In this chapter, their implementation as basic prediction methods, processing the primary structure or the profile of multiple alignments, is investigated. A kernel devoted to the task is introduced, which incorporates high-level pieces of knowledge. Initial experimental results illustrate the potential of this approach.</abstract>
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A Kernel for Protein Secondary Structure Prediction

A Kernel for Protein Secondary Structure Prediction

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