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A Kernel for Protein Secondary Structure Prediction

Yann Guermeur 1 Alain Lifchitz 2 Régis Vert 3 
1 MODBIO - Computational models in molecular biology
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
2 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
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.
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Contributor : Alain Lifchitz Connect in order to contact the contributor
Submitted on : Sunday, December 11, 2005 - 10:18:16 PM
Last modification on : Sunday, June 26, 2022 - 11:45:28 AM
Long-term archiving on: : Monday, September 20, 2010 - 1:29:17 PM


  • HAL Id : hal-00012701, version 2


Yann Guermeur, Alain Lifchitz, Régis Vert. A Kernel for Protein Secondary Structure Prediction. Bernhard Schölkopf, Koji Tsuda, Jean-Philippe Vert. Kernel Methods in Computational Biology, The MIT Press, Cambridge, Massachussetts, pp.193-206, 2004, 0-262-19509-7. ⟨hal-00012701v2⟩



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