A Similar Fragments Merging Approach to Learn Automata on Proteins

François Coste 1 Goulven Kerbellec 1
1 SYMBIOSE - Biological systems and models, bioinformatics and sequences
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : We propose here to learn automata for the characterization of proteins families to overcome the limitations of the position-specific characterizations classically used in Pattern Discovery. We introduce a new heuristic approach learning non-deterministic automata based on selection and ordering of significantly similar fragments to be merged and on physico-chemical properties identification. Quality of the characterization of the major intrinsic protein (MIP) family is assessed by leave-one-out cross-validation for a large range of models specificity.
Type de document :
[Research Report] RR-5672, INRIA. 2005, pp.17
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Contributeur : Rapport de Recherche Inria <>
Soumis le : vendredi 19 mai 2006 - 20:10:33
Dernière modification le : vendredi 16 novembre 2018 - 01:21:57
Document(s) archivé(s) le : dimanche 4 avril 2010 - 20:59:32



  • HAL Id : inria-00070340, version 1


François Coste, Goulven Kerbellec. A Similar Fragments Merging Approach to Learn Automata on Proteins. [Research Report] RR-5672, INRIA. 2005, pp.17. 〈inria-00070340〉



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