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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.
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Contributor : Rapport de Recherche Inria <>
Submitted on : Friday, May 19, 2006 - 8:10:33 PM
Last modification on : Friday, July 10, 2020 - 4:20:29 PM
Long-term archiving on: : Sunday, April 4, 2010 - 8:59:32 PM


  • 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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