A Similar Fragments Merging Approach to Learn Automata on Proteins

  • François Coste
  • Goulven Kerbellec
Conference paper

DOI: 10.1007/11564096_50

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3720)
Cite this paper as:
Coste F., Kerbellec G. (2005) A Similar Fragments Merging Approach to Learn Automata on Proteins. In: Gama J., Camacho R., Brazdil P.B., Jorge A.M., Torgo L. (eds) Machine Learning: ECML 2005. ECML 2005. Lecture Notes in Computer Science, vol 3720. Springer, Berlin, Heidelberg

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • François Coste
    • 1
  • Goulven Kerbellec
    • 1
  1. 1.Symbiose, IRISARennes CedexFrance

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