A similar fragments merging approach to learn automata on proteins

Authors:
François Coste;Goulven Kerbellec
Affiliations:
Symbiose, IRISA, Rennes Cedex, France;Symbiose, IRISA, Rennes Cedex, France
Venue:
ECML'05 Proceedings of the 16th European conference on Machine Learning
Year:
2005

Citing 5
Cited 1

Random DFA's can be approximately learned from sparse uniform examples

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Learning non-deterministic finite automata from queries and counterexamples

Machine intelligence 13
Results of the Abbadingo One DFA Learning Competition and a New Evidence-Driven State Merging Algorithm

ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Recent Methods for RNA Modeling Using Stochastic Context-Free Grammars

CPM '94 Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching
Identifying hierarchical structure in sequences: a linear-time algorithm

Journal of Artificial Intelligence Research

Path-equivalent removals of ε-transitions in a genomic weighted finite automaton

CIAA'06 Proceedings of the 11th international conference on Implementation and Application of Automata

Quantified Score

Hi-index	0.00

Visualization

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.