Learning the Language of Biological Sequences

François Coste 1
1 Dyliss - Dynamics, Logics and Inference for biological Systems and Sequences
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : Learning the language of biological sequences is an appealing challenge for the grammatical inference research field. While some first successes have already been recorded, such as the inference of profile hidden Markov models or stochastic context-free grammars which are now part of the classical bioinformatics toolbox, it is still a source of open and nice inspirational problems for grammatical inference, enabling us to confront our ideas to real fundamental applications. As an introduction to this field, we survey here the main ideas and concepts behind the approaches developed in pattern/motif discovery and grammatical inference to characterize successfully the biological sequences with their specificities.
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Chapitre d'ouvrage
Jeffrey Heinz; José M. Sempere. Topics in Grammatical Inference, Springer-Verlag, 2016, 978-3-662-48393-0. 〈10.1007/978-3-662-48395-4_8〉. 〈http://link.springer.com/chapter/10.1007%2F978-3-662-48395-4_8〉
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Soumis le : lundi 12 septembre 2016 - 13:51:57
Dernière modification le : mercredi 2 août 2017 - 10:07:16

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François Coste. Learning the Language of Biological Sequences. Jeffrey Heinz; José M. Sempere. Topics in Grammatical Inference, Springer-Verlag, 2016, 978-3-662-48393-0. 〈10.1007/978-3-662-48395-4_8〉. 〈http://link.springer.com/chapter/10.1007%2F978-3-662-48395-4_8〉. 〈hal-01244770v2〉

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