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Learning the Language of Biological Sequences

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Topics in Grammatical Inference

Abstract

The application to biological sequences is an appealing challenge for Grammatical Inference. 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 nice and open source of problems or inspiration for our research, with the possibility to apply our ideas to real fundamental applications. In this chapter, we survey biological sequences’ main specificities and how they are handled in Pattern/Motif Discovery in order to introduce the important concepts and techniques used and present the latest successful approaches in that field by Grammatical Inference.

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Notes

  1. 1.

    This corresponds to the preservation constraint from [104] forbidding us to merge together the states resulting from merging a diagonal to prevent identified conserved words from being damaged.

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Authors and Affiliations

  1. Inria Rennes—Bretagne Atlantique, Campus de Beaulieu, 35042, Rennes Cedex, France

    François Coste

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  1. François Coste
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Correspondence to François Coste .

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  1. Dept. of Linguistics & Cognitive Sci., University of Delaware, Newark, Delaware, USA

    Jeffrey Heinz

  2. Universidad Politécnica de Valencia, Valencia, Spain

    José M. Sempere

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Coste, F. (2016). Learning the Language of Biological Sequences. In: Heinz, J., Sempere, J. (eds) Topics in Grammatical Inference. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48395-4_8

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  • DOI: https://doi.org/10.1007/978-3-662-48395-4_8

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