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Identification of All Exact and Approximate Inverted Repeats in Regular and Weighted Sequences

  • Conference paper
Engineering Applications of Neural Networks (EANN 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 384))

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Abstract

The detection of various types of repeats is a fundamental and well studied problem in stringology. In this paper we present extensions to this problem with applications to bioinformatics. In this paper we consider the detection of all exact and approximate inverted repeats, as well as all exact and approximate weighted inverted repeats and give efficient algorithms for their computation.

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Author information

Authors and Affiliations

  1. Dept. of Informatics, King’s College London, London, WC2R 2LS, UK

    Carl Barton & Costas S. Iliopoulos

  2. Computational Biology Group, University of Cape Town, Cape Town, South Africa

    Nicola Mulder

  3. Fastar Group, University of Pretoria, Pretoria, South Africa

    Costas S. Iliopoulos & Bruce Watson

Authors
  1. Carl Barton
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  2. Costas S. Iliopoulos
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  3. Nicola Mulder
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  4. Bruce Watson
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Editor information

Editors and Affiliations

  1. Department of Forestry & Management of the Environment and Natural Resources, Democritus University of Thrace, GR-68200, Orestiada, Hellas

    Lazaros Iliadis

  2. Frederick University of Cyprus, Cyprus

    Harris Papadopoulos

  3. Faculty of Engineering and Computing, Coventry University, UK

    Chrisina Jayne

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© 2013 Springer-Verlag Berlin Heidelberg

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Barton, C., Iliopoulos, C.S., Mulder, N., Watson, B. (2013). Identification of All Exact and Approximate Inverted Repeats in Regular and Weighted Sequences. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41016-1_2

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  • DOI: https://doi.org/10.1007/978-3-642-41016-1_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41015-4

  • Online ISBN: 978-3-642-41016-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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