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Boosting the Detection of Transposable Elements Using Machine Learning

  • Conference paper
7th International Conference on Practical Applications of Computational Biology & Bioinformatics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 222))

Abstract

Transposable Elements (TE) are sequences of DNA that move and transpose within a genome. TEs, as mutation agents, are quite important for their role in both genome alteration diseases and on species evolution. Several tools have been developed to discover and annotate TEs but no single one achieves good results on all different types of TEs. In this paper we evaluate the performance of several TEs detection and annotation tools and investigate if Machine Learning techniques can be used to improve their overall detection accuracy. The results of an in silico evaluation of TEs detection and annotation tools indicate that their performance can be improved by using machine learning classifiers.

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Author information

Authors and Affiliations

  1. DEI & Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    Tiago Loureiro

  2. DEI & Faculdade de Engenharia & LIAAD-INESCTEC, Universidade do Porto, Porto, Portugal

    Rui Camacho

  3. IBMC - Instituto de Biologia Molecular e Celular & Universidade do Porto, Porto, Portugal

    Jorge Vieira

  4. EMBL Outstation, European Bioinformatics Institute (EBI), Hinxton, Cambridge, CB10 ISD, UK

    Nuno A. Fonseca

  5. CRACS-INESCTEC, Porto, Portugal

    Nuno A. Fonseca

Authors
  1. Tiago Loureiro
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  2. Rui Camacho
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  3. Jorge Vieira
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  4. Nuno A. Fonseca
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Corresponding author

Correspondence to Tiago Loureiro .

Editor information

Editors and Affiliations

  1. , Department of Software Engineering, Universiti Teknologi Malaysia, Skudai, Johor, 81310, Malaysia

    Mohd Saberi Mohamad

  2. , Department of Information Engineering, Università di Padua, Via Gradenigo 6, Padua, 35122, Italy

    Loris Nanni

  3. , Dep. Informática / CCTC, Universidade do Minho, Braga, 4710 - 057, Portugal

    Miguel P. Rocha

  4. , Department of Informatics, University of Vigo, Ourense, 32004, Spain

    Florentino Fdez-Riverola

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© 2013 Springer International Publishing Switzerland

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Loureiro, T., Camacho, R., Vieira, J., Fonseca, N.A. (2013). Boosting the Detection of Transposable Elements Using Machine Learning. In: Mohamad, M., Nanni, L., Rocha, M., Fdez-Riverola, F. (eds) 7th International Conference on Practical Applications of Computational Biology & Bioinformatics. Advances in Intelligent Systems and Computing, vol 222. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00578-2_12

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  • DOI: https://doi.org/10.1007/978-3-319-00578-2_12

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-00577-5

  • Online ISBN: 978-3-319-00578-2

  • eBook Packages: EngineeringEngineering (R0)

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