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Journal of Integrative Bioinformatics

Editor-in-Chief: Schreiber, Falk / Hofestädt, Ralf

Managing Editor: Sommer, Björn

Ed. by Baumbach, Jan / Chen, Ming / Orlov, Yuriy / Allmer, Jens

Editorial Board: Giorgetti, Alejandro / Harrison, Andrew / Kochetov, Aleksey / Krüger, Jens / Ma, Qi / Matsuno, Hiroshi / Mitra, Chanchal K. / Pauling, Josch K. / Rawlings, Chris / Fdez-Riverola, Florentino / Romano, Paolo / Röttger, Richard / Shoshi, Alban / Soares, Siomar de Castro / Taubert, Jan / Tauch, Andreas / Yousef, Malik / Weise, Stephan

4 Issues per year


CiteScore 2016: 0.93

SCImago Journal Rank (SJR) 2016: 0.416

Open Access
Online
ISSN
1613-4516
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Volume 10, Issue 3

Issues

Improving the performance of Transposable Elements detection tools

Tiago Loureiro / Rui Camacho
  • Corresponding author
  • DEI & Faculdade de Engenharia, Universidade do Porto, http://www.fe.up.pt Portugal
  • LIAAD-INESCTEC, Universidade do Porto, http://www2.inescporto.pt , Portugal
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Jorge Vieira
  • IBMC - Instituto de Biologia Molecular e Celular & Universidade do Porto, http://www.ibmc.up.pt, Portugal
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Nuno A. Fonseca
  • European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), http://www.ebi.ac.uk/ Portugal
  • CRACS-INESCTEC, http://cracs.fc.up.pt/, Portugal
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-10-18 | DOI: https://doi.org/10.1515/jib-2013-231

Summary

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 tool 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 constructed classifiers.

About the article

Published Online: 2016-10-18

Published in Print: 2013-12-01


Citation Information: Journal of Integrative Bioinformatics, Volume 10, Issue 3, Pages 40–50, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2013-231.

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© 2013 The Author(s). Published by Journal of Integrative Bioinformatics.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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