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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 / Hassani-Pak, Keywan

4 Issues per year


CiteScore 2017: 0.77

SCImago Journal Rank (SJR) 2017: 0.336

Open Access
Online
ISSN
1613-4516
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Volume 9, Issue 3

Issues

Towards a Classification Approach using Meta-Biclustering: Impact of Discretization in the Analysis of Expression Time Series

André V. Carreiro
  • KDBIO group, INESC-ID, Lisbon Portugal
  • Instituto Superior Técnico, Technical University of Lisbon, Portugal
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Artur J. Ferreira
  • Instituto de Telecomunicações, Lisbon Portugal
  • Instituto Superior de Engenharia de Lisboa, Lisbon, Portugal
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Mário A. T. Figueiredo
  • Instituto Superior Técnico, Technical University of Lisbon Portugal
  • Instituto de Telecomunicações, Lisbon, Portugal
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Sara C. Madeira
  • Corresponding author
  • KDBIO group, INESC-ID, Lisbon Portugal
  • Instituto Superior Técnico, Technical University of Lisbon, Portugal
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-10-18 | DOI: https://doi.org/10.1515/jib-2012-207

Summary

Biclustering has been recognized as a remarkably effective method for discovering local temporal expression patterns and unraveling potential regulatory mechanisms, essential to understanding complex biomedical processes, such as disease progression and drug response. In this work, we propose a classification approach based on meta-biclusters (a set of similar biclusters) applied to prognostic prediction. We use real clinical expression time series to predict the response of patients with multiple sclerosis to treatment with Interferon-β. As compared to previous approaches, the main advantages of this strategy are the interpretability of the results and the reduction of data dimensionality, due to biclustering. This would allow the identification of the genes and time points which are most promising for explaining different types of response profiles, according to clinical knowledge. We assess the impact of different unsupervised and supervised discretization techniques on the classification accuracy. The experimental results show that, in many cases, the use of these discretization methods improves the classification accuracy, as compared to the use of the original features.

About the article

Published Online: 2016-10-18

Published in Print: 2012-12-01


Citation Information: Journal of Integrative Bioinformatics, Volume 9, Issue 3, Pages 105–120, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2012-207.

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© 2012 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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