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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 2017: 0.77

SCImago Journal Rank (SJR) 2017: 0.336

Open Access
Online
ISSN
1613-4516
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Volume 9, Issue 1

Issues

Predicting Genes Involved in Human Cancer Using Network Contextual Information

Hossein Rahmani
  • Corresponding author
  • Leiden Institute of Advanced Computer Science, Universiteit Leiden, Niels Bohrweg 1, 2333 CA Leiden, Netherlands
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Hendrik Blockeel
  • Leiden Institute of Advanced Computer Science, Universiteit Leiden, Niels Bohrweg 1, 2333 CA Leiden Belgium
  • Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Andreas Bender
  • Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom of Great Britain and Northern Ireland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-10-18 | DOI: https://doi.org/10.1515/jib-2012-210

Summary

Protein-Protein Interaction (PPI) networks have been widely used for the task of predicting proteins involved in cancer. Previous research has shown that functional information about the protein for which a prediction is made, proximity to specific other proteins in the PPI network, as well as local network structure are informative features in this respect. In this work, we introduce two new types of input features, reflecting additional information: (1) Functional Context: the functions of proteins interacting with the target protein (rather than the protein itself); and (2) Structural Context: the relative position of the target protein with respect to specific other proteins selected according to a novel ANOVA (analysis of variance) based measure. We also introduce a selection strategy to pinpoint the most informative features. Results show that the proposed feature types and feature selection strategy yield informative features. A standard machine learning method (Naive Bayes) that uses the features proposed here outperforms the current state-of-the-art methods by more than 5% with respect to F-measure. In addition, manual inspection confirms the biological relevance of the top-ranked features.

About the article

Published Online: 2016-10-18

Published in Print: 2012-03-01


Citation Information: Journal of Integrative Bioinformatics, Volume 9, Issue 1, Pages 44–71, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2012-210.

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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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