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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


CiteScore 2018: 0.90

SCImago Journal Rank (SJR) 2018: 0.315

Open Access
Online
ISSN
1613-4516
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Volume 11, Issue 1

Issues

Profiling of Genetic Switches using Boolean Implications in Expression Data

M. Volkan Çakır
  • Corresponding author
  • Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstr. 16 – 18, 04107 Leipzig, Germany
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Hans Binder
  • Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstr. 16 – 18, 04107 Leipzig, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Henry Wirth
  • Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstr. 16 – 18, 04107 Leipzig, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-10-18 | DOI: https://doi.org/10.1515/jib-2014-246

Summary

Correlation analysis assuming coexpression of the genes is a widely used method for gene expression analysis in molecular biology. Yet growing extent, quality and dimensionality of the molecular biological data permits emerging, more sophisticated approaches like Boolean implications.

We present an approach which is a combination of the SOM (self organizing maps) machine learning method and Boolean implication analysis to identify relations between genes, metagenes and similarly behaving metagene groups (spots). Our method provides a way to assign Boolean states to genes/metagenes/spots and offers a functional view over significantly variant elements of gene expression data on these three different levels. While being able to cover relations between weakly correlated entities Boolean implication method also decomposes these relations into six implication classes.

Our method allows one to validate or identify potential relationships between genes and functional modules of interest and to assess their switching behaviour. Furthermore the output of the method renders it possible to construct and study the network of genes. By providing logical implications as updating rules for the network it can also serve to aid modelling approaches.

About the article

Published Online: 2016-10-18

Published in Print: 2014-03-01


Citation Information: Journal of Integrative Bioinformatics, Volume 11, Issue 1, Pages 30–54, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2014-246.

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© 2014 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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