Comparing Biological Networks: A Survey on Graph Classifying Techniques

  • Laurin A. J. Mueller
  • Matthias Dehmer
  • Frank Emmert-Streib

Abstract

In order to compare biological networks numerous methods have been developed. Here, we give an overview of existing methods to compare biological networks meaningfully. Therefore we survey classical approaches of exact an inexact graph matching and discuss existing approaches to compare special types of biological networks. Moreover we review graph kernel-based methods and describe an approach based on structural network measures to classify large biological networks. The aim of this chapter is to provide a survey of techniques to compare biological networks for the interdisciplinary research community dealing with novel research questions in the field of systems biology

Keywords

Graph classification Comparative network analysis  Network biology Exact graph matching Gene networks Global network alignment Graph clustering Graph distance Graph Kernels Graph matching Graph probability distributions Graph similarity  Inexact graph matching Local network alignment Metabolic networks Network alignment Network classification Network clustering Network distance Network probability distributions Network similarity  Structural network measures Superindex Systems biology Topological network descriptors 

Acronyms

GED

Graph Edit Distance

GEO

Gene Expression Omnibus

GO

Gene Ontology

PPI

Protein Protein Interactions

QSAR

Quantitative Structure-Activity Relationship

QSPR

Quantitative Structure-Property Relationships

Notes

Acknowledgments

This work was supported by the Tiroler Wissenschaftsfonds and the Standortagentur Tirol (formerly Tiroler Zukunftsstiftung).

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Laurin A. J. Mueller
    • 1
  • Matthias Dehmer
    • 1
  • Frank Emmert-Streib
    • 2
  1. 1.UMITInstitute for Bioinformatics and Translational ResearchHall in TyrolAustria
  2. 2.Computational Biology and Machine Learning Lab, Center for Cancer Research and Cell Biology, School of MedicineDentistry and Biomedical Sciences, Queen’s University BelfastBelfastUK

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