Network-Based Methods for Computational Diagnostics by Means of R

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


Networks representing biomedical data have become a powerful approach in different research disciplines dealing with complex diseases. Also, R and Bioconductor have emerged as a standard research environment to investigate and analyze high-throughput data. Therefore, we present and discuss existing packages, available in R or Bioconductor, that provide methods for computational diagnostics by means of networks. In particular, we summarize packages to reconstruct and analyze networks from high-throughput data. Moreover, we discuss packages that provide comprehensive methods to visualize large-scale gene networks in order to support the field of computational diagnostics of complex diseases. The aim of this chapter is to support an interdisciplinary research community dealing with computational diagnostics to investigate novel hypothesis in a medical and clinical context to gain a better understanding of complex diseases.


Mutual Information Gene Network Layout Algorithm Unweighted Network Prior Biological Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Matthias Dehmer thanks the Austrian Science Funds for supporting this work (project P22029-N13). This work was also partly supported by the Tiroler Wissenschaftsfonds and the Standortagentur Tirol (Tiroler Zukunftsstiftung).


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Copyright information

© Springer-Verlag Wien 2012

Authors and Affiliations

  • Laurin A. J. Mueller
    • 1
  • Matthias Dehmer
    • 2
  • Frank Emmert-Streib
    • 3
  1. 1.UMIT, Institute for Bioinformatics and Translational ResearchHall in TirolAustria
  2. 2.UMIT, Institute for Bioinformatics and Translational ResearchHall in TyrolAustria
  3. 3.Computational Biology and Machine Learning Lab, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical SciencesQueen’s University BelfastBelfastUK

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