Computational Medicine

pp 185-197


Network-Based Methods for Computational Diagnostics by Means of R

  • Laurin A. J. MuellerAffiliated withUMIT, Institute for Bioinformatics and Translational Research
  • , Matthias DehmerAffiliated withUMIT, Institute for Bioinformatics and Translational Research Email author 
  • , Frank Emmert-StreibAffiliated withComputational Biology and Machine Learning Lab, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast

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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.