Integration of Full-Coverage Probabilistic Functional Networks with Relevance to Specific Biological Processes

  • Katherine James
  • Anil Wipat
  • Jennifer Hallinan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5647)


Probabilistic functional integrated networks are powerful tools with which to draw inferences from high-throughput data. However, network analyses are generally not tailored to specific biological functions or processes. This problem may be overcome by extracting process-specific sub-networks, but this approach discards useful information and is of limited use in poorly annotated areas of the network. Here we describe an extension to existing integration methods which exploits dataset biases in order to emphasise interactions relevant to specific processes, without loss of data. We apply the method to high-throughput data for the yeast Saccharomyces cerevisiae, using Gene Ontology annotations for ageing and telomere maintenance as test processes. The resulting networks perform significantly better than unbiased networks for assigning function to unknown genes, and for clustering to identify important sets of interactions. We conclude that this integration method can be used to enhance network analysis with respect to specific processes of biological interest.


Integrated networks relevance network analysis clustering 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Katherine James
    • 1
    • 2
  • Anil Wipat
    • 1
    • 2
  • Jennifer Hallinan
    • 1
    • 2
  1. 1.School of Computing ScienceNewcastle UniversityNewcastle-upon-TyneUnited Kingdom
  2. 2.Centre for Integrated Biology of Ageing and Nutrition, Ageing Research Laboratoires, Institute for Ageing and HealthNewcastle UniversityNewcastle-upon-TyneUnited Kingdom

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