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

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Data Integration in the Life Sciences (DILS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 5647))

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Abstract

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.

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Authors and Affiliations

  1. School of Computing Science, Newcastle University, Newcastle-upon-Tyne, NE1 7RU, United Kingdom

    Katherine James, Anil Wipat & Jennifer Hallinan

  2. Centre for Integrated Biology of Ageing and Nutrition, Ageing Research Laboratoires, Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle-upon-Tyne, NE4 5PL, United Kingdom

    Katherine James, Anil Wipat & Jennifer Hallinan

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  1. Katherine James
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Editors and Affiliations

  1. School of Computer Science, University of Manchester, M13 9PL, Manchester, UK

    Norman W. Paton  & Paolo Missier  & 

  2. School of Computer Science, The University of Manchester, Oxford Road, M13 9PL, Manchester, UK

    Cornelia Hedeler

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James, K., Wipat, A., Hallinan, J. (2009). Integration of Full-Coverage Probabilistic Functional Networks with Relevance to Specific Biological Processes. In: Paton, N.W., Missier, P., Hedeler, C. (eds) Data Integration in the Life Sciences. DILS 2009. Lecture Notes in Computer Science(), vol 5647. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02879-3_4

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  • DOI: https://doi.org/10.1007/978-3-642-02879-3_4

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