Integration of full-coverage probabilistic functional networks with relevance to specific biological processes

Katherine James*, Anil Wipat, Jennifer Hallinan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

8 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationData Integration in the Life Sciences
Subtitle of host publication6th International Workshop, DILS 2009, Manchester, UK, July 20-22, 2009. Proceedings
EditorsNorman W. Paton, Paolo Missier, Cornelia Hedeler
Place of PublicationBerlin
PublisherSpringer, Springer Nature
Pages31-46
Number of pages16
ISBN (Electronic)9783642028793
ISBN (Print)3642028780, 9783642028786
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event6th International Workshop on Data Integration in the Life Sciences, DILS 2009 - Manchester, United Kingdom
Duration: 20 Jul 200922 Jul 2009

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume5647
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Workshop on Data Integration in the Life Sciences, DILS 2009
CountryUnited Kingdom
CityManchester
Period20/07/0922/07/09

Keywords

  • Clustering
  • Integrated networks
  • Network analysis
  • Relevance

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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 N. W. Paton, P. Missier, & C. Hedeler (Eds.), Data Integration in the Life Sciences: 6th International Workshop, DILS 2009, Manchester, UK, July 20-22, 2009. Proceedings (pp. 31-46). (Lecture Notes in Computer Science; Vol. 5647). Berlin: Springer, Springer Nature. https://doi.org/10.1007/978-3-642-02879-3_4