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Extracting and Explaining Biological Knowledge in Microarray Data

  • Paul J. Kennedy
  • Simeon J. Simoff
  • David Skillicorn
  • Daniel Catchpoole
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3056)

Abstract

This paper describes a method of clustering lists of genes mined from a microarray dataset using functional information from the Gene Ontology. The method uses relationships between terms in the ontology both to build clusters and to extract meaningful cluster descriptions. The approach is general and may be applied to assist explanation of other datasets associated with ontologies.

Keywords

Cluster analysis bioinformatics cDNA microarray 

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References

  1. 1.
    Han, J.: How can data mining help bio–data analysis. In: Proc. 2nd Workshop on Data Mining in Bioinformatics BIOKDD 2002, ACM Press, New York (2002)Google Scholar
  2. 2.
    Skillicorn, D., et al.: Strategies for winnowing microarray data. In: Proc. SIAM Data Mining Conf. (2004) (accepted)Google Scholar
  3. 3.
    Ontology Consortium, T.G.: Gene Ontology: tool for the unification of biology. Nature Genetics 25, 25–29 (2000); PubMed ID:10802651 Google Scholar
  4. 4.
    Kennedy, P.J., Simoff, S.J.: CONGO: Clustering on the Gene Ontology. In: Proc. 2nd Australasian Data Mining Workshop ADM 2003, University of Technology, Sydney (2003)Google Scholar
  5. 5.
    Lee, S.G., et al.: A graph–theoretic modeling on GO space for biological interpretation of gene clusters. Bioinformatics 20, 381–388 (2004)CrossRefGoogle Scholar
  6. 6.
    Diehn, M., et al.: SOURCE: a unified genomic resource of functional annotations, ontologies, and gene expression data. Nucleic Acids Research 31, 219–223 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Paul J. Kennedy
    • 1
  • Simeon J. Simoff
    • 1
  • David Skillicorn
    • 2
  • Daniel Catchpoole
    • 3
  1. 1.Faculty of Information TechnologyUniversity of Technology, SydneyBroadwayAustralia
  2. 2.School of ComputingQueen’s UniversityKingstonCanada
  3. 3.The Oncology Research Unit, The Children’s Hospital at WestmeadWestmeadAustralia

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