Learning from ontological annotation : an application of formal concept analysis to feature construction in the gene ontology

Elma Akand, Michael Bain, Mark Temple

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

Abstract

A key role for ontologies in bioinformatics is their use as a standardised, structured terminology, particularly to annotate the genes in a genome with functional and other properties. Since the output of many genome-scale experiments results in gene sets it is natural to ask if they share common function. A standard approach is to apply a statistical test for overrepresentation of ontological annotation, often within the Gene Ontology. In this paper we propose an alternative to the standard approach that avoids problems in over-representation analysis due to statistical dependencies between ontology categories. We use a feature construction approach to pre-process Gene Ontology annotation of gene sets and incorporate these features as input to a standard supervised machine learning algorithm. Our approach is shown to allow the straightforward use of an ontology in the context of data sourced from multiple experiments to learn a classifier predicting gene function as part of cellular response to an environmental stress.
Original languageEnglish
Title of host publicationAdvances in Ontologies 2007: Proceedings of the 3rd Australasian Ontology Workshop (AOW 2007), Gold Coast, Australia, 2 December 2007
PublisherAustralian Computer Society
Pages15-23
Number of pages9
ISBN (Print)9781920682668
Publication statusPublished - 2007
EventAustralasian Ontology Workshop -
Duration: 2 Dec 2007 → …

Publication series

Name
ISSN (Print)1445-1336

Conference

ConferenceAustralasian Ontology Workshop
Period2/12/07 → …

Keywords

  • gene ontology
  • bioinformatics

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