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

Authors:
Elma Akand;Michael Bain;Mark Temple
Affiliations:
University of New South Wales, Sydney, Australia;University of New South Wales, Sydney, Australia;University of New South Wales, Sydney, Australia
Venue:
AOW '07 Proceedings of the Third Australasian Workshop on Advances in Ontologies - Volume 85
Year:
2007

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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 over-representation 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.