Learning from Ontological Annotation : an Application of Formal Concept Analysis to Feature Construction in the Gene Ontology

Akand, E., Bain, M. and Temple, M.

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.

Cite as: Akand, E., Bain, M. and Temple, M. (2007). Learning from Ontological Annotation : an Application of Formal Concept Analysis to Feature Construction in the Gene Ontology. In Proc. Third Australasian Ontology Workshop (AOW 2007), Gold Coast, Australia. CRPIT, 85. Meyer, T. and Nayak, A. C., Eds. ACS. 15-23.

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