@inproceedings{40f0ad0df2254266967254aa5709b536,
title = "Learning from ontological annotation : an application of formal concept analysis to feature construction in the gene ontology",
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.",
keywords = "gene ontology, bioinformatics",
author = "Elma Akand and Michael Bain and Mark Temple",
year = "2007",
language = "English",
isbn = "9781920682668",
publisher = "Australian Computer Society",
pages = "15--23",
booktitle = "Advances in Ontologies 2007: Proceedings of the 3rd Australasian Ontology Workshop (AOW 2007), Gold Coast, Australia, 2 December 2007",
note = "Australasian Ontology Workshop ; Conference date: 02-12-2007",
}