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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