Learning with gene ontology annotation using feature selection and construction

A key role for ontologies in bioinformatics is their use as a standardized, 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 a common function. A standard approach is to apply a statistical test for overrepresentation of functional annotation, often within the gene ontology. In this article we propose an alternative to the standard approach that avoids problems in overrepresentation analysis due to statistical dependencies between ontology categories. We apply methods of feature construction and selection to preprocess gene ontology terms used for the 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 classifiers predicting gene function as part of a cellular response to environmental stress.