Predictive Integration of Gene Ontology-Driven Similarity and Functional Interactions

Authors:
Francisco Azuaje;Haiying Wang;Huiru Zheng;Olivier Bodenreider;Alban Chesneau
Affiliations:
University of Ulster, UK;University of Ulster, UK;University of Ulster, UK;National Institutes of Health., USA;High-Throughput Protein Technologies Group, France
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

Citing 0
Cited 3

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An information theoretic approach to assessing gene-ontology-driven similarity and its application

International Journal of Data Mining and Bioinformatics

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Abstract

There is a need to develop methods to automatically incorporate prior knowledge to support the prediction and validation of novel functional associations. One such important source is represented by the Gene Ontology (GO)脝脣 and the many model organism databases of gene products annotated to the GO. We investigated quantitative relationships between the GO-driven similarity of genes and their functional interactions by analyzing different types of associations in Saccharomyces cerevisiae and Caenorhabditis elegans. Interacting genes exhibited significantly higher levels of GO-driven similarity (GOS) in comparison to random pairs of genes used as a surrogate for negative interactions. The Biological Process hierarchy provides more reliable results for co-regulatory and protein-protein interactions. GOS represent a relevant resource to support prediction of functional networks in combination with other resources.