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2013 IEEE 13th International Conference on Data Mining Workshops (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
ISBN: 0-7695-2702-7
pp: 114-119
Francisco Azuaje , University of Ulster, UK
Alban Chesneau , High-Throughput Protein Technologies Group, France
Olivier Bodenreider , National Institutes of Health., USA
Huiru Zheng , University of Ulster, UK
Haiying Wang , University of Ulster, UK
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)?? 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.
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CITATION
Francisco Azuaje, Alban Chesneau, Olivier Bodenreider, Huiru Zheng, Haiying Wang, "Predictive Integration of Gene Ontology-Driven Similarity and Functional Interactions", 2013 IEEE 13th International Conference on Data Mining Workshops, vol. 00, no. , pp. 114-119, 2006, doi:10.1109/ICDMW.2006.130
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