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Nucleic Acids Research 2005 33(5):1544-1552; doi:10.1093/nar/gki296
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Published online 14 March 2005

© The Author 2005. Published by Oxford University Press. All rights reserved
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Article

Integration of text- and data-mining using ontologies successfully selects disease gene candidates

Nicki Tiffin*, Janet F. Kelso, Alan R. Powell, Hong Pan1, Vladimir B. Bajic1 and Winston A. Hide

South African National Bioinformatics Institute, University of the Western Cape Belville 7535, South Africa 1Knowledge Extraction Laboratory, Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore 119613

*To whom correspondence should be addressed. Tel: +27219592611; Fax: 27219592512; Email: nicki{at}sanbi.ac.za

Received November 19, 2004. Revised January 26, 2005. Accepted February 22, 2005.

Genome-wide techniques such as microarray analysis, Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS), linkage analysis and association studies are used extensively in the search for genes that cause diseases, and often identify many hundreds of candidate disease genes. Selection of the most probable of these candidate disease genes for further empirical analysis is a significant challenge. Additionally, identifying the genes that cause complex diseases is problematic due to low penetrance of multiple contributing genes. Here, we describe a novel bioinformatic approach that selects candidate disease genes according to their expression profiles. We use the eVOC anatomical ontology to integrate text-mining of biomedical literature and data-mining of available human gene expression data. To demonstrate that our method is successful and widely applicable, we apply it to a database of 417 candidate genes containing 17 known disease genes. We successfully select the known disease gene for 15 out of 17 diseases and reduce the candidate gene set to 63.3% (±18.8%) of its original size. This approach facilitates direct association between genomic data describing gene expression and information from biomedical texts describing disease phenotype, and successfully prioritizes candidate genes according to their expression in disease-affected tissues.

Present address: Janet F.Kelso, Max-Planck-Institute for Evolutionary Anthropology, Deutscher Platz 6, D-04103 Leipzig, Germany


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