Genomics. 2012 Jan;99(1):1-9. doi: 10.1016/j.ygeno.2011.10.002. Epub 2011 Oct 14.

A prioritization analysis of disease association by data-mining of functional annotation of human genes.

Taniya T, Tanaka S, Yamaguchi-Kabata Y, Hanaoka H, Yamasaki C, Maekawa H, Barrero RA, Lenhard B, Datta MW, Shimoyama M, Bumgarner R, Chakraborty R, Hopkinson I, Jia L, Hide W, Auffray C, Minoshima S, Imanishi T, Gojobori T.

Source

Japan Biological Information Research Center, Japan Biological Informatics Consortium, AIST Bio-IT Research Building 7F, 2-4-7 Aomi, Tokyo 135-0064, Japan.

Abstract

Complex diseases result from contributions of multiple genes that act in concert through pathways. Here we present a method to prioritize novel candidates of disease-susceptibility genes depending on the biological similarities to the known disease-related genes. The extent of disease-susceptibility of a gene is prioritized by analyzing seven features of human genes captured in H-InvDB. Taking rheumatoid arthritis (RA) and prostate cancer (PC) as two examples, we evaluated the efficiency of our method. Highly scored genes obtained included TNFSF12 and OSM as candidate disease genes for RA and PC, respectively. Subsequent characterization of these genes based upon an extensive literature survey reinforced the validity of these highly scored genes as possible disease-susceptibility genes. Our approach, Prioritization ANalysis of Disease Association (PANDA), is an efficient and cost-effective method to narrow down a large set of genes into smaller subsets that are most likely to be involved in the disease pathogenesis.

PMID:: 22019378; [PubMed - indexed for MEDLINE]

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A prioritization analysis of disease association by data-mining of functional annotation of human genes.
Genomics. 2012 Jan ;99(1):1-9. doi: 10.1016/j.ygeno.2011.10.002. Epub 2011 Oct 14 .

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