Speeding disease gene discovery by sequence based candidate prioritization
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* Corresponding author: Euan A Adie euan.adie@ed.ac.uk
BMC Bioinformatics 2005, 6:55 doi:10.1186/1471-2105-6-55
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Method
Bolan Linghu, Evan S Snitkin, Zhenjun Hu, Yu Xia, Charles DeLisi Genome Biology 2009, 10:R91 (3 September 2009) An evidence-weighted functional-linkage network of human genes reveals associations among diseases that share no known disease genes and have dissimilar phenotypes |
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Review
Linking genes to diseases: it's all in the data Nicki Tiffin, Miguel A Andrade-Navarro, Carolina Perez-Iratxeta Genome Med 2009, 1:77 (7 August 2009) Computational approaches to disease-gene associations, especially those that use phenotype ontologies, can help to prioritize the most likely candidate genes.
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Rong Chen, Alex A Morgan, Joel Dudley, Tarangini Deshpande, Li Li, Keiichi Kodama, Annie P Chiang, Atul J Butte Genome Biology 2008, 9:R170 (5 December 2008) Differential expressed genes are more likely to have variants associated with disease. A new tool, fitSNP, prioritizes candidate SNPs from association studies. |
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Improved human disease candidate gene prioritization using mouse phenotype Jing Chen, Huan Xu, Bruce J Aronow, Anil G Jegga BMC Bioinformatics 2007, 8:392 (16 October 2007) |
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Further understanding human disease genes by comparing with housekeeping genes and other genes Zhidong Tu, Li Wang, Min Xu, Xianghong Zhou, Ting Chen, Fengzhu Sun BMC Genomics 2006, 7:31 (21 February 2006) |