POCUS: mining genomic sequence annotation to predict disease genes
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* Corresponding author: Colin AM Semple Colin.Semple@hgu.mrc.ac.uk
- † Equal contributors
Genome Biology 2003, 4:R75 doi:10.1186/gb-2003-4-11-r75
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Research
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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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Research article
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Identifying hypothetical genetic influences on complex disease phenotypes Benjamin J Keller, Richard C McEachin BMC Bioinformatics 2009, 10(Suppl 2):S13 (5 February 2009) |
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Research
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Research article
Mandeep Kaur, Sebastian Schmeier, Cameron R MacPherson, Oliver Hofmann, Winston A Hide, Stephen Taylor, Nick Willcox, Vladimir B Bajic BMC Genomics 2008, 9:481 (13 October 2008) |
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Methodology article
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Research article
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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Oral presentation
Prioritisation of Disease Gene Candidates: A Systems Biology Approach Frances S Turner BMC Bioinformatics 2005, 6(Suppl 3):S14 (21 September 2005) |
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Software
G2D: a tool for mining genes associated with disease Carolina Perez-Iratxeta, Matthias Wjst, Peer Bork, Miguel A Andrade BMC Genetics 2005, 6:45 (22 August 2005) |
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Research article
A web tool for finding gene candidates associated with experimentally induced arthritis in the rat Lars Andersson, Greta Petersen, Per Johnson, Fredrik Ståhl Arthritis Research & Therapy 2005, 7:R485-R492 (18 February 2005) |