POCUS: mining genomic sequence annotation to predict disease genes View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2003-11

AUTHORS

Frances S Turner, Daniel R Clutterbuck, Colin AM Semple

ABSTRACT

Here we present POCUS (prioritization of candidate genes using statistics), a novel computational approach to prioritize candidate disease genes that is based on over-representation of functional annotation between loci for the same disease. We show that POCUS can provide high (up to 81-fold) enrichment of real disease genes in the candidate-gene shortlists it produces compared with the original large sets of positional candidates. In contrast to existing methods, POCUS can also suggest counterintuitive candidates. More... »

PAGES

r75

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/gb-2003-4-11-r75

DOI

http://dx.doi.org/10.1186/gb-2003-4-11-r75

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1051291402

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/14611661


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