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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 2 of 2 1 Combining evidence, biomedical literature and statistical dependence: new insights for functional annotation of gene sets. Aubry, Marc; Monnier, Annabelle; Chicault, Céline... In: ISSN: 1471-2105 ; BMC Bioinformatics ; https://hal.archives-ouvertes.fr/hal-00094484 ; BMC Bioinformatics, BioMed Central, 2006, 7, pp.241. ⟨10.1186/1471-2105-7-241⟩ (2006) BASE Show details 2 NON-LEXICAL APPROACHES TO IDENTIFYING ASSOCIATIVE RELATIONS IN THE GENE ONTOLOGY Bodenreider, Olivier; Aubry, Marc; Burgun, Anita. - 2005 Abstract: The Gene Ontology (GO) is a controlled vocabulary widely used for the annotation of gene products. GO is organized in three hierarchies for molecular functions, cellular components, and biological processes but no relations are provided among terms across hierarchies. The objective of this study is to investigate three non-lexical approaches to identifying such associative relations in GO and compare them among themselves and to lexical approaches. The three approaches are: computing similarity in a vector space model, statistical analysis of co-occurrence of GO terms in annotation databases, and association rule mining. Five annotation databases (FlyBase, the Human subset of GOA, MGI, SGD, and WormBase) are used in this study. A total of 7,665 associations were identified by at least one of the three non-lexical approaches. Of these, 12% were identified by more than one approach. While there are almost 6,000 lexical relations among GO terms, only 203 associations were identified by both non-lexical and lexical approaches. The associations identified in this study could serve as the starting point for adding associative relations across hierarchies to GO, but would require manual curation. The application to quality assurance of annotation databases is also discussed. Keyword: Article URL: http://www.ncbi.nlm.nih.gov/pubmed/15759617 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2224899 BASE Hide details

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