ArticleStandards in genomic sciences2010
Quantifying protein function specificity in the gene ontology.
Article in Standards in genomic sciences, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Benchmarking multiple gene ontology enrichment tools reveals high biological significance, ranking, and stringency heterogeneity among datasets.Frontiers in bioinformatics · 2026Article
- FANTASIA leverages language models to decode the functional dark proteome across the animal tree of life.Communications biology · 2025Article
- UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches.Bioinformatics (Oxford, England) · 2015Article
- Integrative computational and experimental approaches to establish a post-myocardial infarction knowledge map.PLoS computational biology · 2014Article
- Modeling sequence and function similarity between proteins for protein functional annotation.Proceedings of the ... International Symposium on High Performance Distributed Computing · 2010Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative or numerical metrics of protein function specificity made possible by the Gene Ontology are useful in that they enable development of distance or similarity measures between protein functions. Here we describe how to calculate four measures of function specificity for GO terms: 1) number of ancestor terms; 2) number of offspring terms; 3) proportion of terms; and 4) Information Content (IC). We discuss the relationship between the metrics and the strengths and weaknesses of each.
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