ArticlePloS one2014
Semantic particularity measure for functional characterization of gene sets using gene ontology.
Article in PloS one, 2014. 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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5 citing papers in PubMed.
- Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation.Bioinformatics (Oxford, England) · 2018Article
- Integrating Information in Biological Ontologies and Molecular Networks to Infer Novel Terms.Scientific reports · 2016Article
- Representing virus-host interactions and other multi-organism processes in the Gene Ontology.BMC microbiology · 2015Article
- Article
- Identification of Chemical Toxicity Using Ontology Information of Chemicals.Computational and mathematical methods in medicine · 2015Article
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3 authors.
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Abstract
backgroundGenetic and genomic data analyses are outputting large sets of genes. Functional comparison of these gene sets is a key part of the analysis, as it identifies their shared functions, and the functions that distinguish each set. The Gene Ontology (GO) initiative provides a unified reference for analyzing the genes molecular functions, biological processes and cellular components. Numerous semantic similarity measures have been developed to systematically quantify the weight of the GO terms shared by two genes. We studied how gene set comparisons can be improved by considering gene set particularity in addition to gene set similarity.
resultsWe propose a new approach to compute gene set particularities based on the information conveyed by GO terms. A GO term informativeness can be computed using either its information content based on the term frequency in a corpus, or a function of the term's distance to the root. We defined the semantic particularity of a set of GO terms Sg1 compared to another set of GO terms Sg2. We combined our particularity measure with a similarity measure to compare gene sets. We demonstrated that the combination of semantic similarity and semantic particularity measures was able to identify genes with particular functions from among similar genes. This differentiation was not recognized using only a semantic similarity measure.
conclusionSemantic particularity should be used in conjunction with semantic similarity to perform functional analysis of GO-annotated gene sets. The principle is generalizable to other ontologies.
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