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dc.contributor.authorSheehan, Brendan
dc.contributor.authorQuigley, Aaron John
dc.contributor.authorGaudin, Benoit
dc.contributor.authorDobson, Simon Andrew
dc.identifier.citationSheehan , B , Quigley , A J , Gaudin , B & Dobson , S A 2008 , ' A relation based measure of semantic similarity for Gene Ontology annotations ' , BMC Bioinformatics , vol. 9 , no. November 2008 , 468 .
dc.identifier.otherPURE: 5051090
dc.identifier.otherPURE UUID: 5d552dd9-4fd6-4db8-866a-6ee51833b231
dc.identifier.otherWOS: 000263971900001
dc.identifier.otherScopus: 60849139073
dc.identifier.otherORCID: /0000-0002-5274-6889/work/34040120
dc.identifier.otherORCID: /0000-0001-9633-2103/work/70234183
dc.descriptionThis work has been supported by Microsoft Research Cambridge and the Irish Research Council for Science, Engineering and Technology.en
dc.description.abstractBackground: Various measures of semantic similarity of terms in bio-ontologies such as the Gene Ontology (GO) have been used to compare gene products. Such measures of similarity have been used to annotate uncharacterized gene products and group gene products into functional groups. There are various ways to measure semantic similarity, either using the topological structure of the ontology, the instances (gene products) associated with terms or a mixture of both. We focus on an instance level definition of semantic similarity while using the information contained in the ontology, both in the graphical structure of the ontology and the semantics of relations between terms, to provide constraints on our instance level description. Semantic similarity of terms is extended to annotations by various approaches, either though aggregation operations such as min, max and average or through an extrapolative method. These approaches introduce assumptions about how semantic similarity of terms relates to the semantic similarity of annotations that do not necessarily reflect how terms relate to each other. Results: We exploit the semantics of relations in the GO to construct an algorithm called SSA that provides the basis of a framework that naturally extends instance based methods of semantic similarity of terms, such as Resnik's measure, to describing annotations and not just terms. Our measure attempts to correctly interpret how terms combine via their relationships in the ontological hierarchy. SSA uses these relationships to identify the most specific common ancestors between terms. We outline the set of cases in which terms can combine and associate partial order constraints with each case that order the specificity of terms. These cases form the basis for the SSA algorithm. The set of associated constraints also provide a set of principles that any improvement on our method should seek to satisfy. Conclusion: We derive a measure of semantic similarity between annotations that exploits all available information without introducing assumptions about the nature of the ontology or data. We preserve the principles underlying instance based methods of semantic similarity of terms at the annotation level. As a result our measure better describes the information contained in annotations associated with gene products and as a result is better suited to characterizing and classifying gene products through their annotations.
dc.relation.ispartofBMC Bioinformaticsen
dc.rights© 2008 Sheehan et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly citeden
dc.subjectQH426 Geneticsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.titleA relation based measure of semantic similarity for Gene Ontology annotationsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.description.statusPeer revieweden

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