Information content-based gene ontology functional similarity measures: which one to use for a given biological data type?

dc.contributor.authorMazandu, Gaston Ken_ZA
dc.contributor.authorMulder, Nicola Jen_ZA
dc.date.accessioned2015-12-28T06:47:48Z
dc.date.available2015-12-28T06:47:48Z
dc.date.issued2014en_ZA
dc.description.abstractThe current increase in Gene Ontology (GO) annotations of proteins in the existing genome databases and their use in different analyses have fostered the improvement of several biomedical and biological applications. To integrate this functional data into different analyses, several protein functional similarity measures based on GO term information content (IC) have been proposed and evaluated, especially in the context of annotation-based measures. In the case of topology-based measures, each approach was set with a specific functional similarity measure depending on its conception and applications for which it was designed. However, it is not clear whether a specific functional similarity measure associated with a given approach is the most appropriate, given a biological data set or an application, i.e., achieving the best performance compared to other functional similarity measures for the biological application under consideration. We show that, in general, a specific functional similarity measure often used with a given term IC or term semantic similarity approach is not always the best for different biological data and applications. We have conducted a performance evaluation of a number of different functional similarity measures using different types of biological data in order to infer the best functional similarity measure for each different term IC and semantic similarity approach. The comparisons of different protein functional similarity measures should help researchers choose the most appropriate measure for the biological application under consideration.en_ZA
dc.identifier.apacitationMazandu, G. K., & Mulder, N. J. (2014). Information content-based gene ontology functional similarity measures: which one to use for a given biological data type?. <i>PLoS One</i>, http://hdl.handle.net/11427/16054en_ZA
dc.identifier.chicagocitationMazandu, Gaston K, and Nicola J Mulder "Information content-based gene ontology functional similarity measures: which one to use for a given biological data type?." <i>PLoS One</i> (2014) http://hdl.handle.net/11427/16054en_ZA
dc.identifier.citationMazandu, G. K., & Mulder, N. J. (2014). Information content-based gene ontology functional similarity measures: which one to use for a given biological data type?. PloS one, 9(12), e113859. doi:10.1371/journal.pone.0113859en_ZA
dc.identifier.ris TY - Journal Article AU - Mazandu, Gaston K AU - Mulder, Nicola J AB - The current increase in Gene Ontology (GO) annotations of proteins in the existing genome databases and their use in different analyses have fostered the improvement of several biomedical and biological applications. To integrate this functional data into different analyses, several protein functional similarity measures based on GO term information content (IC) have been proposed and evaluated, especially in the context of annotation-based measures. In the case of topology-based measures, each approach was set with a specific functional similarity measure depending on its conception and applications for which it was designed. However, it is not clear whether a specific functional similarity measure associated with a given approach is the most appropriate, given a biological data set or an application, i.e., achieving the best performance compared to other functional similarity measures for the biological application under consideration. We show that, in general, a specific functional similarity measure often used with a given term IC or term semantic similarity approach is not always the best for different biological data and applications. We have conducted a performance evaluation of a number of different functional similarity measures using different types of biological data in order to infer the best functional similarity measure for each different term IC and semantic similarity approach. The comparisons of different protein functional similarity measures should help researchers choose the most appropriate measure for the biological application under consideration. DA - 2014 DB - OpenUCT DO - 10.1371/journal.pone.0113859 DP - University of Cape Town J1 - PLoS One LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - Information content-based gene ontology functional similarity measures: which one to use for a given biological data type? TI - Information content-based gene ontology functional similarity measures: which one to use for a given biological data type? UR - http://hdl.handle.net/11427/16054 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/16054
dc.identifier.urihttp://dx.doi.org/10.1371/journal.pone.0113859
dc.identifier.vancouvercitationMazandu GK, Mulder NJ. Information content-based gene ontology functional similarity measures: which one to use for a given biological data type?. PLoS One. 2014; http://hdl.handle.net/11427/16054.en_ZA
dc.language.isoengen_ZA
dc.publisherPublic Library of Scienceen_ZA
dc.publisher.departmentInstitute of Infectious Disease and Molecular Medicineen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.rightsThis 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 author and source are credited.en_ZA
dc.rights.holder© 2014 Mazandu, Mulderen_ZA
dc.rights.urihttp://creativecommons.org/licenses/by/4.0en_ZA
dc.sourcePLoS Oneen_ZA
dc.source.urihttp://journals.plos.org/plosoneen_ZA
dc.subject.otherGene ontologiesen_ZA
dc.subject.otherProtein domainsen_ZA
dc.subject.otherProtein interaction networksen_ZA
dc.subject.otherProtein-protein interactionsen_ZA
dc.subject.otherGene ontology annotationsen_ZA
dc.subject.otherSemanticsen_ZA
dc.subject.otherGene expressionen_ZA
dc.subject.otherMicroarraysen_ZA
dc.titleInformation content-based gene ontology functional similarity measures: which one to use for a given biological data type?en_ZA
dc.typeJournal Articleen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceArticleen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Mazandu_Information_Content_Based_Gene_Ontology_2014.pdf
Size:
1.31 MB
Format:
Adobe Portable Document Format
Description:
Collections