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PLoS One

hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorAYLLÓN-BENÍTEZ, Aarón
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorMOUGIN, Fleur
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorALLALI, Julien
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorTHIÉBAUT, Rodolphe
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorTHEBAULT, Patricia
dc.date.accessioned2024-04-15T09:54:22Z
dc.date.available2024-04-15T09:54:22Z
dc.date.issued2018
dc.identifier.issn1932-6203
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/198669
dc.description.abstractEnMOTIVATION: The recent revolution in new sequencing technologies, as a part of the continuous process of adopting new innovative protocols has strongly impacted the interpretation of relations between phenotype and genotype. Thus, understanding the resulting gene sets has become a bottleneck that needs to be addressed. Automatic methods have been proposed to facilitate the interpretation of gene sets. While statistical functional enrichment analyses are currently well known, they tend to focus on well-known genes and to ignore new information from less-studied genes. To address such issues, applying semantic similarity measures is logical if the knowledge source used to annotate the gene sets is hierarchically structured. In this work, we propose a new method for analyzing the impact of different semantic similarity measures on gene set annotations. RESULTS: We evaluated the impact of each measure by taking into consideration the two following features that correspond to relevant criteria for a "good" synthetic gene set annotation: (i) the number of annotation terms has to be drastically reduced and the representative terms must be retained while annotating the gene set, and (ii) the number of genes described by the selected terms should be as large as possible. Thus, we analyzed nine semantic similarity measures to identify the best possible compromise between both features while maintaining a sufficient level of details. Using Gene Ontology to annotate the gene sets, we obtained better results with node-based measures that use the terms' characteristics than with measures based on edges that link the terms. The annotation of the gene sets achieved with the node-based measures did not exhibit major differences regardless of the characteristics of terms used.
dc.language.isoen
dc.publisherPublic Library of Science
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enSISTM
dc.subject.enERIAS
dc.titlePLoS One
dc.title.enA new method for evaluating the impacts of semantic similarity measures on the annotation of gene sets
dc.typeArticle de revue
dc.identifier.doi10.1371/journal.pone.0208037
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologie
bordeaux.journalPLoS ONE
bordeaux.pagee0208037
bordeaux.volume13
bordeaux.hal.laboratoriesLaboratoire Bordelais de Recherche en Informatique (LaBRI) - UMR 5800*
bordeaux.issue11
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01938961
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01938961v1
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