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dc.contributor.author
Edera, Alejandro
dc.contributor.author
Milone, Diego Humberto
dc.contributor.author
Stegmayer, Georgina
dc.date.available
2023-09-08T17:22:36Z
dc.date.issued
2022-02
dc.identifier.citation
Edera, Alejandro; Milone, Diego Humberto; Stegmayer, Georgina; Anc2vec: Embedding gene ontology terms by preserving ancestors relationships; Oxford University Press; Briefings In Bioinformatics; 23; 2; 2-2022; 1-11
dc.identifier.issn
1467-5463
dc.identifier.uri
http://hdl.handle.net/11336/210993
dc.description.abstract
The gene ontology (GO) provides a hierarchical structure with a controlled vocabulary composed of terms describing functions and localization of gene products. Recent works propose vector representations, also known as embeddings, of GO terms that capture meaningful information about them. Significant performance improvements have been observed when these representations are used on diverse downstream tasks, such as the measurement of semantic similarity between GO terms and functional similarity between proteins. Despite the success shown by these approaches, existing embeddings of GO terms still fail to capture crucial structural features of the GO. Here, we present anc2vec, a novel protocol based on neural networks for constructing vector representations of GO terms by preserving three important ontological features: its ontological uniqueness, ancestors hierarchy and sub-ontology membership. The advantages of using anc2vec are demonstrated by systematic experiments on diverse tasks: visualization, sub-ontology prediction, inference of structurally related terms, retrieval of terms from aggregated embeddings, and prediction of protein-protein interactions. In these tasks, experimental results show that the performance of anc2vec representations is better than those of recent approaches. This demonstrates that higher performances on diverse tasks can be achieved by embeddings when the structure of the GO is better represented. Full source code and data are available at https://github.com/sinc-lab/anc2vec.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Oxford University Press
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
GENE ONTOLOGY
dc.subject
NEURAL NETWORKS
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PROTEIN-PROTEIN INTERACTIONS
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SEMANTIC SIMILARITY
dc.subject.classification
Ciencias de la Información y Bioinformática
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Anc2vec: Embedding gene ontology terms by preserving ancestors relationships
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-08-07T14:58:16Z
dc.journal.volume
23
dc.journal.number
2
dc.journal.pagination
1-11
dc.journal.pais
Reino Unido
dc.journal.ciudad
Oxford
dc.description.fil
Fil: Edera, Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
dc.description.fil
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
dc.description.fil
Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
dc.journal.title
Briefings In Bioinformatics
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbac003/6523148
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1093/bib/bbac003
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