Show simple item record

dc.contributor.authorSmaili, Fatima Z.
dc.contributor.authorGao, Xin
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2019-01-21T13:35:03Z
dc.date.available2018-02-13T13:43:19Z
dc.date.available2019-01-21T13:35:03Z
dc.date.issued2018-06-27
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.doi10.1093/bioinformatics/bty259
dc.identifier.urihttp://hdl.handle.net/10754/627122
dc.description.abstractMotivation Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations make them valuable for developing machine learning, data analysis and knowledge extraction algorithms; notably, semantic similarity is widely used to identify relations between biological entities, and ontology-based annotations are frequently used as features in machine learning applications. Results We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering. To evaluate Onto2Vec, we use the gene ontology (GO) and jointly produce dense vector representations of proteins, the GO classes to which they are annotated, and the axioms in GO that constrain these classes. First, we demonstrate that Onto2Vec-generated feature vectors can significantly improve prediction of protein–protein interactions in human and yeast. We then illustrate how Onto2Vec representations provide the means for constructing data-driven, trainable semantic similarity measures that can be used to identify particular relations between proteins. Finally, we use an unsupervised clustering approach to identify protein families based on their Enzyme Commission numbers. Our results demonstrate that Onto2Vec can generate high quality feature vectors from biological entities and ontologies. Onto2Vec has the potential to significantly outperform the state-of-the-art in several predictive applications in which ontologies are involved.
dc.publisherOxford University Press (OUP)
dc.relation.urlhttp://arxiv.org/abs/1802.00864v1
dc.relation.urlhttp://arxiv.org/pdf/1802.00864v1
dc.rightsArchived with thanks to arXiv
dc.titleOnto2Vec: joint vector-based representation of biological entities and their ontology-based annotations
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.identifier.journalBioinformatics
dc.eprint.versionPre-print
dc.identifier.arxividarXiv:1802.00864
kaust.personSmaili, Fatima Z.
kaust.personGao, Xin
kaust.personHoehndorf, Robert
kaust.grant.numberURF/1/1976-04
kaust.grant.numberURF/1/1976-06
kaust.grant.numberURF/1/3450-01-01
kaust.grant.numberURF/1/3454-01-01
refterms.dateFOA2018-06-14T05:17:27Z


Files in this item

Thumbnail
Name:
bty259.pdf
Size:
547.2Kb
Format:
PDF
Description:
Published Article

This item appears in the following Collection(s)

Show simple item record

VersionItemEditorDateSummary

*Selected version