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dc.contributor.authorSmaili, Fatima Z.*
dc.contributor.authorGao, Xin*
dc.contributor.authorHoehndorf, Robert*
dc.date.accessioned2018-02-13T13:43:19Z
dc.date.available2018-02-13T13:43:19Z
dc.date.issued2018-01-31en
dc.identifier.urihttp://hdl.handle.net/10754/627122
dc.description.abstractWe 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.en
dc.publisherarXiven
dc.relation.urlhttp://arxiv.org/abs/1802.00864v1en
dc.relation.urlhttp://arxiv.org/pdf/1802.00864v1en
dc.rightsArchived with thanks to arXiven
dc.titleOnto2Vec: joint vector-based representation of biological entities and their ontology-based annotationsen
dc.typePreprinten
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division*
dc.contributor.departmentComputer Science Program*
dc.contributor.departmentComputational Bioscience Research Center (CBRC)*
dc.eprint.versionPre-printen
dc.identifier.arxividarXiv:1802.00864en
kaust.authorSmaili, Fatima Z.*
kaust.authorGao, Xin*
kaust.authorHoehndorf, Robert*
kaust.grant.numberURF/1/1976-04en
kaust.grant.numberURF/1/1976-06en
kaust.grant.numberURF/1/3450-01-01en
kaust.grant.numberURF/1/3454-01-01en
refterms.dateFOA2018-06-14T05:17:27Z


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