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dc.contributor.authorAli, Mehdi
dc.contributor.authorHoyt, Charles Tapley
dc.contributor.authorDomingo-Fernández, Daniel
dc.contributor.authorLehmann, Jens
dc.contributor.authorJabeen, Hajira
dc.date.accessioned2021-03-11T07:54:33Z
dc.date.available2021-03-11T07:54:33Z
dc.date.issued2019-02-15
dc.identifier.citationAli, M., Hoyt, C. T., Domingo-Fernández, D., Lehmann, J., & Jabeen, H. (2019). BioKEEN: a library for learning and evaluating biological knowledge graph embeddings. Bioinformatics, 35(18), 3538–3540. doi:10.1093/bioinformatics/btz117
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.doi10.1093/bioinformatics/btz117
dc.identifier.urihttp://hdl.handle.net/10754/668066
dc.description.abstractKnowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs’ nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies.
dc.description.sponsorshipWe thank our partners from the Bio2Vec, MLwin and SimpleML projects for their assistance.
dc.description.sponsorshipThis research was supported by Bio2Vec project (http://bio2vec.net/, CRG6) grant [3454] with funding from King Abdullah University of Science and Technology (KAUST).
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/bioinformatics/article/35/18/3538/5320556
dc.rightsThis is a pre-copyedited, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The version of record is available online at: https://academic.oup.com/bioinformatics/article/35/18/3538/5320556.
dc.titleBioKEEN: a library for learning and evaluating biological knowledge graph embeddings
dc.typeArticle
dc.identifier.journalBioinformatics
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Computer Science, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
dc.contributor.institutionDepartment of Life Science Informatics, Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
dc.contributor.institutionDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
dc.contributor.institutionDepartment of Enterprise Information Systems, Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Sankt Augustin, Germany
dc.identifier.volume35
dc.identifier.issue18
dc.identifier.pages3538-3540
dc.identifier.eid2-s2.0-85072307229


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