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dc.contributor.authorMohamed, Sameh K.
dc.contributor.authorNováček, Vít
dc.contributor.authorNounu, Aayah
dc.date.accessioned2019-03-28T11:24:44Z
dc.date.issued2019-04-08
dc.identifier.citationMohamed, Sameh K., Nováček, Vít, & Nounu, Aayah. (2019). Drug target discovery using knowledge graph embeddings. Paper presented at the 34th ACM/SIGAPP Symposium on Applied Computing (SAC ’19), Limassol, Cyprus, 08-12 April.en_IE
dc.identifier.urihttp://hdl.handle.net/10379/15065
dc.description.abstractThe field of drug discovery has entered a plateau stage lately. It is increasingly more expensive and time-demanding to introduce new drugs into the market. One of the main reasons is the slow progress in finding novel targets for drug candidates and the lack of insight in terms of the associated mechanisms of action. Current works in this area mainly utilise different chemical, genetic and proteomic methods, which are limited in terms of the scalability of experimentation and the scope of studied drugs and targets per experiment. This is mainly due to their dependency on laboratory experiments and available physical resource. This has led to an increasing importance of computational methods for the identification of candidate drug targets. In this work, we introduce a novel computational approach for predicting drug target proteins. We approach the problem as a link prediction task on knowledge graphs. We process drug and target information as a knowledge graph of interconnected drugs, proteins, disease, pathways and other relevant entities. We then apply knowledge graph embedding (KGE) models over this data to enable scoring drug-target associations, where we employ a customised version of state-of-the-art KGE model ComplEx. We generate a benchmarking dataset based on KEGG database to train and evaluate our method. Our experiments show that our method achieves best results in comparison to other traditional KGE models. Specifically, the method predicts drug target links with mean reciprocal rank (MRR) of 0.78 and Hits@10 of 0.88. This provides a promising basis for further experimentation and comparisons with domain-specific predictive models.en_IE
dc.description.sponsorshipThis work has been supported by Insight Centre for Data Analytics at National University of Ireland Galway, Ireland (supported by the Science Foundation Ireland grant 12/RC/2289). The GPU card used in our experiments is granted to us by the Nvidia GPU Grant Program.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherAssociation for Computing Machineryen_IE
dc.relation.ispartofACM Symposium on Applied Computingen
dc.subjectknowledge graphsen_IE
dc.subjectlink predictionen_IE
dc.subjectdrug targetsen_IE
dc.titleDrug target discovery using knowledge graph embeddingsen_IE
dc.typeConference Paperen_IE
dc.date.updated2019-03-21T18:41:46Z
dc.identifier.doi10. 1145/3297280.3297282
dc.local.publishedsourcehttps://doi.org/10. 1145/3297280.3297282en_IE
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funderScience Foundation Irelanden_IE
dc.description.embargo2019-04-08
dc.internal.rssid16055069
dc.local.contactSameh Mohamed, Insight Centre For Data Analytics , Ida Business Park, Newcastle Rd, Galway. - Email: s.kamal1@nuigalway.ie
dc.local.copyrightcheckedYes
dc.local.versionACCEPTED
dcterms.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en_IE
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