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dc.contributor.authorMohamed, Sameh K .
dc.contributor.authorNováček, Vít
dc.contributor.authorNounu, Aayah
dc.date.accessioned2019-09-03T12:18:18Z
dc.date.issued2019-08-01
dc.identifier.citationMohamed, Sameh K, Nováček, Vít, & Nounu, Aayah. (2019). Discovering Protein Drug Targets Using Knowledge Graph Embeddings. Bioinformatics. doi: 10.1093/bioinformatics/btz600en_IE
dc.identifier.issn1460-2059
dc.identifier.urihttp://hdl.handle.net/10379/15375
dc.description.abstractMotivation Computational approaches for predicting drug-target interactions (DTIs) can provide valuable insights into the drug mechanism of action. DTI predictions can help to quickly identify new promising (on-target) or unintended (off-target) effects of drugs. However, existing models face several challenges. Many can only process a limited number of drugs and/or have poor proteome coverage. The current approaches also often suffer from high false positive prediction rates. Results We propose a novel computational approach for predicting drug target proteins. The approach is based on formulating the problem as a link prediction in knowledge graphs (robust, machine-readable representations of networked knowledge). We use biomedical knowledge bases to create a knowledge graph of entities connected to both drugs and their potential targets. We propose a specific knowledge graph embedding model, TriModel, to learn vector representaions (i.e. embeddings) for all drugs and targets in the created knowledge graph. These representations are consequently used to infer candidate drug target interactions based on their scores computed by the trained TriModel model. We have experimentally evaluated our method using computer simulations and compared it to five existing models. This has shown that our approach outperforms all previous ones in terms of both area under ROC and precision-recall curves in standard benchmark tests. Availability The data, predictions, and models are available at: drugtargets.insight-centre.orgen_IE
dc.description.sponsorshipThis publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289, co-funded by the European Regional Development Fund.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherOxford University Pressen_IE
dc.relation.ispartofBioinformaticsen
dc.subjectDrug targetsen_IE
dc.subjectknowledge graph embeddingsen_IE
dc.titleDiscovering protein drug targets using knowledge graph embeddingsen_IE
dc.typeArticleen_IE
dc.date.updated2019-08-06T14:33:01Z
dc.identifier.doi10.1093/bioinformatics/btz600
dc.local.publishedsourcehttps://doi.org/10.1093/bioinformatics/btz600en_IE
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funderScience Foundation Irelanden_IE
dc.contributor.funderEuropean Regional Development Funden_IE
dc.description.embargo2020-08-01
dc.internal.rssid17159839
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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