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Repurposing Drugs for Alzheimer's Diseases through Link Prediction on Biomedical Literature

dc.rights.licenseopenen_US
dc.contributor.authorXIAO, Y.
dc.contributor.authorHOU, Y.
dc.contributor.authorZHOU, H.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDIALLO, Gayo
ORCID: 0000-0002-9799-9484
IDREF: 112800084
dc.contributor.authorFISZMAN, M.
dc.contributor.authorWOLFSON, J.
dc.contributor.authorKILICOGLU, H.
dc.contributor.authorCHEN, Y.
dc.contributor.authorXU, H.
dc.contributor.authorMANTYH, W. G.
dc.contributor.authorZHANG, R.
dc.date.accessioned2024-02-22T14:57:29Z
dc.date.available2024-02-22T14:57:29Z
dc.date.issued2023-08-19
dc.date.conference2023-06-26
dc.identifier.isbn979-8-3503-0263-9en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/188319
dc.description.abstractEnRecently, computational drug repurposing has emerged as a promising method for identifying new interventions for diseases. This study predicts novel drugs for Alzheimer's disease (AD) through link prediction on our developed biomedical knowledge graph. We constructed a comprehensive knowledge graph containing AD concepts and various potential interventions, called ADInt, by integrating a dietary supplement (DS) domain knowledge graph, SuppKG, with semantic triples from SemMedDB database. Four knowledge graph embedding models (TransE, RotatE, DistMult and ComplEX) and two graph convolutional network models (R-GCN and CompGCN) were compared to learn the representation of ADInt. R-GCN outperformed other models by evaluating on the time slice test set and the clinical trial test set, and was used to generate the score tables for the link prediction task. According to the results of link prediction, we proposed candidate drugs for AD. In conclusion, we presented a novel methodology to extend an existing knowledge graph and discover novel drugs for AD. Our method can potentially be applied to other clinical problems, such as discovering drug adverse reactions and drug-drug interactions.
dc.language.isoENen_US
dc.title.enRepurposing Drugs for Alzheimer's Diseases through Link Prediction on Biomedical Literature
dc.typeCommunication dans un congrèsen_US
dc.identifier.doi10.1109/ICHI57859.2023.00137en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
bordeaux.page750-752en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.conference.titleThe 11th IEEE International Conference on Healthcare Informatics (IEEE ICHI 2023)en_US
bordeaux.countryusen_US
bordeaux.title.proceeding2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)en_US
bordeaux.teamAHEAD_BPHen_US
bordeaux.conference.cityHoustonen_US
hal.identifierhal-04473390
hal.version1
hal.date.transferred2024-02-22T14:57:31Z
hal.invitedouien_US
hal.proceedingsouien_US
hal.conference.organizerInstitute of Electrical and Electronics Engineers (IEEE)en_US
hal.conference.end2023-06-29
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2023-08-19&rft.spage=750-752&rft.epage=750-752&rft.au=XIAO,%20Y.&HOU,%20Y.&ZHOU,%20H.&DIALLO,%20Gayo&FISZMAN,%20M.&rft.isbn=979-8-3503-0263-9&rft.genre=unknown


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