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dc.rights.licenseopenen_US
dc.contributor.authorXIAO, Yongkang
dc.contributor.authorHOU, Yu
dc.contributor.authorZHOU, Huixue
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDIALLO, Gayo
ORCID: 0000-0002-9799-9484
IDREF: 112800084
dc.contributor.authorFISZMAN, Marcelo
dc.contributor.authorWOLFSON, Julian
dc.contributor.authorZHOU, Li
dc.contributor.authorKILICOGLU, Halil
dc.contributor.authorCHEN, You
dc.contributor.authorSU, Chang
dc.contributor.authorXU, Hua
dc.contributor.authorMANTYH, William G
dc.contributor.authorZHANG, Rui
dc.date.accessioned2024-06-24T13:52:53Z
dc.date.available2024-06-24T13:52:53Z
dc.date.issued2024-04-15
dc.identifier.issn2045-2322en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/200631
dc.description.abstractEnNon-pharmaceutical interventions (NPI) have great potential to improve cognitive function but limited investigation to discover NPI repurposing for Alzheimer's Disease (AD). This is the first study to develop an innovative framework to extract and represent NPI information from biomedical literature in a knowledge graph (KG), and train link prediction models to repurpose novel NPIs for AD prevention. We constructed a comprehensive KG, called ADInt, by extracting NPI information from biomedical literature. We used the previously-created SuppKG and NPI lexicon to identify NPI entities. Four KG embedding models (i.e., TransE, RotatE, DistMult and ComplEX) and two novel graph convolutional network models (i.e., R-GCN and CompGCN) were trained and compared to learn the representation of ADInt. Models were evaluated and compared on two test sets (time slice and clinical trial ground truth) and the best performing model was used to predict novel NPIs for AD. Discovery patterns were applied to generate mechanistic pathways for high scoring candidates. The ADInt has 162,212 nodes and 1,017,284 edges. R-GCN performed best in time slice (MR = 5.2054, Hits@10 = 0.8496) and clinical trial ground truth (MR = 3.4996, Hits@10 = 0.9192) test sets. After evaluation by domain experts, 10 novel dietary supplements and 10 complementary and integrative health were proposed from the score table calculated by R-GCN. Among proposed novel NPIs, we found plausible mechanistic pathways for photodynamic therapy and Choerospondias axillaris to prevent AD, and validated psychotherapy and manual therapy techniques using real-world data analysis. The proposed framework shows potential for discovering new NPIs for AD prevention and understanding their mechanistic pathways.
dc.description.sponsorshipInitiative d'excellence de l'Université de Bordeauxen_US
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.title.enRepurposing non-pharmacological interventions for Alzheimer's disease through link prediction on biomedical literature
dc.title.alternativeSci Repen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1038/s41598-024-58604-8en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed38622164en_US
bordeaux.journalScientific Reportsen_US
bordeaux.page8693en_US
bordeaux.volume14en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.issue1en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamAHEAD_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.identifier.funderIDNational Institutes of Healthen_US
hal.identifierhal-04622367
hal.version1
hal.date.transferred2024-06-24T13:52:57Z
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.jtitle=Scientific%20Reports&rft.date=2024-04-15&rft.volume=14&rft.issue=1&rft.spage=8693&rft.epage=8693&rft.eissn=2045-2322&rft.issn=2045-2322&rft.au=XIAO,%20Yongkang&HOU,%20Yu&ZHOU,%20Huixue&DIALLO,%20Gayo&FISZMAN,%20Marcelo&rft.genre=article


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