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Biomedical Knowledge Graph Embeddings for Personalized Medicine

2021-09Conference object Restricted access
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dc.contributor.authorVilela, Joana
dc.contributor.authorAsif, Muhammad
dc.contributor.authorMarques, Ana Rita
dc.contributor.authorXavier Santos, João
dc.contributor.authorRasga, Célia
dc.contributor.authorVicente, Astrid
dc.contributor.authorMartiniano, Hugo
dc.date.accessioned2022-07-06T15:09:03Z
dc.date.available2022-07-06T15:09:03Z
dc.date.issued2021-09
dc.descriptionConference paper publicado em: In: Marreiros G, Melo FS, Lau N, Lopes Cardoso H, Reis LP, (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science. Springer Cham, 2021, pp. 584-595. (LNAI,vol. 12981). https://doi.org/10.1007/978-3-030-86230-5_46pt_PT
dc.description.abstractPersonalized medicine promises to revolutionize healthcare in the coming years. However significant challenges remain, namely in regard to integrating the vast amount of biomedical knowledge generated in the last few years. Here we describe an approach that uses Knowledge Graph Embedding (KGE) methods on a biomedical Knowledge Graph as a path to reasoning over the wealth of information stored in publicly accessible databases. We use curated databases such as Ensembl, DisGeNET and Gene Ontology as data sources to build a Knowledge Graph containing relationships between genes, diseases and other biological entities and explore the potential of KGE methods to derive medically relevant insights from this KG. To showcase the method’s usefulness we describe two use cases: a) prediction of gene-disease associations and b) clustering of disease embeddings. We show that the top gene-disease associations predicted by this approach can be confirmed in external databases or have already been identified in the literature. An analysis of clusters of diseases, with a focus on Autism Spectrum Disorder (ASD), affords novel insights into the biology of this paradigmatic complex disorder and the overlap of its genetic background with other diseasespt_PT
dc.description.sponsorshipThe authors would like to acknowledge the support by the UID/MULTI/04046/2019 centre grant from FCT, Portugal (to BioISI), and the MedPerSyst project (POCI-01-0145-FEDER-016428-PAC) “Redes sinapticas e abordagens compreensivas de medicina personalizada em doen¸cas neurocomportamentais ao longo da vida” (SAICTPAC/0010/2015). This work used the European Grid Infrastructure (EGI) with the support of NCG-INGRID-PT/INCD (Portugal). This work was produced with the support of INCD funded by FCT and FEDER under the project 01/SAICT/2016 n◦ 022153.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.urihttp://hdl.handle.net/10400.18/8070
dc.language.isoengpt_PT
dc.relationBiosystems & Integrative Sciences Institute
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-030-86230-5_46pt_PT
dc.subjectGraph Embeddingpt_PT
dc.subjectPersonalized Medicinept_PT
dc.subjectGene-disease Associationspt_PT
dc.titleBiomedical Knowledge Graph Embeddings for Personalized Medicinept_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleBiosystems & Integrative Sciences Institute
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FMulti%2F04046%2F2019/PT
oaire.citation.conferencePlace(Online)pt_PT
oaire.citation.title20th EPIA Conference on Artificial Intelligence (EPIA 2021), 7-9 September 2021pt_PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsembargoedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isProjectOfPublication35168786-8dfc-4a00-9759-dab3669fe1ae
relation.isProjectOfPublication.latestForDiscovery35168786-8dfc-4a00-9759-dab3669fe1ae

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