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Biomedical knowledge graph embeddings for personalized medicine: Predicting diseaseā€gene associations

2022-11-20Journal article Open access
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dc.contributor.authorVilela, Joana
dc.contributor.authorAsif, Muhammad
dc.contributor.authorMarques, Ana Rita
dc.contributor.authorSantos, JoĆ£o Xavier
dc.contributor.authorRasga, CĆ©lia
dc.contributor.authorVicente, Astrid
dc.contributor.authorMartiniano, Hugo
dc.date.accessioned2023-02-02T15:13:42Z
dc.date.available2023-02-02T15:13:42Z
dc.date.issued2022-11-20
dc.description.abstractPersonalized medicine is a concept that has been subject of increasing interest in medical research and practice in the last few years. However, significant challenges stand in the way of practical implementations, namely in regard to extracting clinically valuable insights from 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 (KG) as a path to reasoning over the wealth of information stored in publicly accessible databases. We built a Knowledge Graph using data from DisGeNET and GO, containing relationships between genes, diseases and other biological entities. The KG contains 93,657 nodes of 5 types and 1,705,585 relationships of 59 types. We applied KGE methods to this KG, obtaining an excellent performance in predicting gene-disease associations (MR 0.13, MRR 0.96, HITS@1 0.93, HITS@3 0.99, and HITS@10 0.99). The optimal hyperparameter set was used to predict all possible novel gene-disease associations. An in-depth analysis of novel gene-disease predictions for disease terms related to Autism Spectrum Disorder (ASD) shows that this approach produces predictions consistent with known candidate genes and biological pathways and yields relevant insights into the biology of this paradigmatic complex disorder.pt_PT
dc.description.sponsorshipFundaĆ§Ć£o para a CiĆŖncia e a Tecnologia, Grant/Award Numbers: SAICTPAC/0010/2015, POCI- 01-0145-FEDER-016428-PAC, EXPL/CCI-BIO/0126/2021, PTDC/MED-OUT/28937/2017, UIDP/04046/2020, UIDB/04046/2020; Fundo Europeu de Desenvolvimento Regional, Grant/Award Number: 022153pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationExpert Systems. 2022 Nov 20;e13181. doi: 10.1111/exsy.13181. Online ahead of print.pt_PT
dc.identifier.doi10.1111/exsy.13181pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.18/8498
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherWileypt_PT
dc.relationDeep graph learning approaches to personalized medicine
dc.relationBiosystems and Integrative Sciences Institute
dc.relationBiosystems and Integrative Sciences Institute
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13181pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAutism Spectrum Disorderpt_PT
dc.subjectGene-disease Associationspt_PT
dc.subjectKnowledge Graph Embeddingpt_PT
dc.subjectPersonalized Medicinept_PT
dc.subjectPerturbaƧƵes do Desenvolvimento Infantil e SaĆŗde Mentalpt_PT
dc.subjectAutismopt_PT
dc.titleBiomedical knowledge graph embeddings for personalized medicine: Predicting diseaseā€gene associationspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleDeep graph learning approaches to personalized medicine
oaire.awardTitleBiosystems and Integrative Sciences Institute
oaire.awardTitleBiosystems and Integrative Sciences Institute
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/9471 - RIDTI/SAICTPAC%2F0010%2F2015/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/EXPL%2FCCI-BIO%2F0126%2F2021/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FMED-OUT%2F28937%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04046%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04046%2F2020/PT
oaire.citation.endPage15pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleExpert Systemspt_PT
oaire.citation.volumee13181pt_PT
oaire.fundingStream9471 - RIDTI
oaire.fundingStream3599-PPCDT
oaire.fundingStream3599-PPCDT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
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project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundaĆ§Ć£o para a CiĆŖncia e a Tecnologia
project.funder.nameFundaĆ§Ć£o para a CiĆŖncia e a Tecnologia
project.funder.nameFundaĆ§Ć£o para a CiĆŖncia e a Tecnologia
project.funder.nameFundaĆ§Ć£o para a CiĆŖncia e a Tecnologia
project.funder.nameFundaĆ§Ć£o para a CiĆŖncia e a Tecnologia
rcaap.embargofctAcesso de acordo com polĆ­tica editorial da revista.pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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