Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associations
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.18/8498 |
Resumo: | Personalized 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. |
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Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associationsAutism Spectrum DisorderGene-disease AssociationsKnowledge Graph EmbeddingPersonalized MedicinePerturbações do Desenvolvimento Infantil e Saúde MentalAutismoPersonalized 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.Fundação para a Ciência 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: 022153WileyRepositório Científico do Instituto Nacional de SaúdeVilela, JoanaAsif, MuhammadMarques, Ana RitaSantos, João XavierRasga, CéliaVicente, AstridMartiniano, Hugo2023-02-02T15:13:42Z2022-11-202022-11-20T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.18/8498engExpert Systems. 2022 Nov 20;e13181. doi: 10.1111/exsy.13181. Online ahead of print.10.1111/exsy.13181info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-20T15:42:36Zoai:repositorio.insa.pt:10400.18/8498Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:43:07.756177Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associations |
title |
Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associations |
spellingShingle |
Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associations Vilela, Joana Autism Spectrum Disorder Gene-disease Associations Knowledge Graph Embedding Personalized Medicine Perturbações do Desenvolvimento Infantil e Saúde Mental Autismo |
title_short |
Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associations |
title_full |
Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associations |
title_fullStr |
Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associations |
title_full_unstemmed |
Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associations |
title_sort |
Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associations |
author |
Vilela, Joana |
author_facet |
Vilela, Joana Asif, Muhammad Marques, Ana Rita Santos, João Xavier Rasga, Célia Vicente, Astrid Martiniano, Hugo |
author_role |
author |
author2 |
Asif, Muhammad Marques, Ana Rita Santos, João Xavier Rasga, Célia Vicente, Astrid Martiniano, Hugo |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Nacional de Saúde |
dc.contributor.author.fl_str_mv |
Vilela, Joana Asif, Muhammad Marques, Ana Rita Santos, João Xavier Rasga, Célia Vicente, Astrid Martiniano, Hugo |
dc.subject.por.fl_str_mv |
Autism Spectrum Disorder Gene-disease Associations Knowledge Graph Embedding Personalized Medicine Perturbações do Desenvolvimento Infantil e Saúde Mental Autismo |
topic |
Autism Spectrum Disorder Gene-disease Associations Knowledge Graph Embedding Personalized Medicine Perturbações do Desenvolvimento Infantil e Saúde Mental Autismo |
description |
Personalized 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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-11-20 2022-11-20T00:00:00Z 2023-02-02T15:13:42Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.18/8498 |
url |
http://hdl.handle.net/10400.18/8498 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Expert Systems. 2022 Nov 20;e13181. doi: 10.1111/exsy.13181. Online ahead of print. 10.1111/exsy.13181 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Wiley |
publisher.none.fl_str_mv |
Wiley |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132176798711808 |