A graph-embedded topic model enables characterization of diverse pain phenotypes among UK biobank individuals.
bioinformatics
general medicine
health sciences
medical informatics
Journal
iScience
ISSN: 2589-0042
Titre abrégé: iScience
Pays: United States
ID NLM: 101724038
Informations de publication
Date de publication:
17 Jun 2022
17 Jun 2022
Historique:
received:
25
02
2022
revised:
08
04
2022
accepted:
06
05
2022
entrez:
31
5
2022
pubmed:
1
6
2022
medline:
1
6
2022
Statut:
epublish
Résumé
Large biobank repositories of clinical conditions and medications data open opportunities to investigate the phenotypic disease network. We present a graph embedded topic model (GETM). We integrate existing biomedical knowledge graph information in the form of pre-trained graph embedding into the embedded topic model. Via a variational autoencoder framework, we infer patient phenotypic mixture by modeling multi-modal discrete patient medical records. We applied GETM to UK Biobank (UKB) self-reported clinical phenotype data, which contains 443 self-reported medical conditions and 802 medications for 457,461 individuals. Compared to existing methods, GETM demonstrates good imputation performance. With a more focused application on characterizing pain phenotypes, we observe that GETM-inferred phenotypes not only accurately predict the status of chronic musculoskeletal (CMK) pain but also reveal known pain-related topics. Intriguingly, medications and conditions in the cardiovascular category are enriched among the most predictive topics of chronic pain.
Identifiants
pubmed: 35637735
doi: 10.1016/j.isci.2022.104390
pii: S2589-0042(22)00661-7
pmc: PMC9142639
doi:
Types de publication
Journal Article
Langues
eng
Pagination
104390Subventions
Organisme : Medical Research Council
ID : MC_PC_17228
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_QA137853
Pays : United Kingdom
Informations de copyright
© 2022 The Author(s).
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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