| Title: | A graph-embedded topic model enables characterization of diverse pain phenotypes among UK biobank individuals |
| Journal: | iScience |
| Published: | 12 May 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35637735/ |
| DOI: | https://doi.org/10.1016/j.isci.2022.104390 |
| URL: | http://www.cell.com/article/S2589004222006617/pdf |
| Citations: | 9 (6 in last 2 years) as of 8 Aug 2024 |
Publication 7131
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Abstract
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.5 Authors
- Yuening Wang
- Rodrigo Benavides
- Luda Diatchenko
- Audrey V. Grant
- Yue Li
1 Application
| Application ID | Title |
|---|---|
| 20802 | A replication study of pain interactions with comorbidities |
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