ArticleBioinformatics (Oxford, England)2023
Discovering comorbid diseases using an inter-disease interactivity network based on biobank-scale PheWAS data.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical spectrum of Transthyretin amyloidogenic mutations among diverse population origins.Human genomics · 2024Pooled it
- PMBB Geno-Pheno Toolkit: A suite of scalable, reproducible pipelines for cross-biobank association analyses.bioRxiv : the preprint server for biology · 2026Article
- Graph transformer with disease subgraph positional encoding for improved comorbidity prediction.Quantitative biology (Beijing, China) · 2025Article
- Phenome-wide comorbidity network analysis reveals clinical risk patterns in enthesopathy and enthesitis.medRxiv : the preprint server for health sciences · 2025Article
- The interplay of sex and genotype in disease associations: a comprehensive network analysis in the UK Biobank.Human genomics · 2025Article
- Shared genetics between breast cancer and predisposing diseases identifies novel breast cancer treatment candidates.Human genomics · 2024Article
- Decoding the genetic comorbidity network of Alzheimer's disease.BioData mining · 2024Article
- Shared genetics between breast cancer and predisposing diseases identifies novel breast cancer treatment candidates.Research square · 2024Article
- Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network.Bioinformatics (Oxford, England) · 2024Article
- Article
- The Human Extracellular Matrix Diseasome Reveals Genotype-Phenotype Associations with Clinical Implications for Age-Related Diseases.Biomedicines · 2023Article
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Authors and funding
11 authors.
Funding
Abstract
motivationUnderstanding comorbidity is essential for disease prevention, treatment and prognosis. In particular, insight into which pairs of diseases are likely or unlikely to co-occur may help elucidate the potential relationships between complex diseases. Here, we introduce the use of an inter-disease interactivity network to discover/prioritize comorbidities. Specifically, we determine disease associations by accounting for the direction of effects of genetic components shared between diseases, and categorize those associations as synergistic or antagonistic. We further develop a comorbidity scoring algorithm to predict whether diseases are more or less likely to co-occur in the presence of a given index disease. This algorithm can handle networks that incorporate relationships with opposite signs.
resultsWe finally investigate inter-disease associations among 427 phenotypes in UK Biobank PheWAS data and predict the priority of comorbid diseases. The predicted comorbidities were verified using the UK Biobank inpatient electronic health records. Our findings demonstrate that considering the interaction of phenotype associations might be helpful in better predicting comorbidity. AVAILABILITY AND IMPLEMENTATION: The source code and data of this study are available at https://github.com/dokyoonkimlab/DiseaseInteractiveNetwork. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.