Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests
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- Oregon State University, Corvallis, OR (United States); University of Chicago, IL (United States)
- Università degli Studi di Milano (Italy); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); European Laboratory for Learning and Intelligent Systems (ELLIS)
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- National Institute of Environmental Health Sciences (NIEHS), Durham, NC (United States)
- University of Colorado Anschutz Medical Campus, Aurora, CO (United States)
- Università degli Studi di Milano (Italy); European Laboratory for Learning and Intelligent Systems (ELLIS)
- European Laboratory for Learning and Intelligent Systems (ELLIS); The Jackson Laboratory for Genomic Medicine, Farmington, CT (United States)
- University of North Carolina, Chapel Hill, NC (United States)
Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (for example, endometriosis, ovarian cyst, and uterine fibroids). We harmonized survey data from the Personalized Environment and Genes Study (PEGS) on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison. Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant or suggestive predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures. Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal but can support hypothesis generation. This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE; National Institutes of Health (NIH)
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 2470921
- Journal Information:
- International Journal of Medical Informatics, Journal Name: International Journal of Medical Informatics Journal Issue: C Vol. 187; ISSN 1386-5056
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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