Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Dec 22, 2020
Date Accepted: Apr 27, 2021
A Biomedical Knowledge Graph System to Propose Mechanistic Hypotheses for Real-world Environmental Health Observations: Application
ABSTRACT
Background:
Knowledge graphs are a common form of knowledge representation in biomedicine and many other fields. We developed an open biomedical knowledge graph–based system termed Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways, or ROBOKOP. ROBOKOP consists of both a front-end user interface and a back-end knowledge graph. The ROBOKOP user interface allows users to posit questions and explore answer subgraphs. Users can also posit questions through direct Cypher query of the underlying knowledge graph, which currently contains roughly 6M nodes or biomedical entities and 140M edges or predicates describing the relationship between nodes, drawn from >30 curated data sources.
Objective:
We aimed to apply ROBOKOP to survey data on workplace exposures and immune-medicated diseases from the Environmental Polymorphisms Registry (EPR) within the National Institute of Environmental Health Sciences.
Methods:
We analyzed EPR survey data focused on immune-mediated diseases and identified 45 associations between chemical workplace exposures and immune-mediated diseases, as self-reported by study participants (N = 4574), with 20 associations significant at P < .05 after a false discovery rate connection. We then used ROBOKOP to: (1) validate the associations by determining whether plausible connections exist within the ROBOKOP knowledge graph; and (2) propose biological mechanisms that might explain them and serve as hypotheses for subsequent testing. We highlight three exemplar associations: carbon monoxide – multiple sclerosis; ammonia – asthma; and isopropanol – allergic disease.
Results:
ROBOKOP successfully returned answer sets for three queries that were posed in the context of the driving examples. The answer sets included potential intermediary genes, as well as supporting evidence that might explain the observed associations.
Conclusions:
We demonstrate a real-world application of ROBOKOP to generate mechanistic hypotheses for associations between chemical workplace exposure and immune-mediates diseases. We expect that ROBOKOP will find broad application across many biomedical fields and other scientific disciplines due to its generalizability, speed to discovery and generation of mechanistic hypotheses, and open nature.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.