Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project

Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project

September 16, 2020

Ontologies are powerful tools that facilitate the integration of large and disparate data sets. Here, researchers from the Kidney Precision Medicine Project provide an introduction to ontologies, including those developed by the consortium, describing how these will be used to improve the annotation of kidney-relevant data, eventually leading to new definitions of kidney disease in support of precision medicine.

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KPMP ontology lead Oliver He (University of Michigan) notes that the KPMP ontology work is important because it established a robust and feasible platform to standardize and integrate heterogeneous big data related to the kidney. This supports productive and reproducible kidney precision medicine research.  The work covers two main interoperable ontologies, Kidney Tissue Atlas Ontology (KTAO) and Ontology of Precision Medicine & Investigation (OPMI), generated out of KPMP research.

KTAO standardizes, represents, and semantically integrates kidney tissue, cell types, genes, proteins, phenotypes, and diseases together. The results can be systematically represented using a computer-interpretable way, supporting advanced computer-assisted reasoning and AI analysis. OPMI and KTAO also standardize the metadata types obtained from KPMP.

When asked about the purpose of KPMP ontology work, Dr. He responds, "With our ontology support, the heterogeneous kidney data coming from KPMP and other organizations can be efficiently  integrated and analyzed together. Such integration is a huge challenge.  Our KTAO and OPMI ontologies are developed to effectively address this big challenge."