Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine.
Document Type
Article
Publication Date
5-22-2024
Original Citation
He Y,
Barisoni L,
Rosenberg A,
Robinson P,
Diehl A,
Chen Y,
Phuong J,
Hansen J,
Herr Ii B,
Börner K,
Schaub J,
Bonevich N,
Arnous G,
Boddapati S,
Zheng J,
Alakwaa F,
Sardar P,
Duncan W,
Liang C,
Valerius M,
Jain S,
Iyengar R,
Himmelfarb J,
Kretzler M,
.
Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine. AMIA Annu Symp Proc. 2024;2024:523-32.
Keywords
JGM, Humans, Precision Medicine, Biological Ontologies, Acute Kidney Injury, Kidney, Biomarkers, Renal Insufficiency, Chronic, Gene Expression Profiling
JAX Source
AMIA Annu Symp Proc. 2024;2024:523-32.
ISSN
1942-597X
PMID
40417545
Abstract
Many data resources generate, process, store, or provide kidney related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data integration. The Kidney Precision Medicine Project (KPMP) team contributed to the representation and addition of 329 kidney phenotype terms to the Human Phenotype Ontology (HPO), and identified many subcategories of acute kidney injury (AKI) or chronic kidney disease (CKD). The Kidney Tissue Atlas Ontology (KTAO) imports and integrates kidney-related terms from existing ontologies (e.g., HPO, CL, and Uberon) and represents 259 kidney-related biomarkers. We have also developed a precision medicine metadata ontology (PMMO) to integrate 50 variables from KPMP and CZ CellxGene data resources and applied PMMO for integrative kidney data analysis. The gene expression profiles of kidney gene biomarkers were specifically analyzed under healthy control or AKI/CKD disease states. This work demonstrates how ontology-based approaches support multi-domain data and knowledge integration in precision medicine.