Utilizing Kidney Ontology for Data-Driven Exploration of Potential Biomarkers in Kidney Diseases: Introducing the Kidney Diseases Biomarker Ontology (KDBO)

This study proposes a new method of identifying possible kidney disease biomarkers using Kidney Diseases Biomarker Ontology (KDBO).This study classically merges clinical imaging, biopsy data, proteomics, and genomic data derived from various sources through the Kidney Development Subontology (KDSO) of Gene Ontology (GO).Machine learning algorithms, network analysis, and statistical approaches are used to identify novel biomarkers that have implications for renal impairment at different stages of the disease have been employed here.A comparison between the new upstart markers found and the available right markers was made in patient cohorts to confirm their accuracy.The study showed that employing kidney ontology improves diagnosis, prognosis, and treatment by facilitating earlier identification of dysfunction, precise evaluation of disease severity levels, tailored choice therapy, addressing issues related to renal ontologies, and presenting suggestions for future studies.