COVID-19-assocaited Kidney Disease Analysis using Ontology and Bioinformatics – UROP Spring Symposium 2021

COVID-19-assocaited Kidney Disease Analysis using Ontology and Bioinformatics

Jennifer Roman

Jennifer Roman

Pronouns: she/her/hers

Research Mentor(s): Yongqun He, Associate Professor
Research Mentor School/College/Department: Lab Animal Medicine, Microbiology and Immunology, & Bioinformatics, Michigan Medicine
Presentation Date: Thursday, April 22, 2021
Session: Session 4 (2pm-2:50pm)
Breakout Room: Room 10
Presenter: 3

Event Link


Kidney diseases pose a major threat to human health. Human acute kidney injury (AKI) is a sudden and temporary loss of kidney function. This project examines the relation between AKI and COVID-19 in patients under different conditions. Using International Classification of Diseases (ICD) codes, the project aims to better understand COVID-19 on patients under different conditions. In the study, we are given the ICD and UBERON codes (Uber-anatomy ontology) in which we have to decode them and know the conditions associated with the ICD code to then transfer the term label and the information associated with it to an Ontology tree on the Protege application. The ICO Ontology (ICDO) codes are decoded on a Google Sheet and then transferred to the Protege application. When inputting the terms into the ontology on Protege, each group has a Parent Code which then produces the sub groups where they have the same first two numbers of the ICDO code to represent them being in the same group. This makes them easier to identify and input the information where it belongs within the Entities on Protege. The data from the Consortium for Clinical Characterization of COVID-19 by EHR (4CE) were used for our ontology modeling and analysis. Kidney related data were analyzed. Over 200 ICD codes were transferred and ontologized to the ICDO. Our study found COVID-19 targeted to diferent kideny parts. This project established a new ontology-based bioinformatics pipeline to systematically study COVID-19 related kidney disease data, supporting our better understanding of COVID-19 pathogenesis.

Authors: Jennifer Roman, Yongqun Oliver He
Research Method: Clinical Research

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