Jinyang Du
Pronouns: He/him/his
Research Mentor(s): Yongqun He
Co-Presenter:
Research Mentor School/College/Department: Lab Animal Medicine, Microbiology and Immunology, & Bioinformatics / Medicine
Presentation Date: April 20
Presentation Type: Oral5
Session: Session 3 – 1:40pm – 2:30 pm
Room: Breakout Room 6
Authors: Jinyang Du, Yongqun He
Presenter: 6
Abstract
Till January 2022, the global COVID-19 pandemic has caused more than 300 million individuals to get infected and has taken away more than 5.5 million people’s lives. Additionally, as an RNA virus, the COVID-19 can relatively easily mutate, producing some variants that could escape the immune system even with vaccinations. How to live with COVID-19 and finding a treatment have become an inevitable question. Taken inspiration from the cocktail treatment and ontology, this study aims to locate effective drugs and repurpose them into the COVID-19 treatment. Looking through 332 high-confidence protein-protein interactions, 66 druggable human protein targets are identified (Gordon et. al, 2020). This project takes this information. Using BioGRID and Ontorat, detailed interaction types are examined and converted into an ontology. Multiple ontology databases such as OGG, ChEBI, and PR are also utilized. Within the ontology, more than 66 drugs that could potentially become a treatment for COVID-19 are identified and added to CIDO. This gives us an idea of developing a cocktail treatment for COVID-19 with the help of machine learning. Using OPA2Vec, ontology can be transformed into data that can be processed by machine learning models, which could significantly increase the speed of identification. Future clinical experiments are required for this study for further validation.
Engineering, Interdisciplinary, Natural/Life Sciences
