Impact of Clinical and Genomic Factors on COVID-19 Disease Severity.
Year: 2021
Type: article
Abstract: To date, there have been 180 million confirmed cases of COVID-19, with more than 3.8 million deaths, reported to WHO worldwide. In this paper we address the problem of understanding the host genome's influence, in concert with clinical variables, on the severity of COVID-19 manifestation in the patient. Leveraging positive-unlabeled machine learning algorithms coupled with RubricOE, a state-of-the-art genomic analysis framework, on UK BioBank data we extract novel insights on the complex interplay. The algorithm is also sensitive enough to detect the changing influence of the emergent B.1.1.7 SARS-CoV-2 (alpha) variant on disease severity, and, changing treatment protocols. The genomic component also implicates biological pathways that can help in understanding the disease etiology. Our work demonstrates that it is possible to build a robust and sensitive model despite significant bias, noise and incompleteness in both clinical and genomic data by a careful interleaving of clinical and genomic methodologies.
Source: PubMed Central
Authors: Sanjoy Dey, Aritra Bose, Subrata Saha, Prithwish Chakraborty, Mohamed Ghalwash, Aldo Guzm X E N-Sáenz, Filippo Utro, Kenney Ng, Jianying Hu, Laxmi Parida, Daby Sow
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4287541245