Five Critical Genes Related to Seven COVID-19 Subtypes: A Data Science Discovery

Zhang, Zhengjun

doi:10.6339/21-JDS1005

Journal of Data Science

Five Critical Genes Related to Seven COVID-19 Subtypes: A Data Science Discovery

Volume 19, Issue 1 (2021), pp. 142–150

Zhengjun Zhang

https://doi.org/10.6339/21-JDS1005

Pub. online: 3 February 2021 Type: Data Science In Action

Received
1 December 2020

Accepted
1 January 2021

Published
3 February 2021

Abstract

Since the first confirmed case of COVID-19 was identified in December 2019, the total COVID-19 patients are up to 80,675,745, and the number of deaths is 1,764,185 as of December 27, 2020. The problem is that researchers are still learning about it, and new variants of SARS-CoV-2 are not stopping. For medical treatment, essential and informative genes can lead to accurate tests of whether an individual has contracted COVID-19 and help develop highly efficient vaccines, antiviral drugs, and treatments. As a result, identifying critical genes related to COVID-19 has been an urgent task for medical researchers. We conducted a competing risk analysis using the max-linear logistic regression model to analyze 126 blood samples from COVID-19-positive and COVID-19-negative patients. Our research led to a competing COVID-19 risk classifier derived from 19,472 genes and their differential expression values. The final classifier model only involves five critical genes, ABCB6, KIAA1614, MND1, SMG1, RIPK3, which led to 100% sensitivity and 100% specificity of the 126 samples. Given their 100% accuracy in predicting COVID-19 positive or negative status, these five genes can be critical in developing proper, focused, and accurate COVID-19 testing procedures, guiding the second-generation vaccine development, studying antiviral drugs and treatments. It is expected that these five genes can motivate numerous new COVID-19 researches.

Supplementary material

Supplementary Material

Outcome Table 1 is in a supplementary file available online. A Matlab^® demo code for solving Equation (4) is also available.

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Keywords

classification competing risk COVID-19 test COVID-19 treatment COVID-19 vaccine gene-gene interaction

Funding

The work was partially supported by NSF-DMS-2012298 (NSF).

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