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. 2021 May 24:6:683212.
doi: 10.3389/frma.2021.683212. eCollection 2021.

Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics

Mengjia Wu  1 , Yi Zhang  1 , Mark Grosser  2 , Steven Tipper  2 , Deon Venter  2 , Hua Lin  2 , Jie Lu  1
Affiliations

Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics

Mengjia Wu et al. Front Res Metr Anal. .

Abstract

The COVID-19 pandemic constitutes an ongoing worldwide threat to human society and has caused massive impacts on global public health, the economy and the political landscape. The key to gaining control of the disease lies in understanding the genetics of SARS-CoV-2 and the disease spectrum that follows infection. This study leverages traditional and intelligent bibliometric methods to conduct a multi-dimensional analysis on 5,632 COVID-19 genetic research papers, revealing that 1) the key players include research institutions from the United States, China, Britain and Canada; 2) research topics predominantly focus on virus infection mechanisms, virus testing, gene expression related to the immune reactions and patient clinical manifestation; 3) studies originated from the comparison of SARS-CoV-2 to previous human coronaviruses, following which research directions diverge into the analysis of virus molecular structure and genetics, the human immune response, vaccine development and gene expression related to immune responses; and 4) genes that are frequently highlighted include ACE2, IL6, TMPRSS2, and TNF. Emerging genes to the COVID-19 consist of FURIN, CXCL10, OAS1, OAS2, OAS3, and ISG15. This study demonstrates that our suite of novel bibliometric tools could help biomedical researchers follow this rapidly growing field and provide substantial evidence for policymakers' decision-making on science policy and public health administration.

Keywords: COVID-19; bibliometrics; genetic research; knowledge discovery; network analytics.

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Conflict of interest statement

HL, MG, ST, DV are employees of Strands Pty Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The research framework.
FIGURE 2
FIGURE 2
Monthly trend of the number of publications.
FIGURE 3
FIGURE 3
The co-authorship network of research institutions (normalized by Jaccard Coefficient).
FIGURE 4
FIGURE 4
The co-occurrence network of scientific terms.
FIGURE 5
FIGURE 5
The SEP on COVID-19 genetic research between January 2020 and April 2021.
FIGURE 6
FIGURE 6
Top 10 genes (A), co-morbidities (B) and chemicals (C) in COVID-19 genetic research between December 2019 and January 2021.
FIGURE 7
FIGURE 7
Emerging gene discovery for COVID-19 genetic research.
FIGURE 8
FIGURE 8
Emerging gene discovery for COVID-19 genetic research–detailed partial view. Note: This figure is a zoom-in map for the red broken-line box in Figure 7.
See this image and copyright information in PMC

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