Prevention and treatment of COVID-19 based on post-genomic pharmacological analysis: Systematic computer analysis of 290,000 scientific articles on COVID-19

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Abstract

The COVID-19 pandemic has highlighted pressing challenges in biomedical research methodology. It has become obvious that the rapid and effective development of treatments for “new” viral infections is impossible without the coordination of interdisciplinary research and in-depth analysis of data obtained within the framework of the post-genomic paradigm. Presents the results of a systematic computer analysis of 290,000 scientific articles on COVID-19, with an emphasis on the results of post-genomic studies of SARS-CoV-2. The futility of the overly simplified approach, which considers only one “most important receptor protein”, only one “key virus gene”, etc., is shown. It is shown how post-genomic technologies will make it possible to find informative biomarkers of severe coronavirus infection, including those based on complex immune disorders associated with COVID-19.

About the authors

Ivan Yu. Torshin

Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences

Email: tiy135@gmail.com
ORCID iD: 0000-0002-2659-7998

канд. физ.-мат. наук, канд. хим. наук, вед. науч. сотр. ФИЦ ИУ РАН

Russian Federation, Moscow

Olga A. Gromova

Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences

Author for correspondence.
Email: unesco.gromova@gmail.com
ORCID iD: 0000-0002-7663-710X
SPIN-code: 6317-9833

д-р мед. наук, проф., вед. науч. сотр. ФИЦ ИУ РАН

Russian Federation, Moscow

Alexander G. Chuchalin

Pirogov Russian National Research Medical University

Email: pulmomoskva@mail.ru
ORCID iD: 0000-0002-5070-5450

акад. РАН, д-р мед. наук, проф., зав. каф. госпитальной терапии педиатрического фак-та ФГАОУ ВО «РНИМУ им. Н.И. Пирогова»

Russian Federation, Moscow

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Supplementary files

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2. Fig. 1. Metric diagram showing a map of the molecular pathophysiology of COVID-19. The distance between the points corresponding to the terms is inversely proportional to the joint occurrence of the terms in the studied sample of publications (the closer the two arbitrary points, the more common the joint use of the two corresponding terms).

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3. Fig. 2. Predictors for distinguishing between sepsis and COVID-19, including plasma proteins, results of whole blood total RNA sequencing, and cytometric parameters.

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