Abstract
The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.
Highlights
In late 2019, the Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the spread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARSCoV-2)
Two kinds of studies are conducted on different levels (Lv et al, 2020; McAloon et al, 2020): one is at the public health level, which includes the identification of pathogen, revealing the pathogen infection and transmission, and development of vaccines; the other is at the biological level, which includes revealing the biological mechanisms of pathogen infection, demonstrating the pathogenesis of infection-associated complications, and tracing the origin of the pathogen, such as in virus evolutionary studies
Regarding the recently reported transcriptomic data on 234 acute respiratory illnesses (ARIs) patients, which included 93 patients infected with SARSCOV-2, 100 patients with other viruses, and 41 patients with no viral infection, we employed several advanced machine learning algorithms on such data
Summary
In late 2019, the Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the spread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARSCoV-2). With the first confirmed case reported, the pandemic has rapidly spread all over the world, affecting 227 countries and territories. According to the summarized public health data of Sep 27, 2020, more than 9 million patients all over the world are still active (Dong et al, 2020a,b), making COVID19 one of the most severe and long-lasting pandemics affecting human beings in the 21st century. Given that COVID-19 triggered by SARS-CoV-2 infection is regarded as a worldwide pandemic disease, severely threatening human health, multiple studies on the pathogenesis of SARSCoV-2 have been conducted immediately after the spread of the disease (Lv et al, 2020). Lockdown of epidemic areas (Inoue and Todo, 2020; Lian et al, 2020) and wearing masks (Feng et al, 2020) are necessary for the control of SARS-CoV-2 spread, which have been confirmed to be effective in China
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