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Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods.
Chen, Lei; Li, Zhandong; Zeng, Tao; Zhang, Yu-Hang; Feng, KaiYan; Huang, Tao; Cai, Yu-Dong.
  • Chen L; School of Life Sciences, Shanghai University, shanghai 200444, China.
  • Li Z; College of Information Engineering, Shanghai Maritime University, shanghai 201306, China.
  • Zeng T; College of Food Engineering, Jilin Engineering Normal University, Changchun 130052, China.
  • Zhang YH; Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, shanghai 200031, China.
  • Feng K; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Huang T; Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou 510507, China.
  • Cai YD; Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, shanghai 200031, China.
Biomed Res Int ; 2021: 9939134, 2021.
Article in English | MEDLINE | ID: covidwho-1301740
ABSTRACT
COVID-19, a severe respiratory disease caused by a new type of coronavirus SARS-CoV-2, has been spreading all over the world. Patients infected with SARS-CoV-2 may have no pathogenic symptoms, i.e., presymptomatic patients and asymptomatic patients. Both patients could further spread the virus to other susceptible people, thereby making the control of COVID-19 difficult. The two major challenges for COVID-19 diagnosis at present are as follows (1) patients could share similar symptoms with other respiratory infections, and (2) patients may not have any symptoms but could still spread the virus. Therefore, new biomarkers at different omics levels are required for the large-scale screening and diagnosis of COVID-19. Although some initial analyses could identify a group of candidate gene biomarkers for COVID-19, the previous work still could not identify biomarkers capable for clinical use in COVID-19, which requires disease-specific diagnosis compared with other multiple infectious diseases. As an extension of the previous study, optimized machine learning models were applied in the present study to identify some specific qualitative host biomarkers associated with COVID-19 infection on the basis of a publicly released transcriptomic dataset, which included healthy controls and patients with bacterial infection, influenza, COVID-19, and other kinds of coronavirus. This dataset was first analysed by Boruta, Max-Relevance and Min-Redundancy feature selection methods one by one, resulting in a feature list. This list was fed into the incremental feature selection method, incorporating one of the classification algorithms to extract essential biomarkers and build efficient classifiers and classification rules. The capacity of these findings to distinguish COVID-19 with other similar respiratory infectious diseases at the transcriptomic level was also validated, which may improve the efficacy and accuracy of COVID-19 diagnosis.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Testing / COVID-19 Type of study: Diagnostic study / Prognostic study / Qualitative research Limits: Humans Language: English Journal: Biomed Res Int Year: 2021 Document Type: Article Affiliation country: 2021

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Testing / COVID-19 Type of study: Diagnostic study / Prognostic study / Qualitative research Limits: Humans Language: English Journal: Biomed Res Int Year: 2021 Document Type: Article Affiliation country: 2021