Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods.
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 ot...
| Publicado en: | BioMed Research International pp. 1 - 12 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/7/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151269908&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151269908 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/7/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 151269908 151269908 151269908 10.1155/2021/9939134 151269908 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods. aug: au: Chen, Lei Li, Zhandong Zeng, Tao Zhang, Yu-Hang Feng, KaiYan Huang, Tao Cai, Yu-Dong affil: School of Life Sciences, Shanghai University, shanghai 200444, China sug: subj: COVID-19 Diagnosis Gene Expression Profiling Biological Markers Machine Learning Methods Human Comparative Studies Algorithms ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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