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10.1155/2021/9939134 http://scihub22266oqcxt.onion/10.1155/2021/9939134 34307679!8272456!34307679     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Res+Int 2021 ; 2021 (ä): 9939134 Nephropedia Template TP {{tp|p=34307679|t=2021. Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods |pdf=|usr=}}{{34307679}} gab.com Text Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=34307679 #FOAvip 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. Twit Text FOAVip Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=34307679 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34307679 #FOAvip English Wikipedia <ref>{{Cite pmid|34307679}}</ref> Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods #MMPMID34307679 Chen L; Li Z; Zeng T; Zhang YH; Feng K; Huang T; Cai YD Biomed Res Int 2021[]; 2021 (ä): 9939134 PMID34307679show ga 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. |Biomarkers/analysis[MESH] |COVID-19 Testing/*methods[MESH] |COVID-19/blood/*diagnosis/*genetics[MESH] |Databases, Genetic[MESH] |Gene Expression Profiling/methods[MESH] |Humans[MESH] |Influenza, Human[MESH] |Machine Learning[MESH] |Mass Screening/methods[MESH] |Models, Theoretical[MESH] |Respiratory Tract Infections/blood/diagnosis[MESH] |SARS-CoV-2/genetics/pathogenicity[MESH]Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine L(epmc).pdf DeepDyve Pubget Overpricing