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10.3389/fpubh.2022.926069 http://scihub22266oqcxt.onion/10.3389/fpubh.2022.926069 35812523!9256927!35812523     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Front+Public+Health 2022 ; 10 (ä): 926069 Nephropedia Template TP {{tp|p=35812523|t=2022. XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers |pdf=|usr=}}{{35812523}} gab.com Text XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers JO: Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=35812523 #FOAvip In December 2019, an outbreak of novel coronavirus pneumonia spread over Wuhan, Hubei Province, China, which then developed into a significant global health public event, giving rise to substantial economic losses. We downloaded throat swab expression profiling data of COVID-19 positive and negative patients from the Gene Expression Omnibus (GEO) database to mine novel diagnostic biomarkers. XGBoost was used to construct the model and select feature genes. Subsequently, we constructed COVID-19 classifiers such as MARS, KNN, SVM, MIL, and RF using machine learning methods. We selected the KNN classifier with the optimal MCC value from these classifiers using the IFS method to identify 24 feature genes. Finally, we used principal component analysis to classify the samples and found that the 24 feature genes could effectively be used to classify COVID-19-positive and negative patients. Additionally, we analyzed the possible biological functions and signaling pathways in which the 24 feature genes were involved by GO and KEGG enrichment analyses. The results demonstrated that these feature genes were primarily enriched in biological functions such as viral transcription and viral gene expression and pathways such as Coronavirus disease-COVID-19. In summary, the 24 feature genes we identified were highly effective in classifying COVID-19 positive and negative patients, which could serve as novel markers for COVID-19. Twit Text FOAVip XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers ????Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=35812523 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35812523 #FOAvip English Wikipedia <ref>{{Cite pmid|35812523}}</ref> XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers #MMPMID35812523 Song X; Zhu J; Tan X; Yu W; Wang Q; Shen D; Chen W Front Public Health 2022[]; 10 (ä): 926069 PMID35812523show ga In December 2019, an outbreak of novel coronavirus pneumonia spread over Wuhan, Hubei Province, China, which then developed into a significant global health public event, giving rise to substantial economic losses. We downloaded throat swab expression profiling data of COVID-19 positive and negative patients from the Gene Expression Omnibus (GEO) database to mine novel diagnostic biomarkers. XGBoost was used to construct the model and select feature genes. Subsequently, we constructed COVID-19 classifiers such as MARS, KNN, SVM, MIL, and RF using machine learning methods. We selected the KNN classifier with the optimal MCC value from these classifiers using the IFS method to identify 24 feature genes. Finally, we used principal component analysis to classify the samples and found that the 24 feature genes could effectively be used to classify COVID-19-positive and negative patients. Additionally, we analyzed the possible biological functions and signaling pathways in which the 24 feature genes were involved by GO and KEGG enrichment analyses. The results demonstrated that these feature genes were primarily enriched in biological functions such as viral transcription and viral gene expression and pathways such as Coronavirus disease-COVID-19. In summary, the 24 feature genes we identified were highly effective in classifying COVID-19 positive and negative patients, which could serve as novel markers for COVID-19. |*COVID-19/diagnosis[MESH] |*Pneumonia[MESH] |Biomarkers[MESH] |Humans[MESH] |Machine Learning[MESH]XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagno(epmc).pdf DeepDyve Pubget Overpricing