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XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers

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...

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Autores principales: Song, Xianbin, Zhu, Jiangang, Tan, Xiaoli, Yu, Wenlong, Wang, Qianqian, Shen, Dongfeng, Chen, Wenyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9256927/
https://www.ncbi.nlm.nih.gov/pubmed/35812523
http://dx.doi.org/10.3389/fpubh.2022.926069
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author Song, Xianbin
Zhu, Jiangang
Tan, Xiaoli
Yu, Wenlong
Wang, Qianqian
Shen, Dongfeng
Chen, Wenyu
author_facet Song, Xianbin
Zhu, Jiangang
Tan, Xiaoli
Yu, Wenlong
Wang, Qianqian
Shen, Dongfeng
Chen, Wenyu
author_sort Song, Xianbin
collection PubMed
description 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.
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spelling pubmed-92569272022-07-07 XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers Song, Xianbin Zhu, Jiangang Tan, Xiaoli Yu, Wenlong Wang, Qianqian Shen, Dongfeng Chen, Wenyu Front Public Health Public Health 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. Frontiers Media S.A. 2022-06-22 /pmc/articles/PMC9256927/ /pubmed/35812523 http://dx.doi.org/10.3389/fpubh.2022.926069 Text en Copyright © 2022 Song, Zhu, Tan, Yu, Wang, Shen and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Song, Xianbin
Zhu, Jiangang
Tan, Xiaoli
Yu, Wenlong
Wang, Qianqian
Shen, Dongfeng
Chen, Wenyu
XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers
title XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers
title_full XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers
title_fullStr XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers
title_full_unstemmed XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers
title_short XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers
title_sort xgboost-based feature learning method for mining covid-19 novel diagnostic markers
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9256927/
https://www.ncbi.nlm.nih.gov/pubmed/35812523
http://dx.doi.org/10.3389/fpubh.2022.926069
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