Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells
Corona Virus Disease 2019 (COVID-19) not only causes respiratory system damage, but also imposes strain on the cardiovascular system. Vascular endothelial cells and cardiomyocytes play an important role in cardiac function. The aberrant expression of genes in vascular endothelial cells and cardiomyo...
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| Format: | Article |
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MDPI AG
2023-04-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/13/4/1011 |
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| author | Yaochen Xu Qinglan Ma Jingxin Ren Lei Chen Wei Guo Kaiyan Feng Zhenbing Zeng Tao Huang Yudong Cai |
| author_facet | Yaochen Xu Qinglan Ma Jingxin Ren Lei Chen Wei Guo Kaiyan Feng Zhenbing Zeng Tao Huang Yudong Cai |
| author_sort | Yaochen Xu |
| collection | DOAJ |
| description | Corona Virus Disease 2019 (COVID-19) not only causes respiratory system damage, but also imposes strain on the cardiovascular system. Vascular endothelial cells and cardiomyocytes play an important role in cardiac function. The aberrant expression of genes in vascular endothelial cells and cardiomyocytes can lead to cardiovascular diseases. In this study, we sought to explain the influence of respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the gene expression levels of vascular endothelial cells and cardiomyocytes. We designed an advanced machine learning-based workflow to analyze the gene expression profile data of vascular endothelial cells and cardiomyocytes from patients with COVID-19 and healthy controls. An incremental feature selection method with a decision tree was used in building efficient classifiers and summarizing quantitative classification genes and rules. Some key genes, such as MALAT1, MT-CO1, and CD36, were extracted, which exert important effects on cardiac function, from the gene expression matrix of 104,182 cardiomyocytes, including 12,007 cells from patients with COVID-19 and 92,175 cells from healthy controls, and 22,438 vascular endothelial cells, including 10,812 cells from patients with COVID-19 and 11,626 cells from healthy controls. The findings reported in this study may provide insights into the effect of COVID-19 on cardiac cells and further explain the pathogenesis of COVID-19, and they may facilitate the identification of potential therapeutic targets. |
| first_indexed | 2024-03-11T04:50:21Z |
| format | Article |
| id | doaj.art-bdd5b1ce183140d7a5be3a09bb635b47 |
| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-11T04:50:21Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-bdd5b1ce183140d7a5be3a09bb635b472023-11-17T20:06:52ZengMDPI AGLife2075-17292023-04-01134101110.3390/life13041011Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial CellsYaochen Xu0Qinglan Ma1Jingxin Ren2Lei Chen3Wei Guo4Kaiyan Feng5Zhenbing Zeng6Tao Huang7Yudong Cai8Department of Mathematics, School of Sciences, Shanghai University, Shanghai 200444, ChinaSchool of Life Sciences, Shanghai University, Shanghai 200444, ChinaSchool of Life Sciences, Shanghai University, Shanghai 200444, ChinaCollege of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaKey Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) & Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai 200030, ChinaDepartment of Computer Science, Guangdong AIB Polytechnic College, Guangzhou 510507, ChinaDepartment of Mathematics, School of Sciences, Shanghai University, Shanghai 200444, ChinaBio-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, ChinaDepartment of Mathematics, School of Sciences, Shanghai University, Shanghai 200444, ChinaCorona Virus Disease 2019 (COVID-19) not only causes respiratory system damage, but also imposes strain on the cardiovascular system. Vascular endothelial cells and cardiomyocytes play an important role in cardiac function. The aberrant expression of genes in vascular endothelial cells and cardiomyocytes can lead to cardiovascular diseases. In this study, we sought to explain the influence of respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the gene expression levels of vascular endothelial cells and cardiomyocytes. We designed an advanced machine learning-based workflow to analyze the gene expression profile data of vascular endothelial cells and cardiomyocytes from patients with COVID-19 and healthy controls. An incremental feature selection method with a decision tree was used in building efficient classifiers and summarizing quantitative classification genes and rules. Some key genes, such as MALAT1, MT-CO1, and CD36, were extracted, which exert important effects on cardiac function, from the gene expression matrix of 104,182 cardiomyocytes, including 12,007 cells from patients with COVID-19 and 92,175 cells from healthy controls, and 22,438 vascular endothelial cells, including 10,812 cells from patients with COVID-19 and 11,626 cells from healthy controls. The findings reported in this study may provide insights into the effect of COVID-19 on cardiac cells and further explain the pathogenesis of COVID-19, and they may facilitate the identification of potential therapeutic targets.https://www.mdpi.com/2075-1729/13/4/1011COVID-19cardiovascular diseasecardiomyocytesvascular endothelial cellmachine learning |
| spellingShingle | Yaochen Xu Qinglan Ma Jingxin Ren Lei Chen Wei Guo Kaiyan Feng Zhenbing Zeng Tao Huang Yudong Cai Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells Life COVID-19 cardiovascular disease cardiomyocytes vascular endothelial cell machine learning |
| title | Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells |
| title_full | Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells |
| title_fullStr | Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells |
| title_full_unstemmed | Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells |
| title_short | Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells |
| title_sort | using machine learning methods in identifying genes associated with covid 19 in cardiomyocytes and cardiac vascular endothelial cells |
| topic | COVID-19 cardiovascular disease cardiomyocytes vascular endothelial cell machine learning |
| url | https://www.mdpi.com/2075-1729/13/4/1011 |
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