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Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction
BACKGROUND: Increasing evidence suggests that people with Coronavirus Disease 2019 (COVID-19) have a much higher prevalence of Acute Myocardial Infarction (AMI) than the general population. However, the underlying mechanism is not yet comprehended. Therefore, our study aims to explore the potential...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10090501/ https://www.ncbi.nlm.nih.gov/pubmed/37065165 http://dx.doi.org/10.3389/fmicb.2023.1153106 |
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author | Liu, Ying Zhou, Shujing Wang, Longbin Xu, Ming Huang, Xufeng Li, Zhengrui Hajdu, Andras Zhang, Ling |
author_facet | Liu, Ying Zhou, Shujing Wang, Longbin Xu, Ming Huang, Xufeng Li, Zhengrui Hajdu, Andras Zhang, Ling |
author_sort | Liu, Ying |
collection | PubMed |
description | BACKGROUND: Increasing evidence suggests that people with Coronavirus Disease 2019 (COVID-19) have a much higher prevalence of Acute Myocardial Infarction (AMI) than the general population. However, the underlying mechanism is not yet comprehended. Therefore, our study aims to explore the potential secret behind this complication. MATERIALS AND METHODS: The gene expression profiles of COVID-19 and AMI were acquired from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed genes (DEGs) shared by COVID-19 and AMI, we conducted a series of bioinformatics analytics to enhance our understanding of this issue. RESULTS: Overall, 61 common DEGs were filtered out, based on which we established a powerful diagnostic predictor through 20 mainstream machine-learning algorithms, by utilizing which we could estimate if there is any risk in a specific COVID-19 patient to develop AMI. Moreover, we explored their shared implications of immunology. Most remarkably, through the Bayesian network, we inferred the causal relationships of the essential biological processes through which the underlying mechanism of co-pathogenesis between COVID-19 and AMI was identified. CONCLUSION: For the first time, the approach of causal relationship inferring was applied to analyzing shared pathomechanism between two relevant diseases, COVID-19 and AMI. Our findings showcase a novel mechanistic insight into COVID-19 and AMI, which may benefit future preventive, personalized, and precision medicine. |
format | Online Article Text |
id | pubmed-10090501 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100905012023-04-13 Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction Liu, Ying Zhou, Shujing Wang, Longbin Xu, Ming Huang, Xufeng Li, Zhengrui Hajdu, Andras Zhang, Ling Front Microbiol Microbiology BACKGROUND: Increasing evidence suggests that people with Coronavirus Disease 2019 (COVID-19) have a much higher prevalence of Acute Myocardial Infarction (AMI) than the general population. However, the underlying mechanism is not yet comprehended. Therefore, our study aims to explore the potential secret behind this complication. MATERIALS AND METHODS: The gene expression profiles of COVID-19 and AMI were acquired from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed genes (DEGs) shared by COVID-19 and AMI, we conducted a series of bioinformatics analytics to enhance our understanding of this issue. RESULTS: Overall, 61 common DEGs were filtered out, based on which we established a powerful diagnostic predictor through 20 mainstream machine-learning algorithms, by utilizing which we could estimate if there is any risk in a specific COVID-19 patient to develop AMI. Moreover, we explored their shared implications of immunology. Most remarkably, through the Bayesian network, we inferred the causal relationships of the essential biological processes through which the underlying mechanism of co-pathogenesis between COVID-19 and AMI was identified. CONCLUSION: For the first time, the approach of causal relationship inferring was applied to analyzing shared pathomechanism between two relevant diseases, COVID-19 and AMI. Our findings showcase a novel mechanistic insight into COVID-19 and AMI, which may benefit future preventive, personalized, and precision medicine. Frontiers Media S.A. 2023-03-29 /pmc/articles/PMC10090501/ /pubmed/37065165 http://dx.doi.org/10.3389/fmicb.2023.1153106 Text en Copyright © 2023 Liu, Zhou, Wang, Xu, Huang, Li, Hajdu and Zhang. 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 | Microbiology Liu, Ying Zhou, Shujing Wang, Longbin Xu, Ming Huang, Xufeng Li, Zhengrui Hajdu, Andras Zhang, Ling Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction |
title | Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction |
title_full | Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction |
title_fullStr | Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction |
title_full_unstemmed | Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction |
title_short | Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction |
title_sort | machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between covid-19 and acute myocardial infarction |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10090501/ https://www.ncbi.nlm.nih.gov/pubmed/37065165 http://dx.doi.org/10.3389/fmicb.2023.1153106 |
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