Elucidating the Mechanism of VVTT Infection Through Machine Learning and Transcriptome Analysis DOI Open Access
Zhili Chen,

Yongxin Jiang,

Jiazhen Cui

et al.

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(3), P. 1203 - 1203

Published: Jan. 30, 2025

The vaccinia virus (VV) is extensively utilized as a vaccine vector in the treatment of various infectious diseases, cardiovascular immunodeficiencies, and cancers. Tiantan strain (VVTT) has been instrumental an irreplaceable eradication smallpox China; however, it still presents significant adverse toxic effects. After WHO recommended that routine vaccination be discontinued, Chinese government stopped national program 1981. outbreak monkeypox 2022 focused people’s attention on Orthopoxvirus. However, there are limited reports safety side effects VVTT. In this study, we employed combination transcriptomic analysis machine learning-based feature selection to identify key genes implicated VVTT infection process. We four learning algorithms, including random forest (RF), minimum redundancy maximum relevance (MRMR), eXtreme Gradient Boosting (XGB), least absolute shrinkage operator cross-validation (LASSOCV), for selection. Among these, XGB was found most effective used further screening, resulting optimal model with ROC curve 0.98. Our revealed involvement pathways such spinocerebellar ataxia p53 signaling pathway. Additionally, identified three critical targets during infection—ARC, JUNB, EGR2—and validated these using qPCR. research elucidates mechanism by which infects cells, enhancing our understanding vaccine. This knowledge not only facilitates development new more vaccines but also contributes deeper comprehension viral pathogenesis. By advancing molecular mechanisms underlying infection, study lays foundation Such insights crucial strengthening global health security ensuring resilient response future pandemics.

Language: Английский

Elucidating the Mechanism of VVTT Infection Through Machine Learning and Transcriptome Analysis DOI Open Access
Zhili Chen,

Yongxin Jiang,

Jiazhen Cui

et al.

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(3), P. 1203 - 1203

Published: Jan. 30, 2025

The vaccinia virus (VV) is extensively utilized as a vaccine vector in the treatment of various infectious diseases, cardiovascular immunodeficiencies, and cancers. Tiantan strain (VVTT) has been instrumental an irreplaceable eradication smallpox China; however, it still presents significant adverse toxic effects. After WHO recommended that routine vaccination be discontinued, Chinese government stopped national program 1981. outbreak monkeypox 2022 focused people’s attention on Orthopoxvirus. However, there are limited reports safety side effects VVTT. In this study, we employed combination transcriptomic analysis machine learning-based feature selection to identify key genes implicated VVTT infection process. We four learning algorithms, including random forest (RF), minimum redundancy maximum relevance (MRMR), eXtreme Gradient Boosting (XGB), least absolute shrinkage operator cross-validation (LASSOCV), for selection. Among these, XGB was found most effective used further screening, resulting optimal model with ROC curve 0.98. Our revealed involvement pathways such spinocerebellar ataxia p53 signaling pathway. Additionally, identified three critical targets during infection—ARC, JUNB, EGR2—and validated these using qPCR. research elucidates mechanism by which infects cells, enhancing our understanding vaccine. This knowledge not only facilitates development new more vaccines but also contributes deeper comprehension viral pathogenesis. By advancing molecular mechanisms underlying infection, study lays foundation Such insights crucial strengthening global health security ensuring resilient response future pandemics.

Language: Английский

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