Identification of hub DE-SRGs.

A. Volcano plot of DEGs, with gene symbols of hub DE-SRGs labeled and red lines illustrating the PPI between them. The size of each point indicates the gene’s importance within the PPI network. B. GSEA analysis demonstrating senescence-related pathways...

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Bibliographic Details
Main Author:	Xihao Shen (20347942) (author)
Other Authors:	Jiyue Wu (3814717) (author), Feilong Zhang (420300) (author), Qing Bi (3864823) (author), Zejia Sun (10031646) (author), Wei Wang (17594) (author)
Published:	2024
Subjects:	Cell Biology Genetics Molecular Biology Neuroscience Biotechnology Developmental Biology Marine Biology Cancer Virology Computational Biology weighted gene co two clusters underscored multiple established databases machine learning algorithms integrative machine learning gene expression omnibus demographic shift towards consensus clustering algorithm b >(</ b aged donor kidneys term graft outcomes expression network analysis differential expression analysis comprehensive analysis underscores worse graft survival reduced graft survival predicting graft survival two rejection clusters rejection remains elusive optimal acute rejection delayed graft function ktx rejection poses ktx rejection occurrence identify predictive srgs invasive diagnostic model kidney transplant rejection graft survival ktx rejection kidney graft diagnostic model kidney rejection gsva analysis cluster analysis allograft function rejection samples diagnosing rejection prognostic model kidney transplantation xlink "> wgcna ), significant threat related genes provided microarray positive correlation level landscape ischemic damage detailed cellular conducted using conclusions drawn cluster c1 c2 ).
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Identification of hub DE-SRGs.

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