Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

• Published in: Neural regeneration research Vol. 19; no. 12; pp. 2723 - 2734
• Main Authors: Li, Xiaolu, Yang, Ye, Xu, Senming, Gui, Yuchang, Chen, Jianmin, Xu, Jianwen
• Format: Journal Article
• Language: English
• Published: India Wolters Kluwer - Medknow 01.12.2024; Medknow Publications & Media Pvt. Ltd; Department of Rehabilitation Medicine,The First Affiliated Hospital of Guangxi Medical University,Nanning,Guangxi Zhuang Autonomous Region,China%Department of Rehabilitation Medicine,Guilin People's Hospital,Guilin,Guangxi Zhuang Autonomous Region,China%Department of Rehabilitation Medicine,The First Affiliated Hospital of Fujian Medical University,Fuzhou,Fujian Province,China; Wolters Kluwer Medknow Publications
• Edition: 2
• Subjects: bioinformatics analysis; biomarker; cibersort; geo dataset; lasso; mirna-mrna network; rna sequencing; spinal cord injury; svm-rfe; weighted gene co-expression network analysis; Biomarkers; Genes; Machine learning; MicroRNAs; Research Article; Spinal cord injuries; T cell receptors; RNA sequencing; spinal cord injury; weighted gene co-expression network analysis; CIBERSORT; SVM-RFE; LASSO; GEO dataset; miRNA-mRNA network; biomarker; bioinformatics analysis

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal cord injury. They can greatly affect nerve regeneration and functional recovery. However, there is still limited understanding of the peripheral immune inflammatory response in spin...