ArticleFrontiers in medicine2022
Machine learning algorithm-based identification and verification of characteristic genes in acute kidney injury.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- JAK2/STAT3-dependent regulation of MDM4/MDM2-p53 signaling in methotrexate-induced ferroptosis and nephrotoxicity.Archives of pharmacal research · 2026Article
- Elucidating the role of FBXW4 in osteoporosis: integrating bioinformatics and machine learning for advanced insight.BMC pharmacology & toxicology · 2025Article
- Systems Approaches to Cell Culture-Derived Extracellular Vesicles for Acute Kidney Injury Therapy: Prospects and Challenges.Function (Oxford, England) · 2024Review
- APOC1 exacerbates renal fibrosis through the activation of the NF-κB signaling pathway in IgAN.Frontiers in pharmacology · 2023Article
- Artificial Intelligence Models in Diagnosis and Treatment of Kidney Diseases: Current Status and Prospects.Kidney diseases (Basel, Switzerland)Article
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Abstract
Background: Acute kidney injury is a common renal disease with high incidence and mortality. Early identification of high-risk acute renal injury patients following renal transplant could improve their prognosis, however, no biomarker exists for early detection. Methods: The GSE139061 dataset was used to identify hub genes in 86 DEGs between acute kidney injury and control samples using three machine learning algorithms (LASSO, random forest, and support vector machine-recursive feature elimination). We used GSEA to identify the related signal pathways of six hub genes. Finally, we validated these potential biomarkers in an Results: Six hub genes Conclusion: We screened six hub genes related to acute kidney injury using three machine learning algorithms and identified genes with potential diagnostic utility. The hub genes identified in this study might play a significant role in the pathophysiology and progression of AKI. As such, they might be useful for the early diagnosis of AKI and provide the possibility of improving the prognosis of AKI patients.
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