Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury

Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury

BackgroundSepsis frequently induces acute kidney injury (AKI), and the complex interplay between these two conditions worsens prognosis, prolongs hospitalization, and increases mortality. Despite therapeutic options such as antibiotics and supportive care, early diagnosis and treatment remain a chal...

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Bibliographic Details
Main Authors: Chen Lin, Meng Zheng, Wensi Wu, Zhishan Wang, Guofeng Lu, Shaodan Feng, Xinlan Zhang
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-07-01
Series:Frontiers in Genetics
Subjects:
sepsis
acute kidney injury
machine learning
diagnostic modeling
immune infiltration
Online Access:https://www.frontiersin.org/articles/10.3389/fgene.2025.1561331/full
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https://www.frontiersin.org/articles/10.3389/fgene.2025.1561331/full

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