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August 15, 2025Frontiers in Cell and Developmental BiologyOpen Access

Identification of progression-related genes and construction of prognostic model for chronic kidney disease by machine learning

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Authors

BZBingkun ZhouHZHu ZhouXHXiaodong Huang

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Overview

Machine learning predicts chronic kidney disease risk with three gene sets, indicating potential targets for further research.

Key Points

  • The study identifies 29 progression-related genes linked to chronic kidney disease occurrence and development.
  • A maximal gene set achieved an AUC of 0.767 for classifying chronic kidney disease in external validation datasets.
  • Analysis included weighted correlation network analysis and random forest algorithms to develop predictive models.
  • Transcriptomic data from human chronic kidney disease was validated in a mouse model, confirming gene relevance for further studies.

Cite This Study

Zhou et al. (2025) studied this question.

synapsesocial.com/papers/68af6c0b7567bf4f94fe9f1bhttps://doi.org/10.3389/fcell.2025.1627355
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