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Updated: Sep 18, 2025

Author Spotlight: Modeling an Aspect of Preeclampsia in Female Mice Using Hypoxic Human Placenta-Derived Small Extracellular Vesicles
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Author Spotlight: Modeling an Aspect of Preeclampsia in Female Mice Using Hypoxic Human Placenta-Derived Small Extracellular Vesicles

Published on: January 26, 2024

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可解释的机器学习揭示了在产前症风险预测中的核糖体生物生成生物标志物

Jingjing Chen1, Dan Zhang1, Chengxiu Zhu1

  • 1Department of Gynecology and Obstetrics, The Third Affiliated Hospital of Wenzhou Medical University, Rui'an, China.

Frontiers in immunology
|June 24, 2025
PubMed
概括

这项研究揭示了核糖体生物发生失调是孕前症 (PE) 的关键因素. 机器学习为早期PE检测和潜在的治疗目标确定了六种新生物标志物.

关键词:
生物标志物验证的验证多算法机器学习机器学习孕前症是什么意思核糖体生物发生失调调节.风险模型的风险模型.

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