一个可解释的机器学习模型,用于准确预测间歇性禁食模式的生物标志物
Xiaoli Hu1, Qingjun Xu1, Xuan Ma1
1Animal-Derived Food Safety Innovation Team, College of Veterinary Medicine, Anhui Agricultural University, Hefei, 230036, People's Republic of China.
Nutrition & metabolism
|December 19, 2024
概括
间歇性禁食显示出有希望的结果,Ganoderenic Acid C被确定为一个关键的便生物标志物. 这项研究有助于理解饮食的影响,并选择更健康的生活方式.
科学领域:
- 代谢学 代谢学 代谢学
- 生物标志物发现发现
- 机器学习在营养学中的应用
背景情况:
- 间歇性禁食 (IF) 是一种流行的饮食方法,人们对其生理效应越来越感兴趣.
- 确定可靠的生物标志物对于了解IF的代谢影响至关重要.
- 便代谢物为肠道健康和对饮食的全身反应提供了一个窗口.
研究的目的:
- 识别潜在的便生物标志物,区分间歇性禁食与正常养模式.
- 在代谢学数据中应用可解释的机器学习来发现生物标志物.
- 为更健康的饮食和生活方式选择提供基础.
主要方法:
- 使用超高性能液体染色学-高分辨率质谱法 (UPLC-HRMS) 来分析小鼠便样本的非向代谢学分析.
- 开发和评估用于模式识别的五种机器学习模型.
- 用形状添加式解释 (SHAP) 进行加权生物标志物分析.
主要成果:
- 随机森林模型在区分间歇性禁食和正常养群体方面表现出最高的准确性.
- 形状添加解释 (SHAP) 分析确定了特定的代谢物贡献.
- 甘烯酸C被确定为区分这两种饮食模式的潜在关键生物标志物.
结论:
- 甘烯酸C是一种有前途的便生物标志物,用于间歇性禁食.
- 可解释机器学习,特别是随机森林与SHAP,对于饮食研究中的生物标志物发现是有效的.
- 这项研究提供了对IF代谢反应的见解,并支持基于证据的饮食建议.
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