通过机器学习对食品健康影响的基于人类微生物的预测
Nanako Inoue1, Tomokazu Shibata2, Ryusuke Sawada3
1Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu Iizuka, Fukuoka 820-8502, Japan.
Journal of agricultural and food chemistry
|August 27, 2025
概括
这项研究引入了一种机器学习模型,通过分析微生物代谢和食物化合物来预测食物的功能. 它揭示了941种食物与83种疾病之间的联系,
科学领域:
- 微生物学
- 计算生物学
- 营养科学
背景情况:
- 食物的功能受肠道微生物及其代谢副产品的影响.
- 了解食物与微生物之间的相互作用对于健康和疾病预防至关重要.
研究的目的:
- 开发基于微生物和代谢数据的机器学习 (ML) 方法来预测食物功能.
- 确定食物,疾病和涉及的微生物之间的潜在关联.
主要方法:
- 使用ML分析70,478种食品化合物和24,255种代谢物的化学特性.
- 检查了与疾病相关的代谢物和标蛋白之间的相互作用.
- 考虑微生物参与的941种食品和83种疾病的综合数据.
主要成果:
- 在941种食物和83种疾病之间确定了潜在的功能联系.
- 突出特定的微生物群 (例如,在脂质不良症中的细菌群,在帕金森病中的细菌群).
- 经过微生物介导的食品效应按食物类型进行分类.
结论:
- ML方法可以识别与疾病相关的微生物和益生菌食物.
- 这种方法通过微生物代谢物提供了对食物疾病相互作用机制的洞察.
- 支持预防医学中的食物干预的潜力.
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