整合机器学习与Omics方法的最新进展 食品科学和营养研究中的Omics方法
Xiang Xiao1, Lin Fu1, Zhihong Zhang2
1School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
Journal of agricultural and food chemistry
|November 13, 2025
概括
奥米克技术和机器学习 (ML) 正在改变食品和营养研究. 它们的整合有助于分析复杂的食品经济学和营养经济学数据,以获得更好的健康见解.
科学领域:
- 食品科学与技术 食品科学与技术
- 营养科学 营养科学
- 生物信息学是一种生物信息学.
背景情况:
- 奥米克技术 (基因组学,转录组学,蛋白质组学,代谢组学) 提供有关食品成分及其与人类健康相互作用的高通量数据.
- 机器学习 (ML) 方法擅长分析大型数据集进行分类,聚类和模式检测.
- 与ML集成的多组数据对于推进食品加工,安全和营养研究至关重要.
研究的目的:
- 审查食品和营养研究中将ML与各种omics技术集成的现状.
- 突出ML技术的应用,包括深度学习 (DL) 和人工神经网络 (ANN),在食品和营养学.
- 讨论ML-omics整合的优势和挑战,并概述未来的研究方向.
主要方法:
- 审查关于OMIC技术和ML在食品和营养中的应用现有的文献.
- 检查特定的omics数据类型:基因组学,转录组学,蛋白质组学,代谢组学和多组学.
- 对ML技术的分析:经典的ML,DL和ANN.
主要成果:
- ML-omics集成使复杂数据集的复杂分析能够用于食品成分,质量评估和营养干预.
- ML有助于理解食品,其成分和人类健康之间的复杂关系.
- 关键应用包括食品加工,安全和个性化营养的预测建模.
结论:
- ML和omics之间的协同作用为推进食品经济学和营养经济学提供了强大的工具.
- 解决数据质量,模型可靠性和可解释性方面的挑战对于未来的进步至关重要.
- 持续的整合有望提高食品安全,质量和定制的营养策略,以改善公共卫生.
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