用知识图和图形神经网络整合多主题数据以实现个性化的营养:全面的综述
Sharath Thoniyot1, Vijayakumar Balakrishnan1
1Department of Computer Science, Birla Institute of Technology and Science, Pilani, Dubai, Campus, Dubai International Academic City, Dubai, United Arab Emirates.
Computational biology and chemistry
|November 15, 2025
概括
知识图和图形神经网络 (GNN) 整合了多omics数据以实现个性化的营养. 这种方法增强了饮食计划的定制性和个人健康结果的优化.
科学领域:
- 生物医学信息学 生物医学信息学
- 营养科学 营养科学
- 计算生物学 计算生物学
背景情况:
- 个性化营养需要整合各种生物数据 (基因组学,蛋白质组学,代谢组学,微生物组).
- 现有的方法难以捕捉复杂的内部关系,以定制饮食建议.
- 知识图和图形神经网络 (GNN) 为多领域数据集成提供了一个新的框架.
研究的目的:
- 审查知识图表和GNN在个性化营养中的应用.
- 评估基于图形的方法对多omics数据集成的有效性.
- 突出利用这些技术进行定制饮食干预的进步和挑战.
主要方法:
- 2015-2025年科学出版物的系统文献综述.
- 对多关系生物数据的知识图构造的分析.
- 评估GNN用于分析基于图形的多omics信息.
主要成果:
- 知识图为生物信息表示提供了一个可扩展的模型.
- 全国统计网识别了传统统计所忽略的复杂的经济间关系.
- 与传统方法相比,基于图表的方法在捕捉生物复杂性的能力更强.
结论:
- 知识图和GNN使我们能够向精准营养转变范式.
- 这些技术改善了个体反应预测,生物标志物发现和营养疗法开发.
- 解决数据质量,模型可扩展性,可解释性和道德问题对于未来的进步至关重要.
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