基于人工智能的整合性方法连接多基因组以使用微生物组-代谢组交互结果作为精密医学
Sohini Mukhopadhyay1,2,3, Nivedhitha Ulaganathan4, Prashanth Dumpuri4
1School of Biological Sciences, National Institute of Science Education and Research (NISER), Khurdha, Odisha, India.
Methods in molecular biology (Clifton, N.J.)
|June 24, 2025
概括
整合omics数据,包括metagenomics和metabolomics,有助于个性化健康干预. 人工智能 (AI) 工具有助于分析这些复杂的生物数据集,以改善诊断和治疗.
科学领域:
- 生物研究中的多学科整合.
- 计算生物学和生物信息学.
背景情况:
- 全基因组协会研究 (GWAS) 提供了大量的生物数据集.
- 基因组学研究 (基因组学,转录组学,蛋白质组学,代谢组学,转基因组学) 产生了大量的数据.
- 整合多样化的OMIC数据对于理解复杂的生物过程至关重要.
研究的目的:
- 介绍一种用于合并omics数据的方法,重点关注代谢学和转基因组学.
- 为了应对数据处理挑战在多omics分析.
- 探索基于微生物组的个性化干预措施.
主要方法:
- 对宿主和微生物代谢学和转基因组学数据的综合分析.
- 利用公开可用的模型进行微生物组和代谢组分析.
- 应用人工智能 (AI) 和数据科学工具用于数据处理和解释.
主要成果:
- 通过组合的奥米克分析,证明了理解诸如炎症性肠病和刺激性肠综合征等疾病的进步.
- 生物流体和微生物对应物中的已识别的代谢特征作为健康指标.
- 使用人工智能模型阐明微生物和代谢资料之间的相关性.
结论:
- 人工智能和数据科学的整合对于处理和解释大型omics数据集至关重要.
- 结合微生物组和代谢组分析弥合了数据采集和临床应用之间的差距.
- 这种方法为个性化干预和优化个人健康铺平了道路.
关键词:
在这里,我们可以看到AIAIAI.数据分析数据分析代谢组中的代谢.这些元数据是元数据.微生物组是一个微生物组.模型预测 模型预测多个omics的多个omics.精准医学是一门精准的医学.益生菌 益生菌是一种益生菌.更多相关视频
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