揭开疾病复杂性:临床研究中的多原子数据的整合性分析
Ornella Cominetti1, Loïc Dayon1,2
1Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne, Switzerland.
Expert review of proteomics
|April 10, 2025
概括
整合多学科数据,包括基因组学和蛋白质组学,为健康和疾病研究提供了对生物系统的整体观点. 在数据整合方面仍然存在挑战,需要强大的方法和高质量的标准来进行全面分析.
科学领域:
- 生物系统分析 生物系统分析
- 多领域技术 多领域技术
- 临床研究 临床研究
背景情况:
- 多原子技术可以对基因组,表观基因组,转录基因组,蛋白质组,代谢基因组和其他分子层进行分析.
- 将多种多种原子数据集成到统一的系统地图中存在重大挑战.
- 在人类健康和疾病中的应用需要大样本大小,强大的方法和高质量标准.
研究的目的:
- 审查整合多学科数据的方法,包括人工智能方法.
- 在临床研究中,提出基于蛋白质组学的综合多组学数据的应用.
- 专注于过去五年内从Scopus和/或PubMed.com出版的文学作品.
主要方法:
- 关于多学科整合方法的文献综述.
- 专注于蛋白质组学作为集成的中央数据层.
- 分析选定的临床研究应用,将蛋白质组学与其他蛋白质组学数据相结合.
主要成果:
- 多组学提供了一种强大的方法,可以全面描述分子层,并将它们与表型联系起来.
- 不同的技术和数据类型对有效的多学科集成提出了挑战.
- 需要改进的策略和方法来整合多式联运电信数据.
结论:
- 多学科数据的有效整合对于促进生物系统理解至关重要.
- 持续开发强大的方法,包括人工智能,是成功实现多领域整合的必要条件.
- 标准化和高质量的数据对于多学科研究的可靠临床应用至关重要.
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