数据驱动的OMIC集成的算法和工具,以实现多层生物见解:一个叙事审查
Aurelia Morabito1,2, Giulia De Simone3,4, Roberta Pastorelli3
1Laboratory of Metabolites and Proteins in Translational Research, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156, Milan, Italy. aurelia.morabito@polimi.it.
Journal of translational medicine
|April 11, 2025
概括
整合多个omics数据集 (基因组学,转录组学,蛋白质组学,代谢组学) 为疾病机制和生物标志物提供了强大的洞察力. 虽然统计和机器学习方法显示出希望,但对综合系统生物学方法而言,数据质量和复杂性的挑战仍然存在.
科学领域:
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
- 计算生物学是一种计算生物学.
背景情况:
- 高通量欧米克技术产生了巨大的数据集,对于理解复杂的生物系统至关重要.
- 整合多种omics数据 (基因组学,转录组学,蛋白质组学,代谢组学) 是解开疾病机制和识别生物标志物的关键.
- 最近的文献 (2018-2024) 强调了多学科数据集成的发展战略.
研究的目的:
- 审查和分类整合基因组学,转录组学,蛋白质组学和代谢组学数据的策略.
- 评估不同整合方法的普遍性和有效性.
- 在多学科融合中识别挑战和开放问题.
主要方法:
- 2018年至2024年期间发表的文献综述,重点关注多学科整合战略.
- 将整合方法分类为基于统计的,多变量和机器学习/人工智能技术.
- 分析不同集成方法的应用和性能.
主要成果:
- 统计方法,特别是基于相关性的方法,是最普遍的,其次是多变量和机器学习技术.
- 与单个omics分析相比,多omics集成通常可以提高分子机制,生物标志物和分类准确性的发现.
- 常见的挑战包括数据质量,缺失值,对线性,维度和数据异质性.
结论:
- 多学科的整合对推进系统生物学和精密医学具有重大潜力.
- 解决数据质量和复杂性问题对于释放整合各种生物数据集的全部好处至关重要.
- 需要进行进一步的研究,以开发强大的方法来克服omics数据集成的剩余挑战.
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