整合多主题数据:在人类复杂疾病中的方法和应用
Pasquale Sibilio1,2, Enrico De Smaele2, Paola Paci3,4
1Translational Oncology Research Unit, IRCCS-Regina Elena National Cancer Institute, Rome, Italy.
Biotechnology reports (Amsterdam, Netherlands)
|December 3, 2025
概括
高通量多学科数据集成为复杂疾病提供了洞察力. 基于网络的计算方法是分析基因组学,转录组学和蛋白质组学数据的关键,以推进生物标志物发现和治疗.
科学领域:
- 生物医学研究生物医学研究
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 技术进步使大规模的多omics数据生成 (基因组学,转录组学,蛋白质组学,代谢组学,表观组学) 成为可能.
- 整合多学科数据提供了对生物过程和疾病机制的全球洞察力,特别是在癌症等多因素疾病中.
研究的目的:
- 审查多omics数据集成的计算方法.
- 突出基于网络的方法,以全面地了解生物系统.
- 展示多学科整合在疾病研究中的成功应用.
主要方法:
- 探索用于多领域数据集成的计算方法.
- 专注于基于网络的方法.
- 审查最近成功申请的情况.
主要成果:
- 多主题数据集成带来了高维度和异质性带来的挑战.
- 基于网络的方法提供了对生物成分关系的整体视角.
- 成功的应用表明了疾病研究的关键领域的变革潜力.
结论:
- 多学科数据集成对于理解复杂疾病至关重要.
- 基于网络的计算方法对于分析集成的omics数据是有效的.
- 多omics数据的整合对生物标志物发现,患者分层和治疗干预具有重大前景.
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