基于网络对生物医学中的多学科数据的分析
Rachit Kumar1,2, Joseph D Romano3,4, Marylyn D Ritchie5,6,7
1Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
BioData mining
|May 28, 2025
概括
本综述探讨了多omics数据的网络表示,整合了基因组学,转录组学和蛋白质组学. 它分析了生物医学大数据任务的深度学习和机器学习方法,例如疾病亚型和生物标志物识别.
科学领域:
- 生物医学数据科学 生物医学数据科学
- 网络生物学 网络生物学
- 计算生物学 计算生物学
背景情况:
- 人类生物学数据是复杂的和分层的,通常存在于多个 -omics 数据集中.
- 网络表示 (图) 可以编码生物概念 (节点和边缘) 之间的关系.
- 多学科数据集成对于理解复杂的生物系统至关重要.
研究的目的:
- 审查现有的网络表示方法和分析多领域数据的方法.
- 检查深度学习和机器学习在这个领域的应用.
- 确定该领域的好处,缺点和未来方向.
主要方法:
- 审查监督和无监督机器学习和深度学习方法.
- 分析基于网络的方法,用于多领域数据集成.
- 专注于以图表为基础的表征来编码生物关系.
主要成果:
- 网络表示方便将关系纳入生物医学大数据任务中.
- 应用包括疾病亚型,相互作用预测,生物标记物识别和患者分类.
- 深度学习和机器学习为分析这些复杂网络提供了强大的框架.
结论:
- 基于网络的多学科分析是推动生物医学研究的一个有前途的方法.
- 进一步开发集成机器学习和深度学习策略是有必要的.
- 识别好处和缺点将指导未来的研究和应用开发.
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