在缺少数据的情况下,多主题监管网络推断
Juan D Henao1, Michael Lauber2, Manuel Azevedo1
1Helmholtz Zentrum München, Computational Health Department, Ingolstädter Landstraße 1, 85764 Munich, Germany, Member of the German Center for Lung Research (DZL).
Briefings in bioinformatics
|September 6, 2023
概括
这项研究将回归方法集成到KiMONo中,用于强大的多omics网络推断,有效地处理生物系统中缺失的数据. 这些发现证明了可行性,使可用的多学科数据能够更好地用于发现监管机制.
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.
- 基因组学就是基因组学.
背景情况:
- 系统生物学旨在发现通过使用多层网络驱动复杂生物系统的调节机制.
- 多omics分析对于理解这些网络至关重要,但由于实验约束,它往往会因为缺少数据而遭受损失.
- 对于监管网络推断的经典计算方法在处理缺失数据时是有限的.
研究的目的:
- 将能够处理缺失数据的回归方法集成到KiMONo (知识引导多态网络推理) 方法中.
- 在单个和多个学科研究中常见的各种缺失数据场景中比较这些综合方法的性能.
- 评估尽管存在缺失的数据,可靠的多omics网络推断的可行性.
主要方法:
- 将设计用于处理缺失数据的回归方法集成到KiMONo框架中.
- 在各种缺失数据场景中进行基准测试,包括随机和块缺失.
- 对单个和多个omics数据集的评估,具有不同的omics层尺寸.
主要成果:
- 两个步骤的方法明确处理缺失,在随机和块缺失场景中表现出卓越的性能,在不平衡的欧米层维度上.
- 隐式处理缺失的方法在平衡的奥米克层尺寸上显示出最佳性能.
- KiMONo成功地实现了强大的多omics网络推理,即使缺少数据.
结论:
- 使用KiMONo在缺少数据的情况下进行强大的多omics网络推断是可行的.
- 开发的方法允许充分利用可用的多学科数据,克服缺少信息的局限性.
- 这有助于更全面地了解复杂生物系统中的调节机制.
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