整合多组学数据用于疾病模块检测的统计物理方法。

IF 4.5 Q1 BIOCHEMICAL RESEARCH METHODS
Xu-Wen Wang, Min Hyung Ryu, Michael H Cho, Peter Castaldi, Craig P Hersh, Edwin K Silverman, Yang-Yu Liu
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引用次数: 0

摘要

与同一疾病相关的基因经常参与相互的生物学相互作用,例如,在分子相互作用组(通常称为疾病模块)的特定邻域内的扰动。这推动了基于网络的方法在阐明人类疾病分子基础方面的进步。尽管已经开发了许多计算方法来整合分子相互作用组和组学概况以提取这种依赖于环境的疾病模块,但利用多组学进行疾病模块检测的方法仍然缺乏。在这里,我们开发了一种基于随机场O(n)模型(RFOnM)的统计物理方法来填补这一空白。我们应用RFOnM方法整合基因表达数据和全基因组关联研究,或mRNA数据和DNA甲基化,用于几种复杂疾病与人类相互作用组。我们发现RFOnM方法在本研究中考虑的大多数复杂疾病中优于现有的单组学方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A statistical physics approach to integrating multi-omics data for disease-module detection.

Genes associated with the same disease frequently engage in mutual biological interactions, e.g., perturbation within a specific neighborhood in the molecular interactome, often referred to as the disease module. This has propelled the advancement of network-based approaches toward elucidating the molecular bases of human diseases. Although many computational methods have been developed to integrate the molecular interactome and omics profiles to extract such context-dependent disease modules, approaches that leverage multi-omics for disease-module detection are still lacking. Here, we developed a statistical physics approach based on the random-field O(n) model (RFOnM) to fill this gap. We applied the RFOnM approach to integrate gene-expression data and genome-wide association studies or mRNA data and DNA methylation for several complex diseases with the human interactome. We found that the RFOnM approach outperforms existing single omics methods in most of the complex diseases considered in this study.

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来源期刊
Cell Reports Methods
Cell Reports Methods Chemistry (General), Biochemistry, Genetics and Molecular Biology (General), Immunology and Microbiology (General)
CiteScore
3.80
自引率
0.00%
发文量
0
审稿时长
111 days
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