Objective: The accumulation of large amounts of multidimensional genomic data provides new opportunities to study multilevel biological regulatory associations. Identifying multidimensional regulatory modules (md-modules) from omics data is crucial to provide a comprehensive understanding of the regulatory mechanisms of biological systems. Methods: We develop a multi-graph matching with multiple network constraints (MGMMNC) model to identify the md-modules. The MGMMNC model aims to accurately capture highly relevant md-modules by considering the relationships intra- and inter-multidimensional omics data, including interactions within a network and cycle consistency information. The proposed technique adopts a novel graph-smoothing similarity measurement for the highly contaminated genetic data. Results: The superiority and effectiveness of MGMMNC have been demonstrated by comparative experiments with three state-of-the-art techniques using simulated and cervical cancer data. Conclusion: MGMMNC can accurately and efficiently identify the md-modules that are significantly enriched in gene ontology biological processes and in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Many different level molecules in the same md-module collaboratively regulate the same pathway. Moreover, the md-modules are capable of stratifying patients into subtypes with significant survival differences. Significance: The problem of identifying multidimensional regulatory modules from omics data is formulated as a multi-graph matching problem, and multiple network constraints and cycle consistency information are seamlessly integrated into the matching model.

中文翻译：

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通过具有网络约束的多图匹配识别多维监管模块

目的：大量多维基因组数据的积累为研究多层次生物调控关联提供了新的机会。从组学数据中识别多维调控模块（md-modules）对于全面了解生物系统的调控机制至关重要。方法：我们开发了具有多个网络约束（MGMMNC）模型的多图匹配来识别 md 模块。MGMMNC 模型旨在通过考虑内部和多维组学数据间的关系（包括网络内的相互作用和循环一致性信息）来准确捕获高度相关的 md 模块。所提出的技术对高度污染的遗传数据采用了一种新颖的图形平滑相似性测量。结果：MGMMNC 的优越性和有效性已通过使用模拟和宫颈癌数据的三种最先进技术的比较实验得到证明。结论：MGMMNC 可以准确有效地识别在基因本体生物学过程和京都基因和基因组百科全书（KEGG）途径中显着富集的 md 模块。同一 md 模块中的许多不同级别的分子协同调节相同的途径。此外，md 模块能够将患者分为具有显着生存差异的亚型。意义：从组学数据中识别多维调控模块的问题被表述为多图匹配问题，将多个网络约束和循环一致性信息无缝集成到匹配模型中。