Precise disease module is conducive to understanding the molecular mechanism of disease causation and identifying drug targets. However, due to the fragmentization of disease module in incomplete human interactome, how to determine connectivity pattern and detect a complete neighbourhood of disease based on this is still an open question. In this paper, we perform exploratory analysis leading to an important observation that through a few intermediate nodes, most separate connected components formed by disease-associated proteins can be effectively connected and eventually form a complete disease module. And based on the topological properties of these intermediate nodes, we propose a connect separate connected components (C3) method to detect a succinct disease module by introducing a relatively small number of intermediate nodes, which allows us to obtain more pure disease module than other methods. Then we apply C3 across a large corpus of diseases to validate this connectivity pattern of disease module. Furthermore, the connectivity of the perturbed genes in multi-omics data such as The Cancer Genome Atlas also fits this pattern. C3 tool is not only useful in detecting a clearly-defined connected disease neighbourhood of 299 diseases and cancer with multi-omics data, but also helpful in better understanding the interconnection of phenotypically related genes in different omics data and studying complex pathological processes.

中文翻译：

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C3：连接单独的连接组件以形成简洁的疾病模块

精确的疾病模块有助于了解疾病原因的分子机制和确定药物靶标。然而，由于不完整的人类交互基因组中疾病模块的碎片化，如何基于此确定连通性模式和检测疾病的完整邻域仍是一个悬而未决的问题。在本文中，我们进行了探索性分析，得出了一个重要的观察结果，即通过几个中间节点，可以有效地连接由疾病相关蛋白形成的大多数独立的连接组件，并最终形成完整的疾病模块。基于这些中间节点的拓扑特性，我们提出了一种连接分离连接组件（C3）方法，通过引入相对少量的中间节点来检测简洁的疾病模块，与其他方法相比，这使我们可以获得更多的纯净疾病模块。然后，我们将C3应用于大量疾病，以验证疾病模块的这种连通性模式。此外，诸如癌症基因组图谱之类的多组学数据中干扰基因的连通性也符合这种模式。C3工具不仅可用于利用多组学数据检测299种疾病和癌症的明确定义的邻近疾病邻域，而且还可帮助更好地了解不同组学数据中表型相关基因的相互关系以及研究复杂的病理过程。多组学数据（如《癌症基因组图谱》）中干扰基因的连通性也符合这种模式。C3工具不仅可用于利用多组学数据检测299种疾病和癌症的明确定义的邻近疾病邻域，而且还可帮助更好地了解不同组学数据中表型相关基因的相互关系以及研究复杂的病理过程。多组学数据（如《癌症基因组图谱》）中干扰基因的连通性也符合这种模式。C3工具不仅可用于利用多组学数据检测299种疾病和癌症的明确定义的邻近疾病邻域，而且还可帮助更好地了解不同组学数据中表型相关基因的相互关系以及研究复杂的病理过程。