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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Kernel differential subgraph reveals dynamic changes in biomolecular networks
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Kernel differential subgraph reveals dynamic changes in biomolecular networks

机译：内核差分子图显示了生物分子网络的动态变化

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摘要

Many major diseases, including various types of cancer, are increasingly threatening human health. However, the mechanisms of the dynamic processes underlying these diseases remain ambiguous. From the holistic perspective of systems science, complex biological networks can reveal biological phenomena. Changes among networks in different states influence the direction of living organisms. The identification of the kernel differential subgraph (KDS) that leads to drastic changes is critical. The existing studies contribute to the identification of a KDS in networks with the same nodes; however, networks in different states involve the disappearance of some nodes or the appearance of some new nodes. In this paper, we propose a new topology-based KDS (TKDS) method to explore the core module from gene regulatory networks with different nodes in this process. For the common nodes, the TKDS method considers the differential value (D-value) of the topological change. For the different nodes, TKDS identifies the most similar gene pairs and computes the D-value. Hence, TKDS discovers the essential KDS, which considers the relationships between the same nodes as well as different nodes. After applying this method to non-small cell lung cancer (NSCLC), we identified 30 genes that are most likely related to NSCLC and extracted the KDSs in both the cancer and normal states. Two significance functional modules were revealed, and gene ontology (GO) analyses and literature mining indicated that the KDSs are essential to the processes in NSCLC. In addition, compared with existing methods, TKDS provides a unique perspective in identifying particular genes and KDSs related to NSCLC. Moreover, TKDS has the potential to predict other critical disease-related genes and modules.
机译：许多主要疾病，包括各种类型的癌症，越来越危及人类健康。然而，这些疾病潜在的动态过程的机制仍然存在含糊不清。从系统科学的整体角度来看，复杂的生物网络可以揭示生物现象。不同状态网络中的网络变化影响了生物体的方向。导致剧烈变化的内核差分子图（KDS）的识别至关重要。现有研究有助于识别具有相同节点的网络中的KDS;但是，不同状态的网络涉及某些节点的消失或某些新节点的外观。在本文中，我们提出了一种新的基于拓扑的KDS（TKDS）方法来探讨来自此过程中不同节点的基因监管网络的核心模块。对于公共节点，TKDS方法考虑拓扑变化的差值（D值）。对于不同的节点，TKD标识最相似的基因对并计算D值。因此，TKDS发现基本KDS，它考虑了同一节点之间的关系以及不同的节点。在将该方法应用于非小细胞肺癌（NSCLC）后，我们确定了30个与NSCLC相关的基因，并在癌症和正常状态下提取KDSS。揭示了两种意义功能模块，基因本体论（GO）分析和文献挖掘表明KDSS对NSCLC中的过程至关重要。此外，与现有方法相比，TKDS提供了独特的视角，用于识别与NSCLC相关的特定基因和KDS。此外，TKD具有预测其他关键疾病相关基因和模块的可能性。

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