Pattern Discovery in Multilayer Networks
Pattern Discovery in Multilayer Networks
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DOI：
10.1109/tcbb.2021.3105001
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发表时间：
2022-03-01
影响因子：
4.5
通讯作者：
Kahveci, Tamer
中科院分区：
文献类型：
--
作者：
Ren, Yuanfang;Sarkar, Aisharjya;Kahveci, Tamer
动机：在生物信息学中，识别重要分子相互作用的复杂细胞建模和行为模拟被认为是一个相关的问题。传统的方法使用单网络和二进制网络对这类复杂系统进行建模。然而，该模型不足以表示生物网络，因为不同的相互作用约束(如转录调节和蛋白质相互作用)可以同时发生不同的相互作用集。此外，在不同的发育阶段或胁迫条件下，即使是相同的相互作用类型，生物系统也可能表现出不同的相互作用拓扑结构。因此，将生物系统视为孤立相互作用的模型是不准确的，因为它们未能捕捉到生物体内细胞相互作用的复杂行为。网络中重复模体的识别和计数是生物网络分析中的基本问题之一。现有的基序依赖于单一网络拓扑的方法不足以捕捉分子相互作用的模式，当在相同生物体内相似的不同生物之间，甚至是时变的网络中识别时，分子相互作用在生物表达中具有显著的变化。也就是说，当他们考虑一组多个网络中的网络的单个快照时，他们无法识别重复的交互。因此，我们需要研究多种网络拓扑以及它们之间的模式守恒的方法。贡献：在本文中，我们考虑了在给定的多层网络中计算用户提供的Motif拓扑的实例数的问题。我们将描述各种条件或时间变化的一组实体(例如，基因)之间的相互作用建模为多层网络。因此，单独的网络作为每一层显示在唯一网络状态下的节点的连通性。现有的模体统计和识别方法仅限于单一的网络拓扑，因此不能直接应用于多层网络。我们应用我们的模型和算法来研究蜂窝网络中的频繁模式，这些模式在不同的应力条件下变化的蜂窝网络状态是常见的，其中每个应力条件下的蜂窝网络拓扑描述了唯一的网络层。结果：提出了一种基于该模型的多层网络模体计数方法和相应的算法。我们在真实数据集和合成数据集上进行了实验。我们在一系列参数下对合成数据集进行建模，如网络规模、密度、基序频率。在合成数据集上的结果表明，与现有的G-TRIES、ESU(FANMODE)和mfinder等方法相比，我们的算法发现Motif嵌入的准确率非常高。此外，我们观察到，我们的方法运行速度比现有方法快几倍到几个数量级。为了在真实数据集上进行实验，我们考虑了不同实验条件下的大肠杆菌转录调控网络。我们观察到，我们的方法选择的基因在不同的胁迫条件下保留了功能特征，错误发现率非常低。此外，该方法在网络规模和层数方面都可扩展到实际网络。
Motivation: In bioinformatics, complex cellular modeling and behavior simulation to identify significant molecular interactions is considered a relevant problem. Traditional methods model such complex systems using single and binary network. However, this model is inadequate to represent biological networks as different sets of interactions can simultaneously take place for different interaction constraints (such as transcription regulation and protein interaction). Furthermore, biological systems may exhibit varying interaction topologies even for the same interaction type under different developmental stages or stress conditions. Therefore, models which consider biological systems as solitary interactions are inaccurate as they fail to capture the complex behavior of cellular interactions within organisms. Identification and counting of recurrent motifs within a network is one of the fundamental problems in biological network analysis. Existing methods for motif counting on single network topologies are inadequate to capture patterns of molecular interactions that have significant changes in biological expression when identified across different organisms that are similar, or even time-varying networks within the same organism. That is, they fail to identify recurrent interactions as they consider a single snapshot of a network among a set of multiple networks. Therefore, we need methods geared towards studying multiple network topologies and the pattern conservation among them. Contributions: In this paper, we consider the problem of counting the number of instances of a user supplied motif topology in a given multilayer network. We model interactions among a set of entities (e.g., genes)describing various conditions or temporal variation as multilayer networks. Thus a separate network as each layer shows the connectivity of the nodes under a unique network state. Existing motif counting and identification methods are limited to single network topologies, and thus cannot be directly applied on multilayer networks. We apply our model and algorithm to study frequent patterns in cellular networks that are common in varying cellular states under different stress conditions, where the cellular network topology under each stress condition describes a unique network layer. Results: We develop a methodology and corresponding algorithm based on the proposed model for motif counting in multilayer networks. We performed experiments on both real and synthetic datasets. We modeled the synthetic datasets under a wide spectrum of parameters, such as network size, density, motif frequency. Results on synthetic datasets demonstrate that our algorithm finds motif embeddings with very high accuracy compared to existing state-of-the-art methods such as G-tries, ESU (FANMODE)and mfinder. Furthermore, we observe that our method runs from several times to several orders of magnitude faster than existing methods. For experiments on real dataset, we consider Escherichia coli (E. coli)transcription regulatory network under different experimental conditions. We observe that the genes selected by our method conserves functional characteristics under various stress conditions with very low false discovery rates. Moreover, the method is scalable to real networks in terms of both network size and number of layers.