Identify bilayer modules via pseudo-3D clustering: applications to miRNA-gene bilayer networks.
Identify bilayer modules via pseudo-3D clustering: applications to miRNA-gene bilayer networks.
复制标题
通过伪 3D 聚类识别双层模块：在 miRNA 基因双层网络中的应用
DOI：
10.1093/nar/gkw679
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发表时间：
2016-11-16
影响因子：
14.9
通讯作者：
Liu G
中科院分区：
文献类型：
--
作者：
Xu Y;Guo M;Liu X;Wang C;Liu Y;Liu G
模块识别是一种常用的方法，用于挖掘全局网络中具有重要意义的局部结构。最近，各种各样的双层网络正在出现，以表征更复杂的生物过程。鉴于双层网络的特殊拓扑性质及其带来的挑战，目前还没有针对双层模块识别的有效方法来从更具启发性的双层网络中探索模块化组织。为此，我们提出了伪三维聚类算法，该算法从提取初始的非分层组织模块开始，然后按照自下而上的策略迭代地解密模块的分层组织。具体而言，提出了双层模块的模块化函数，以促进算法报告最优划分，从而给出最准确的双层网络表征。仿真研究证明了该方法的鲁棒性和优于其他竞争方法的性能。对大豆和人类mirna -基因双层网络的具体应用表明，伪三维聚类算法成功地识别了重叠、分层组织和高度内聚的双层模块。对拓扑结构、功能和人类疾病富集以及参与大豆脂肪生物合成的双层子网络的分析，为伪三维聚类算法的有效性和鲁棒性提供了理论和生物学证据。
Abstract Module identification is a frequently used approach for mining local structures with more significance in global networks. Recently, a wide variety of bilayer networks are emerging to characterize the more complex biological processes. In the light of special topological properties of bilayer networks and the accompanying challenges, there is yet no effective method aiming at bilayer module identification to probe the modular organizations from the more inspiring bilayer networks. To this end, we proposed the pseudo-3D clustering algorithm, which starts from extracting initial non-hierarchically organized modules and then iteratively deciphers the hierarchical organization of modules according to a bottom-up strategy. Specifically, a modularity function for bilayer modules was proposed to facilitate the algorithm reporting the optimal partition that gives the most accurate characterization of the bilayer network. Simulation studies demonstrated its robustness and outperformance against alternative competing methods. Specific applications to both the soybean and human miRNA-gene bilayer networks demonstrated that the pseudo-3D clustering algorithm successfully identified the overlapping, hierarchically organized and highly cohesive bilayer modules. The analyses on topology, functional and human disease enrichment and the bilayer subnetwork involved in soybean fat biosynthesis provided both the theoretical and biological evidence supporting the effectiveness and robustness of pseudo-3D clustering algorithm.
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影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
DOI：
10.1093/bioinformatics/btq197
发表时间：
2010-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
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影响因子：
3.7
作者：
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通讯作者：
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影响因子：
64.8
作者：
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通讯作者：
Murray, AW