Network neighborhood analysis with the multi-node topological overlap measure
Network neighborhood analysis with the multi-node topological overlap measure
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DOI：
10.1093/bioinformatics/btl581
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发表时间：
2007-01-15
期刊：
影响因子：
5.8
通讯作者：
Horvath, Steve
中科院分区：
文献类型：
--
作者：
Li, Ai;Horvath, Steve
动机：邻域分析的目标是找到一组基因（邻域），它类似于一组初始的“种子”基因。网络数据的邻域分析方法在系统生物学中具有重要意义。如果各个网络连接易受噪声影响，则基于鲁棒互连性度量（例如拓扑重叠度量）来定义邻域可能是有利的。由于在种子集中使用多个节点可能会导致更多的信息的邻居，它可以是有利的定义多节点的相似性measures.Results：成对的拓扑重叠措施推广到多个网络节点，随后使用的递归邻域构造方法。局部置换方案用于确定邻域大小。使用四个网络应用程序和一个模拟的例子，我们提供的经验证据表明，由此产生的邻居是生物学上有意义的，例如，我们使用邻域分析，以确定脑癌相关的基因。
Motivation: The goal of neighborhood analysis is to find a set of genes (the neighborhood) that is similar to an initial 'seed' set of genes. Neighborhood analysis methods for network data are important in systems biology. If individual network connections are susceptible to noise, it can be advantageous to define neighborhoods on the basis of a robust interconnectedness measure, e.g. the topological overlap measure. Since the use of multiple nodes in the seed set may lead to more informative neighborhoods, it can be advantageous to define multi-node similarity measures.Results: The pairwise topological overlap measure is generalized to multiple network nodes and subsequently used in a recursive neighborhood construction method. A local permutation scheme is used to determine the neighborhood size. Using four network applications and a simulated example, we provide empirical evidence that the resulting neighborhoods are biologically meaningful, e.g. we use neighborhood analysis to identify brain cancer related genes.