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首页> 外文会议>Pattern Recognition in Bioinformatics >TopEVM: Using Co-occurrence and Topology Patterns of Enzymes in Metabolic Networks to Construct Phylogenetic Trees
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TopEVM: Using Co-occurrence and Topology Patterns of Enzymes in Metabolic Networks to Construct Phylogenetic Trees

机译：TopEVM：使用代谢网络中酶的共现和拓扑模式来构建系统进化树

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摘要

Network-based phylogenetic analysis typically involves representing metabolic networks as graphs and analyzing the characteristics of vertex sets using set theoretic measures. Such approaches, however, fail to take into account the structural characteristics of graphs. In this paper we propose a new pattern recognition technique, TopEVM, to help representing metabolic networks as weighted vectors. We assign weights according to co-occurrence patterns and topology patterns of enzymes, where the former are determined in a manner similar to the Tf-Idf approach used in document clustering, and the latter are determined using the degree centrality of enzymes. By comparing the weighted vectors of organisms, we determine the evolutionary distances and construct the phylogenetic trees. The resulting TopEVM trees are compared to the previous NCE trees with the NCBI Taxonomy trees as reference. It shows that TopEVM can construct trees much closer to the NCBI Taxonomy trees than the previous NCE methods.
机译：基于网络的系统发育分析通常涉及将代谢网络表示为图形，并使用集合理论量度来分析顶点集的特征。但是，这种方法没有考虑图形的结构特征。在本文中，我们提出了一种新的模式识别技术TopEVM，以帮助将代谢网络表示为加权矢量。我们根据酶的共现模式和拓扑结构模式分配权重，其中前者的确定方式与文档聚类中使用的Tf-Idf方法类似，而后者则通过酶的中心度确定。通过比较生物的加权向量，我们确定进化距离并构建系统树。将生成的TopEVM树与以前的NCE树进行比较，并以NCBI分类树为参考。它表明，与以前的NCE方法相比，TopEVM可以构建更接近NCBI分类树的树。

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