A novel functional module detection algorithm for protein-protein interaction networks
A novel functional module detection algorithm for protein-protein interaction networks
复制标题
DOI：
10.1186/1748-7188-1-24
复制
发表时间：
2006-01-01
影响因子：
1
通讯作者：
Ramanathan, Murali
中科院分区：
文献类型：
--
作者：
Hwang, Woochang;Cho, Young-Rae;Ramanathan, Murali
背景：蛋白质-蛋白质相互作用数据集的稀疏连接使得功能模块的识别具有挑战性。本研究的目的是批判性地评估一种新的聚类技术聚类和检测功能模块的蛋白质-蛋白质相互作用网络，称为STM。结果：STM选择代表性的蛋白质为每个集群和迭代的信号转导和图形拓扑结构的组合的基础上改进集群。STM被发现是有效的，在检测集群与各种各样的相互作用的结构是显着的生物相关性的措施。STM的方法相比，六个竞争的方法，包括最大集团，准集团，最小切割，中间切割和马尔可夫聚类（MCL）算法。比较通过每种技术获得的簇的生物学功能的富集。STM生成更大的聚类，并且所识别的聚类具有比生物功能上的其他方法好大约125倍的p值。STM的一个重要优势是，被丢弃的蛋白质的百分比创建clusters是远远低于其他approaches.Conclusion：STM优于竞争的方法，并能够有效地检测密集和稀疏连接，生物相关的功能模块与较少的丢弃。
Background: The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM.Results: STM selects representative proteins for each cluster and iteratively refines clusters based on a combination of the signal transduced and graph topology. STM is found to be effective at detecting clusters with a diverse range of interaction structures that are significant on measures of biological relevance. The STM approach is compared to six competing approaches including the maximum clique, quasi-clique, minimum cut, betweeness cut and Markov Clustering (MCL) algorithms. The clusters obtained by each technique are compared for enrichment of biological function. STM generates larger clusters and the clusters identified have p-values that are approximately 125-fold better than the other methods on biological function. An important strength of STM is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches.Conclusion: STM outperforms competing approaches and is capable of effectively detecting both densely and sparsely connected, biologically relevant functional modules with fewer discards.