Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran ; Database Research Group (DBRG), Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
PLoS One. 2013 Sep 5;8(9):e72366. doi: 10.1371/journal.pone.0072366. eCollection 2013.
Graph clustering algorithms are widely used in the analysis of biological networks. Extracting functional modules in protein-protein interaction (PPI) networks is one such use. Most clustering algorithms whose focuses are on finding functional modules try either to find a clique like sub networks or to grow clusters starting from vertices with high degrees as seeds. These algorithms do not make any difference between a biological network and any other networks. In the current research, we present a new procedure to find functional modules in PPI networks. Our main idea is to model a biological concept and to use this concept for finding good functional modules in PPI networks. In order to evaluate the quality of the obtained clusters, we compared the results of our algorithm with those of some other widely used clustering algorithms on three high throughput PPI networks from Sacchromyces Cerevisiae, Homo sapiens and Caenorhabditis elegans as well as on some tissue specific networks. Gene Ontology (GO) analyses were used to compare the results of different algorithms. Each algorithm's result was then compared with GO-term derived functional modules. We also analyzed the effect of using tissue specific networks on the quality of the obtained clusters. The experimental results indicate that the new algorithm outperforms most of the others, and this improvement is more significant when tissue specific networks are used.
图聚类算法在生物网络分析中得到了广泛的应用。在蛋白质-蛋白质相互作用（PPI）网络中提取功能模块就是这样的一种应用。大多数关注于寻找功能模块的聚类算法要么试图找到一个类似团的子网络，要么从具有高度数的顶点开始生长簇作为种子。这些算法并没有将生物网络与任何其他网络区分开来。在当前的研究中，我们提出了一种新的方法来在 PPI 网络中寻找功能模块。我们的主要思想是构建一个生物概念模型，并利用这个概念来发现 PPI 网络中的功能模块。为了评估所得到的簇的质量，我们将我们的算法的结果与其他一些广泛使用的聚类算法的结果进行了比较，这些算法是基于酿酒酵母、人类和秀丽隐杆线虫三种高通量 PPI 网络以及一些组织特异性网络的。基因本体论（GO）分析被用于比较不同算法的结果。然后，将每个算法的结果与 GO 术语衍生的功能模块进行比较。我们还分析了使用组织特异性网络对获得的簇的质量的影响。实验结果表明，新算法的性能优于大多数其他算法，而当使用组织特异性网络时，这种改进更为显著。
