Li Peng, He Tingting, Hu Xiaohua, Zhao Junmin, Shen Xianjun, Zhang Ming, Wang Yan
IEEE Trans Nanobioscience. 2014 Jun;13(2):89-96. doi: 10.1109/TNB.2014.2317755. Epub 2014 Apr 24.
A novel algorithm based on Connected Affinity Clique Extension (CACE) for mining overlapping functional modules in protein interaction network is proposed in this paper. In this approach, the value of protein connected affinity which is inferred from protein complexes is interpreted as the reliability and possibility of interaction. The protein interaction network is constructed as a weighted graph, and the weight is dependent on the connected affinity coefficient. The experimental results of our CACE in two test data sets show that the CACE can detect the functional modules much more effectively and accurately when compared with other state-of-art algorithms CPM and IPC-MCE.
本文提出了一种基于连通亲和团扩展(CACE)的新颖算法,用于在蛋白质相互作用网络中挖掘重叠功能模块。在这种方法中,从蛋白质复合物推断出的蛋白质连通亲合度值被解释为相互作用的可靠性和可能性。蛋白质相互作用网络被构建为一个加权图,权重取决于连通亲和系数。我们的CACE在两个测试数据集上的实验结果表明,与其他现有算法CPM和IPC-MCE相比,CACE能够更有效、准确地检测功能模块。