Identify protein complexes based on PageRank algorithm and architecture on dynamic PPI networks
Identify protein complexes based on PageRank algorithm and architecture on dynamic PPI networks
复制标题
基于 PageRank 算法和动态 PPI 网络架构识别蛋白质复合物
DOI：
10.1504/ijdmb.2019.101394
复制
发表时间：
2019-08
影响因子：
0.3
通讯作者：
Guo Ling
中科院分区：
文献类型：
--
作者：
Lei Xiujuan;Liang Jing;Guo Ling
蛋白质-蛋白质相互作用（PPI）在细胞组织中是动态的。蛋白质复合物在细胞中发挥重要作用。因此，从动态PPI网络中检测蛋白质复合物是现实的。本文提出了一种基于核心-连接结构和Pagerank算法的蛋白质复合物预测新算法（PRCA），并在动态PPI网络中运行。该方法分为三个步骤。首先计算动态PPI网络中各蛋白质的权值，得到种子蛋白质。其次，考虑到蛋白质的三角形结构，获得了蛋白质复合物的核心。第三，计算蛋白质复合物相邻蛋白质的PageRank值，将蛋白质复合物的附件附加到其对应的核心上，形成蛋白质复合物。该方法识别DIP，MIPS和Krogan数据集的动态PPI网络中的蛋白质复合物。实验结果表明，PRCA算法在查准率、查全率、f-测度和p-值等方面均优于其他算法。
Protein-Protein Interactions (PPI) are dynamic in cellular organisation. Protein complexes play significant roles in cells. Thus, detecting protein complexes from dynamic PPI networks is realistic. In this paper, we proposed a novel protein complexes prediction algorithm based on core-attachment structure and Pagerank algorithm (PRCA), which run in dynamic PPI networks. This method is divided into three steps. Firstly, calculating the weight value of every protein in dynamic PPI networks to obtain seed proteins. Second, considering triangular structures, cores of protein complexes are acquired. Third, calculating the PageRank value of the adjacent proteins of protein complexes, attachments of protein complexes are appended to their corresponding cores to form protein complexes. This method identifies protein complexes in dynamic PPI networks of DIP, MIPS and Krogan dataset. The experimental results show that PRCA algorithm outperforms other algorithms in precision, recall, f-measure and p-value.