SabziNezhad Ali, Jalili Saeed
Computer Engineering Department, Tarbiat Modares University, Tehran, Iran.
Front Genet. 2020 Jun 26;11:567. doi: 10.3389/fgene.2020.00567. eCollection 2020.
Detecting protein complexes from the Protein-Protein interaction network (PPI) is the essence of discovering the rules of the cellular world. There is a large amount of PPI data available, generated from high throughput experimental data. The enormous size of the data persuaded us to use computational methods instead of experimental methods to detect protein complexes. In past years, many researchers presented their algorithms to detect protein complexes. Most of the presented algorithms use current static PPI networks. New researches proved the dynamicity of cellular systems, and so, the PPI is not static over time. In this paper, we introduce DPCT to detect protein complexes from dynamic PPI networks. In the proposed method, TAP and GO data are used to make a weighted PPI network and to reduce the noise of PPI. Gene expression data are also used to make dynamic subnetworks from PPI. A memetic algorithm is used to bicluster gene expression data and to create a dynamic subnetwork for each bicluster. Experimental results show that DPCT can detect protein complexes with better correctness than state-of-the-art detection algorithms. The source code and datasets of DPCT used can be found at https://github.com/alisn72/DPCT.
从蛋白质-蛋白质相互作用网络（PPI）中检测蛋白质复合物是揭示细胞世界规律的关键所在。现有大量由高通量实验数据生成的PPI数据。数据量巨大促使我们采用计算方法而非实验方法来检测蛋白质复合物。在过去几年中，许多研究人员提出了他们检测蛋白质复合物的算法。大多数已提出的算法使用当前的静态PPI网络。新的研究证明了细胞系统的动态性，因此，PPI并非随时间保持静态。在本文中，我们引入了DPCT来从动态PPI网络中检测蛋白质复合物。在所提出的方法中，TAP和GO数据用于构建加权PPI网络并减少PPI的噪声。基因表达数据也用于从PPI中构建动态子网。一种混合算法用于对基因表达数据进行双聚类，并为每个双聚类创建一个动态子网。实验结果表明，DPCT能够比现有最先进的检测算法更准确地检测蛋白质复合物。所使用的DPCT的源代码和数据集可在https://github.com/alisn72/DPCT上找到。
