Lakizadeh Amir, Jalili Saeed, Marashi Sayed-Amir
Computer Engineering Department, Tarbiat Modares University, Tehran, Iran.
Computer Engineering Department, Tarbiat Modares University, Tehran, Iran.
J Theor Biol. 2015 Aug 7;378:31-8. doi: 10.1016/j.jtbi.2015.04.020. Epub 2015 Apr 29.
Detection of protein complexes from protein-protein interaction (PPI) networks is essential to understand the function of cell machinery. However, available PPIs are static, and cannot reflect the dynamics inherent in real networks. Our method uses time series gene expression data in addition to PPI networks to detect protein complexes. The proposed method generates a series of time-sequenced subnetworks (TSN) according to the time that the interactions are activated. It finds, from each TSN, the protein complexes by employing the weighted clustering coefficient and maximal weighted density concepts. The final set of detected protein complexes are obtained from union of all complexes from different subnetworks. Our findings suggest that by employing these considerations can produce far better results in protein complex detection problem.
从蛋白质-蛋白质相互作用(PPI)网络中检测蛋白质复合物对于理解细胞机制的功能至关重要。然而,现有的PPI是静态的,无法反映真实网络中固有的动态特性。我们的方法除了使用PPI网络外,还利用时间序列基因表达数据来检测蛋白质复合物。所提出的方法根据相互作用被激活的时间生成一系列时间序列子网(TSN)。它通过采用加权聚类系数和最大加权密度概念,从每个TSN中找到蛋白质复合物。最终检测到的蛋白质复合物集合是通过合并来自不同子网的所有复合物而获得的。我们的研究结果表明,通过考虑这些因素,可以在蛋白质复合物检测问题上产生更好的结果。