基于动态加权PPI网络的关键蛋白质识别算法.

Compared with static PPI network, the dynamic PPI network can reflect the real situation of interactions among proteins and effectively reduce the false negation in PPI network. Most of the existing methods are based on static PPI network, which neglect the inherent dynamics of PPI network. To effectively predict the essential proteins, this paper extracted the dynamic information of proteins from gene expression data and integrated with static PPI network to construct dynamic PPI, then weighting the interaction between proteins based on GO term similarity and introduced a new method named DWE. This method assessed the important of one protein through the ratio of the sum of the dynamic weighted edge connecting this protein and the number of temporal networks that contained this protein. The result shows that dynamic weighted PPI network help to improve the prediction accuracy of essential proteins and DWE outperforms other methods. [ABSTRACT FROM AUTHOR]

与静态PPI网络相比,动态PPI网络更能体现蛋白质之间相互作用的真实情况,并有效降低PPI网络中的假阴性。现有的关键蛋白质预测方法主要应用在静态PPI网络,忽视了PPI网络的动态特性。为有效预测关键蛋白质,利用基因表达数据提取蛋白质的动态信息,再结合静态PPI网络构建动态PPI网络,然后引入GO术语对网络加权,并基于动态加权PPI网络提出一种新的预测方法——DWE。该方法以蛋白质在动态网络中的动态加权边之和与蛋白质在动态网络中出现的次数的比值衡量蛋白质在网络中的关键性。实验结果表明动态加权PPI网络有助于提高关键蛋白质的预测精度,且DWE方法优于其他几种关键蛋白质预测方法。 [ABSTRACT FROM AUTHOR]