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    基于动态网络切分的关键蛋白质预测方法

    Essential Proteins Prediction Method Based on Dynamic Network Segmentation

    • 摘要: 关键蛋白质作为蛋白质中的关键物质,不仅对研究细胞生长调控有着重要意义,也为更深层次的疾病研究奠定理论基础.目前,针对关键蛋白质的识别方法大多为应用基因表达信息和蛋白质相互作用网络,提出识别关键蛋白质的静态和动态网络方法,但这些方法未考虑基因表达调控的周期性规律,无法准确地刻画受基因周期调控的蛋白质网络.为此,在基因表达动态性的基础上引入了基因周期性表达的概念,提出了一种动态网络切分方法.该方法通过构建基因“活性”表达矩阵,利用切分后的“活性”表达矩阵作用于蛋白质相互作用网络,从而形成蛋白质周期子网络,最终综合各周期子网络来衡量蛋白质结点在网络中的重要性.实验结果表明,该方法在酵母、大肠杆菌和人类膀胱数据中可以有效地提高关键蛋白质预测率.

       

      Abstract: Essential proteins, as the essential substances in proteins, are not only of great importance in studying the regulation of cell growth, but also lay a theoretical foundation for the further study of diseases. At present, most of the methods for protein identification are static and dynamic network methods based on gene expression information and protein-protein interaction (PPI) network, but these methods do not consider the periodicity of gene expression regulation, and cannot accurately describe the protein networks periodically regulated by genes. Therefore, the concept of periodic gene expression is introduced on the basis of dynamic gene expression, and a dynamic network segmentation method is proposed. In this method, the noise data in the gene expression data is filtered by constructing the gene “active” expression matrix and the expression at each moment is classified into “active” and “inactive” expression states. The periods are divided according to the gene “active” expression matrix to characterize the dynamic changes of gene expression over continuous time periods. The segmented “active” expression matrix is applied to act on the protein-protein interaction network to generate the protein periodic subnetworks. Finally, the importance of the protein nodes in the network is measured by integrating each protein periodic subnetwork. The experimental results show that the method can effectively improve the prediction rate of essential proteins in yeast, E.coli and human bladder data.

       

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