基于多属性决策的关键蛋白质识别算法研究

摘要

Essential proteins play an important role in biological life cycle,and always are important nodes in protein protein interaction (PPI) networks.To identify essential proteins in PPI networks,a multi attribute decision based method for essential protein identification (CBT-Topsis) is proposed,in which a local centrality (LC) a node in network is defined to measure the local importance of a node.Then TOPSIS method is introduced into CBT-Topsis to evaluate the importance of a protein in PPI networks by combining some vital metrics such as local centrality,clustering,betweeness centrality and in-degree of complex.The results on Saccharomyces Cerevisiae show that the proposed CBT-TOPSIS shows a considerable or better performance on F-measure,accuracy,separation and sensitivity.%关键蛋白质往往通过蛋白质复合物的形式在生物生命活动中扮演着重要作用,在蛋白质互作用(PPI,Protein-Protein Interaction)网络中关键蛋白质对应互作用网络中的重要节点,基于此,提出了一种融合蛋白质拓扑结构属性信息和蛋白质复合物信息的基于多属性决策的关键蛋白质识别算法CBT Topsis (Topsis based method for Essential Protein Identification on Complex Biological and Topological properties).该算法采用多属性决策方法TOPSIS将节点局部重要性(LN)、聚集系数(CC)、点介数(BC)和蛋白质复合物内度中心(IDC)进行融合,根据节点重要性对PPI网络中的蛋白质进行排序.在酿酒酵母蛋白质互作用网络中进行关键蛋白质识别的结果表明,CBT-TOPSIS算法在F度量、准确率、特异性、敏感度等方面表现了良好的性能.