基于团渗透和距离限制的蛋白质复合物识别算法

算法CPM (clique percolation method)作为一种有效的识别复杂网络中交叠模块结构的算法在社会网络和生物网络中得到了广泛应用. 但, CPM算法应用于蛋白质相互作用网络时蛋白质复合物识别准确率不高, 且不利于识别规模适中的蛋白质复合物. 为克服CPM算法的不足, 本文通过引入距离限制约束识别的蛋白质复合物的规模, 进而提出了一种基于团 渗透和距离限制的蛋白质复合物识别算法CPM-DR. 基于酵母蛋白质相互作用网络平台的实验结果表明, 算法CPM-DR比CPM能够更准确、更有效、更全面的识别出具有特定生物意义的蛋白质复合物.

An algorithm for identifying protein complexes based on clique percolation and distance restriction

Identification of protein complexes in large interaction networks is crucial to understand principles of cellular organization and predict protein functions,which is one of the most important issues in the post-genomic era.Each protein might subordinate multiple protein complexes in the real proteinprotein interaction networks.Identifying overlapping protein complexes from protein-protein interaction networks is a considerable research topic.As an effective algorithm on identifying overlapping module structures,clique percolation method(CPM) has a wide range of application in social networks and biological networks.However,CPM algorithm is recognition accuracy rate lowly and unfit to identifying protein complexes with middling scale when it applied in PPI networks.In this paper,an algorithm called CPM-DR for identifying protein complexes based on clique percolation and distance restriction is proposed. In this algorithm,the scale of protein complex is restricted by distance constraint to conquest the drawbacks in CPM.The experiment results show that CPM-DR algorithm can identify many well known protein complexes more effectively,precisely and comprehensively.