基于人工免疫系统的蛋白质-蛋白质相互作用网络聚类

Wang Chong, L. Xiujuan
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引用次数: 1

摘要

为了提高识别精度,提出了一种蛋白质-蛋白质相互作用(PPI)网络聚类模型和基于人工免疫系统(AIS)机制的算法。该算法将聚类中心集视为抗原,将相邻节点视为抗体。通过计算抗体与抗原的亲和力,将抗体作为集群的记忆细胞。然后选择优秀的抗体作为疫苗,注入聚类模块进行更新。最后在注射前比较各模块的适应度,更新记忆细胞。在PPI数据集上的仿真结果表明,与FLOW算法相比,新算法的精度f值和召回值都得到了提高。
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Protein-protein interaction network clustering based on artificial immune system
A Protein-Protein Interaction(PPI) network clustering model and an algorithm based on the mechanism of the Artificial Immune System(AIS) were proposed to improve the identification accuracy. In this algorithm,the set of cluster centers was regarded as antigens and the neighbor nodes were regarded as antibodies. The antibodies were regarded as the memory cells of clusters by calculating the affinity between the antibodies and antigens. Then excellent antibodies were selected as vaccines,and they were injected into clustering modules to get update. Finally the memory cells were updated after comparing the fitness of the modules before injection. The simulation results on PPI datasets show that,compared with FLOW algorithm,the f-measure of precision and recall value of the new algorithm have got improved.
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