摘要
提出了一种基于人工免疫特性的蛋白质相互作用(PPI)网络聚类模型与算法以期提高其辨识准确率。在该算法中将聚类中心作为抗原,将邻接的节点作为抗体,通过计算抗体与抗原之间的亲和度,将其作为记忆细胞把节点划分到聚类中;然后选择优秀抗体作为疫苗,尝试将疫苗注入聚类模块并进行更新,通过与注射前的模块适应度进行比较,不断更新记忆细胞。对PPI数据集上的数据进行了仿真,实验结果表明,与功能流算法(FLOW)相比,所提方法的正确率和查全率的几何平均值均得到了提高。
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.
出处
《计算机应用》
CSCD
北大核心
2013年第12期3567-3570,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(61100164
61173190)
教育部留学回国人员科研启动基金资助项目(教外司留[2012]1707号)
陕西省2010年自然科学基础研究计划青年基金资助项目(2010JQ8034)
中央高校基本科研业务费专项资金资助项目(GK201302025)
关键词
人工免疫系统
蛋白质相互作用网络
聚类
记忆细胞
疫苗
Artificial Immune System （AIS）
Protein-Protein Interaction （PPI） network
clustering
memory cells
vaccines
