基于文化算法的PPI网络功能模块检测方法

为了解决蛋白质相互作用(protein-protein interaction,PPI)网络功能模块检测问题,提出一种基于文化算法的PPI网络功能模块检测(CA-FMD)方法.首先,每个个体采用基于节点邻居有序表的编码方式表示功能模块检测问题的一个可行解.然后,利用文化算法的双层进化机制获得最优解,其中,上层机制用来模拟信念空间中群体经验的进化,下层机制用来刻画种群空间中个体的进化.最后,借助2个空间的相互作用和影响完成解的优化.在3个数据集上的实验结果表明:与其他算法相比,CA-FMD方法在多项评价指标上都具有明显的优势.

Detecting Functional Module Method Based on Cultural Algorithm in Protein-protein Interaction Networks

To achieve function module detection in protein-protein interaction ( PPI ) networks, a PPI network functional module detection method based on cultural algorithm ( CA-FMD) was proposed. First, an ordered adjacency list encoding scheme was used to model an individual in the population space. Then, the evolutionary mechanism of cultural algorithm was designed and employed to obtain the optimal solution, where the upper mechanism simulated the evolution of the group experience in the belief space, and the lower mechanism described the evolution of individuals in the population space. Finally, the optimation of solutions was completed by the interaction and influence of the two spaces. Experimental results on three datasets show that the CA-FMD method has obvious advantages in some evaluation metrics compared with other algorithms.