多粒度PPI网络描述模型及其蚁群优化的功能模块检测方法

A multiple grain representation model of PPI networks and its ant colony optimization method for functional module detection

针对蚁群算法在大规模蛋白质相互作用(protein-protein interaction,PPI)网络中进行功能模块检测所暴露的时间性能方面的不足,提出了一种基于多粒度描述和蚁群优化的快速求解算法。首先,从粒度计算的角度,给出了一种新的多粒度PPI网络描述模型;然后,基于该模型,设计了融合功能和结构信息的粒度划分,粗粒度的蚁群寻优,解的还原与优化3个阶段的求解过程。在大规模PPI网络上的实验表明:算法在保证检测质量的同时,能显著降低利用蚁群算法进行功能模块检测的求解时间,而且与近年来的一些经典算法相比在检测精度上也具有一定的优势。

The time performance of ant colony optimization （ACO）algorithm is unqualified to detect functional modules in large scale PPI networks.A fast approach based on multiple grain representation and ant colony optimization is proposed.Firstly,a novel multiple grain representation model of PPI networks is proposed from the perspective of granular computing.A new algo-rithm is designed,containing three phases：a granularity partition process integrating functional and topological message,an ACO process on the coarse grain network,and a refinement and optimization process for solutions,is presented.In the experiments for large scale PPI networks,compared with some latest classical algorithms,the proposed algorithm greatly improve the speed of the algorithm using ACO to detect functional modules and presents competitive detection quality.