School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China.
Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computing Intelligence, Jiangnan University, Wuxi, Jiangsu 214122, China.
Comput Math Methods Med. 2021 Nov 3;2021:5548993. doi: 10.1155/2021/5548993. eCollection 2021.
The development of high-throughput technology has provided a reliable technical guarantee for an increased amount of available data on biological networks. Network alignment is used to analyze these data to identify conserved functional network modules and understand evolutionary relationships across species. Thus, an efficient computational network aligner is needed for network alignment. In this paper, the classic bat algorithm is discretized and applied to the network alignment. The bat algorithm initializes the population randomly and then searches for the optimal solution iteratively. Based on the bat algorithm, the global pairwise alignment algorithm BatAlign is proposed. In BatAlign, the individual velocity and the position are represented by a discrete code. BatAlign uses a search algorithm based on objective function that uses the number of conserved edges as the objective function. The similarity between the networks is used to initialize the population. The experimental results showed that the algorithm was able to match proteins with high functional consistency and reach a relatively high topological quality.
高通量技术的发展为生物网络的大量可用数据提供了可靠的技术保障。网络比对用于分析这些数据，以识别保守的功能网络模块，并了解跨物种的进化关系。因此，需要一个有效的计算网络比对器来进行网络比对。在本文中，经典的蝙蝠算法被离散化并应用于网络比对。蝙蝠算法随机初始化种群，然后迭代搜索最优解。基于蝙蝠算法，提出了全局成对比对算法 BatAlign。在 BatAlign 中，个体速度和位置由离散代码表示。BatAlign 使用基于目标函数的搜索算法，将保留边的数量作为目标函数。网络之间的相似性用于初始化种群。实验结果表明，该算法能够匹配具有高度功能一致性的蛋白质，并达到相对较高的拓扑质量。
