A genetic-based approach for discovering pathways in protein-protein interaction networks

Nguyen Hoai Anh, C. Vu, Tu Minh Phuong, L. Bui
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引用次数: 3

Abstract

This paper introduces an approach of using the genetic algorithm for orienting protein-protein interaction networks (PPIs) and discovering pathways. Biological pathways such as metabolic or signaling ones play an important role in understanding cell activities and evolution. A cost-effective method to discover such pathways is analyzing accumulated information about protein-protein interactions, which are usually given in forms of undirected networks or graphs. Previous findings show that orienting protein interactions can improve pathway discovery. However, assigning orientation for protein interactions is a combinatorial optimization problem which has been proved to be NP-hard, making it critical to develop efficient algorithms. For our proposal, we first study the mathematical model of the problem. Then, based on this model, a genetic algorithm is designed to find the solution for the problem. We conducted multiple runs on the data of yeast PPI networks to test the best option for the problem. The preliminary results were compared with the results of the random search algorithm, which was shown to the best in dealing with this problem, in terms of the run time, fitness function values, especially the ratio of gold standard pathways. The findings show that our genetic-based approach addressed this problem better than the random search algorithm did.
发现蛋白质-蛋白质相互作用网络通路的一种基于遗传学的方法
本文介绍了一种利用遗传算法定位蛋白质-蛋白质相互作用网络(PPIs)和发现途径的方法。代谢或信号通路等生物学途径在理解细胞活动和进化中起着重要作用。发现这种途径的一种经济有效的方法是分析蛋白质-蛋白质相互作用的累积信息,这些信息通常以无向网络或图的形式给出。先前的研究结果表明,定向蛋白质相互作用可以改善途径发现。然而,为蛋白质相互作用分配方向是一个组合优化问题,已被证明是np困难的,因此开发有效的算法至关重要。对于我们的建议,我们首先研究问题的数学模型。然后,在此模型的基础上,设计遗传算法求解问题。我们对酵母PPI网络的数据进行了多次运行,以测试解决问题的最佳方案。将初步结果与随机搜索算法的结果进行了比较,结果表明,随机搜索算法在运行时间、适应度函数值,特别是金标准路径的比例方面,都是处理该问题的最佳算法。研究结果表明,我们基于基因的方法比随机搜索算法更好地解决了这个问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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