Gómez-Vela Francisco, Rodriguez-Baena Domingo S, Vázquez-Noguera José Luis
Division of Computer Science, Pablo de Olavide University, 41013 Seville, Spain.
Carrera de Ingeniería Informática, Universidad Americana, Asunción, Paraguay.
Comput Math Methods Med. 2018 Jun 14;2018:9674108. doi: 10.1155/2018/9674108. eCollection 2018.
In the last few years, gene networks have become one of most important tools to model biological processes. Among other utilities, these networks visually show biological relationships between genes. However, due to the large amount of the currently generated genetic data, their size has grown to the point of being unmanageable. To solve this problem, it is possible to use computational approaches, such as heuristics-based methods, to analyze and optimize gene network's structure by pruning irrelevant relationships. In this paper we present a new method, called GeSOp, to optimize large gene network structures. The method is able to perform a considerably prune of the irrelevant relationships comprising the input network. To do so, the method is based on a greedy heuristic to obtain the most relevant subnetwork. The performance of our method was tested by means of two experiments on gene networks obtained from different organisms. The first experiment shows how GeSOp is able not only to carry out a significant reduction in the size of the network, but also to maintain the biological information ratio. In the second experiment, the ability to improve the biological indicators of the network is checked. Hence, the results presented show that GeSOp is a reliable method to optimize and improve the structure of large gene networks.
在过去几年中，基因网络已成为模拟生物过程的最重要工具之一。在其他用途中，这些网络直观地展示了基因之间的生物学关系。然而，由于当前生成的遗传数据量巨大，它们的规模已经增长到难以管理的程度。为了解决这个问题，可以使用计算方法，如基于启发式的方法，通过去除不相关的关系来分析和优化基因网络的结构。在本文中，我们提出了一种名为GeSOp的新方法，用于优化大型基因网络结构。该方法能够对构成输入网络的不相关关系进行大幅删减。为此，该方法基于一种贪婪启发式算法来获取最相关的子网。我们通过对从不同生物体获得的基因网络进行的两个实验来测试我们方法的性能。第一个实验展示了GeSOp不仅能够大幅减小网络规模，还能保持生物学信息比例。在第二个实验中，检查了改善网络生物学指标的能力。因此，所呈现的结果表明GeSOp是一种优化和改善大型基因网络结构的可靠方法。
