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A Review on Application of Particle Swarm Optimization in Bioinformatics

机译:粒子群算法在生物信息学中的应用综述

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Bioinformatics is an emerging interdisciplinary research area which holds great promise in the advancement of research and development in complex areas, such as medicine, biology, agriculture, environment, public health, drug design and so on. It is a blend of computer science and molecular biology. Most of the problems in bioinformatics are NP hard in nature so researchers have used soft computing and artificial intelligence techniques to solve these problems. Recently, the use of Swarm Intelligence techniques for solving bioinformatics problems has been gaining the attention of researchers because of their ability to generate low cost, approximate, good solutions. Among various algorithms of Swarm Intelligence, Particle Swarm Optimization is used in many applications and has proved to be very effective. This paper reviews and discusses some representative methods to provide inspiring examples to illustrate basic concept of PSO and how PSO had been applied to solve bioinformatics problems. These representative examples include RNA Secondary Structure Prediction, Gene Clustering, Phylogenetic Tree Construction, Energy Minimization and Protein Modeling. The aim of this paper is to provide an overall understanding of PSO and its place in bioinformatics so as to motivate researchers to develop new applications and concepts.
机译:生物信息学是一个新兴的跨学科研究领域,在医学,生物学,农业,环境,公共卫生,药物设计等复杂领域的研究与开发中具有广阔的前景。它融合了计算机科学和分子生物学。生物信息学中的大多数问题本质上都是NP难题,因此研究人员已使用软计算和人工智能技术来解决这些问题。最近,由于群体智能技术能够产生低成本,近似,良好的解决方案,因此使用群体智能技术解决生物信息学问题已引起研究人员的关注。在群体智能的各种算法中,粒子群优化被用于许多应用中,并被证明是非常有效的。本文回顾并讨论了一些有代表性的方法,以提供启发性的示例来说明PSO的基本概念以及PSO如何应用于解决生物信息学问题。这些代表性实例包括RNA二级结构预测,基因聚类,系统树构建,能量最小化和蛋白质建模。本文的目的是全面了解PSO及其在生物信息学中的地位,以激发研究人员开发新的应用程序和概念。

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