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A novel approach to phylogenetic tree construction using stochastic optimization and clustering

机译:基于随机优化和聚类的系统树构建新方法

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Background The problem of inferring the evolutionary history and constructing the phylogenetic tree with high performance has become one of the major problems in computational biology. Results A new phylogenetic tree construction method from a given set of objects (proteins, species, etc.) is presented. As an extension of ant colony optimization, this method proposes an adaptive phylogenetic clustering algorithm based on a digraph to find a tree structure that defines the ancestral relationships among the given objects. Conclusion Our phylogenetic tree construction method is tested to compare its results with that of the genetic algorithm (GA). Experimental results show that our algorithm converges much faster and also achieves higher quality than GA.
机译:背景技术推论进化史和高性能构建系统树的问题已成为计算生物学的主要问题之一。结果提出了一种从给定对象(蛋白质,物种等)集中构建新的系统树的方法。作为蚁群优化的扩展,该方法提出了一种基于有向图的自适应系统发生聚类算法,以找到定义给定对象之间的祖先关系的树结构。结论测试了我们的系统树构建方法,将其结果与遗传算法(GA)进行了比较。实验结果表明,与遗传算法相比,我们的算法收敛速度更快,质量更高。

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