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Efficiently Finding the Most Parsimonious Phylogenetic Tree Via Linear Programming

机译:通过线性规划有效地找到最简约的系统发生树

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Reconstruction of phylogenetic trees is a fundamental problem in computational biology. While excellent heuristic methods are available for many variants of this problem, new advances in phylogeny inference will be required if we are to be able to continue to make effective use of the rapidly growing stores of variation data now being gathered. In this paper, we introduce an integer linear programming formulation to find the most parsimonious phylogenetic tree from a set of binary variation data. The method uses a flow-based formulation that could use exponential numbers of variables and constraints in the worst case. The method has, however, proved extremely efficient in practice on datasets that are well beyond the reach of the available provably efficient methods. The program solves several large mtDNA and Y-chromosome instances within a few seconds, giving provably optimal results in times competitive with fast heuristics than cannot guarantee optimality.
机译:系统树的重建是计算生物学中的一个基本问题。尽管出色的启发式方法可用于解决此问题的许多变体,但如果我们要能够继续有效利用目前正在收集的迅速增长的变体数据存储,就需要在系统发育推断方面取得新进展。在本文中,我们介绍了一种整数线性规划公式,可从一组二进制变异数据中找到最简约的系统树。该方法使用基于流的公式,在最坏的情况下可以使用指数形式的变量和约束。但是,该方法在实践中已被证明在数据集上极为有效,而这些数据集远远超出了可证明的有效方法的范围。该程序可在几秒钟内解决多个大型mtDNA和Y染色体实例,在快速试探法无法保证最优性的情况下,在竞争时间上可提供最佳的可证明结果。

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