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arXiv 1805.00548q-bio.PE
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最大简约下系统发育树中祖先状态预测精度的量化

Quantifying the accuracy of ancestral state prediction in a phylogenetic tree under maximum parsimony

Lina Herbst, Thomas Li, Mike Steel

摘要:

在系统发育研究中,生物学家经常希望从在树的叶子上观察到的状态估计进化树$T$的内部顶点$v$的祖先离散特征状态。一种简单而快速的估计方法-最大简约法-将$v$上的祖先状态取为任何状态,该状态使解释$T$上的演化所需的$T$中的状态变化的数量最小化。在本文中,我们进一步研究了这种估计方法的重建精度,在一个简单的对称模型的状态变化,并获得了一些新的结果,无论是2-状态字符,和$r$ -状态字符($r>2$)。我们的研究结果依赖于建立新的身份和不平等,耦合参数的基础上,涉及一个更简单的“抛硬币”的方法来重建祖先的国家。

英文摘要:

In phylogenetic studies, biologists often wish to estimate the ancestral discrete character state at an interior vertex $v$ of an evolutionary tree $T$ from the states that are observed at the leaves of the tree. A simple and fast estimation method --- maximum parsimony --- takes the ancestral state at $v$ to be any state that minimises the number of state changes in $T$ required to explain its evolution on $T$. In this paper, we investigate the reconstruction accuracy of this estimation method further, under a simple symmetric model of state change, and obtain a number of new results, both for 2-state characters, and $r$--state characters ($r>2$). Our results rely on establishing new identities and inequalities, based on a coupling argument that involves a simpler `coin toss' approach to ancestral state reconstruction.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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