系统发生学中树分叉和重新连接(TBR)距离的深度核化

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE Journal of Computer and System Sciences Pub Date : 2024-01-26 DOI:10.1016/j.jcss.2024.103519
Steven Kelk , Simone Linz , Ruben Meuwese
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引用次数: 0

摘要

我们描述了一个大小为 9k-8 的内核,可用于计算两个无根二叉系统发育树之间的树分叉和重新连接(TBR)距离 k 这一 NP 难问题。为此,我们扩展了现有的还原规则组合,增加了三个新的还原规则。其中两条规则是基于以保留距离的方式对树进行拓扑转换的想法,以保证执行之前的缩减规则。第三条规则将文献[20]中引入的局部邻域方法扩展到了更多的全局结构中,从而可以识别出当删除一片树叶时,TBR 距离肯定会减少一个的新情况。对内核大小的约束是严格的,直到一个加法项为止。我们的结果也适用于计算两棵无根二元系统发育树之间的最大一致森林(MAF)的等价问题。我们预计,我们的结果将更广泛地适用于计算基于协议林的异质性度量。
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Deep kernelization for the Tree Bisection and Reconnection (TBR) distance in phylogenetics

We describe a kernel of size 9k8 for the NP-hard problem of computing the Tree Bisection and Reconnection (TBR) distance k between two unrooted binary phylogenetic trees. To achieve this, we extend the existing portfolio of reduction rules with three new reduction rules. Two of these are based on the idea of topologically transforming the trees in a distance-preserving way in order to guarantee execution of earlier reduction rules. The third rule extends the local neighborhood approach introduced in [20] to more global structures, allowing new situations to be identified when the deletion of a leaf definitely reduces the TBR distance by one. The bound on the kernel size is tight up to an additive term. Our results also apply to the equivalent problem of computing a maximum agreement forest between two unrooted binary phylogenetic trees. We anticipate that our results are widely applicable for computing agreement-forest based dissimilarity measures.

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来源期刊
Journal of Computer and System Sciences
Journal of Computer and System Sciences 工程技术-计算机:理论方法
CiteScore
3.70
自引率
0.00%
发文量
58
审稿时长
68 days
期刊介绍: The Journal of Computer and System Sciences publishes original research papers in computer science and related subjects in system science, with attention to the relevant mathematical theory. Applications-oriented papers may also be accepted and they are expected to contain deep analytic evaluation of the proposed solutions. Research areas include traditional subjects such as: • Theory of algorithms and computability • Formal languages • Automata theory Contemporary subjects such as: • Complexity theory • Algorithmic Complexity • Parallel & distributed computing • Computer networks • Neural networks • Computational learning theory • Database theory & practice • Computer modeling of complex systems • Security and Privacy.
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