定向无向系统发育网络

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE Journal of Computer and System Sciences Pub Date : 2023-10-04 DOI:10.1016/j.jcss.2023.103480
Katharina T. Huber , Leo van Iersel , Remie Janssen , Mark Jones , Vincent Moulton , Yukihiro Murakami , Charles Semple
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引用次数: 0

摘要

本文研究了无向(无根)和有向(有根)系统发育网络之间的关系。我们描述了一种多项式时间算法,用于决定在给定根和网状顶点位置的情况下,无向非二元系统发育网络是否可以定向为有向非二次系统发育网络。此外,我们描述了何时这是可能的,并表明在这种情况下,由此产生的定向非二元系统发育网络是独特的。此外,在不给出根和网状顶点的位置的情况下,我们描述了一种算法,用于确定无向二元系统发育网络N是否可以定向为某一类的有向二元系发育网络。当参数为N级时,该算法是固定参数可处理的(FPT),适用于满足一定条件的有向系统发育网络类别。作为一个例子,我们证明了一类研究良好的二叉树子网络满足这些条件。
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Orienting undirected phylogenetic networks

This paper studies the relationship between undirected (unrooted) and directed (rooted) phylogenetic networks. We describe a polynomial-time algorithm for deciding whether an undirected nonbinary phylogenetic network, given the locations of the root and reticulation vertices, can be oriented as a directed nonbinary phylogenetic network. Moreover, we characterize when this is possible and show that, in such instances, the resulting directed nonbinary phylogenetic network is unique. In addition, without being given the location of the root and the reticulation vertices, we describe an algorithm for deciding whether an undirected binary phylogenetic network N can be oriented as a directed binary phylogenetic network of a certain class. The algorithm is fixed-parameter tractable (FPT) when the parameter is the level of N and is applicable to classes of directed phylogenetic networks that satisfy certain conditions. As an example, we show that the well-studied class of binary tree-child networks satisfies these conditions.

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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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