祖先-后代距离下的共识树是NP-Hard的。

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS Journal of Computational Biology Pub Date : 2024-01-01 Epub Date: 2023-11-28 DOI:10.1089/cmb.2023.0262
Yuanyuan Qi, Mohammed El-Kebir
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引用次数: 0

摘要

由于从测序数据推断肿瘤系统发育的不确定性,许多方法推断出同一种癌症的多个同样可信的系统发育。为了总结肿瘤系统发育的解空间T,共识树方法在指定的两两树距离函数下寻求单个最佳代表树S。一个这样的距离函数是祖先-后代(AD)距离[公式:见文],它等于边集[公式:见文]和[公式:见文]的传递闭包的对称差的大小。在这里,我们证明了找到肿瘤系统发育T的共识树S,使总AD距离∑T∈Td(S,T)最小是np困难的。
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Consensus Tree Under the Ancestor-Descendant Distance is NP-Hard.

Due to uncertainty in tumor phylogeny inference from sequencing data, many methods infer multiple, equally plausible phylogenies for the same cancer. To summarize the solution space T of tumor phylogenies, consensus tree methods seek a single best representative tree S under a specified pairwise tree distance function. One such distance function is the ancestor-descendant (AD) distance [Formula: see text] , which equals the size of the symmetric difference of the transitive closures of the edge sets [Formula: see text] and [Formula: see text] . Here, we show that finding a consensus tree S for tumor phylogenies T that minimizes the total AD distance [Formula: see text] is NP-hard.

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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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