An Algorithm to Calculate the p-Value of the Monge-Elkan Distance.

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Petr Ryšavý, Filip Železný
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引用次数: 0

Abstract

The Monge-Elkan distance is a straightforward yet popular distance measure used to estimate the mutual similarity of two sets of objects. It was initially proposed in the field of databases, and it found broad usage in other fields. Nowadays, it is especially relevant to the analysis of new-generation sequencing data as it represents a measure of dissimilarity between genomes of two distinct organisms, particularly when applied to unassembled reads. This article provides an algorithm to calculate the p-value associated with the Monge-Elkan distance. Given the object-level null distribution, that is, the distribution of distances between independently and identically sampled objects such as reads, the method yields the null distribution of the Monge-Elkan distance, which in turn allows for calculating the p-value. We also demonstrate an application on sequencing data, where individual reads are compared by the Levenshtein distance.

一种计算Monge-Elkan距离p值的算法。
Monge-Elkan距离是一种简单而流行的距离度量,用于估计两组物体的相互相似性。它最初是在数据库领域提出的,并在其他领域得到了广泛的应用。如今,它与新一代测序数据的分析尤其相关,因为它代表了两种不同生物基因组之间的差异,特别是当应用于未组装读取时。本文提供了一种计算与Monge-Elkan距离相关的p值的算法。给定对象级零分布,即独立和相同采样对象(如read)之间的距离分布,该方法产生Monge-Elkan距离的零分布,从而允许计算p值。我们还演示了在测序数据上的应用,其中通过Levenshtein距离对单个读取进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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