关于 DNA 链间距离矩阵恢复的更多信息:可靠性系数

Q3 Physics and Astronomy
Mikhail Abramyan, Boris Melnikov, Ye Zhang
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引用次数: 0

摘要

这篇文章是对作者之前有关恢复距离矩阵研究的延续。这种还原的主要困难在于无法使用梯度下降算法的变体等传统技术,因为会产生太多变量。此外,在本文中,我们也没有使用分支与边界法的变体算法,在我们以前的出版物中,有时会将分支与边界法用于本文所考虑的问题;不使用分支与边界法的主要理由是,使用分支与边界法几乎无法提供有关该算法所产生的子任务的信息,特别是很难充分评估所产生的边界的真实值。因此,我们采用了所谓的逐步填充距离矩阵的方法。在本文介绍的方法中,我们认为初始距离形成算法以最佳方式运行。因此,我们有条件地认为,相应的坏度值无法改进。这一假设确实使得改进坏度值成为可能。因此,我们在本文中获得了重建距离矩阵的实际结果,这大大改进了我们之前论文中给出的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Some more on restoring distance matrices between DNA chains: reliability coefficients
This article is a description of the continuation of previous research by the authors related to the restoration of distance matrices. The main difficulty that arises with such a recovery is that it is impossible to use conventional techniques such as variants of gradient descent algorithms, since too many variables would arise. In addition, in this article we also do not use algorithms for variants of the branch and boundary method, which in our previous publications were sometimes used for the problem considered in the article; the main argument for not using it is that using it would provide little information about the subtasks generated by this algorithm, in particular, it is difficult to adequately assess the real the values of the resulting boundaries. Therefore, we use the so-called step-by-step filling of the distance matrix. In the approach presented in the paper we consider, that the algorithm of the initial distance formation works in the best way. Therefore, we conditionally believe that the corresponding value of badness cannot be improved. This assumption really makes it possible to improve the value of badness. Thus, in this paper we have obtained practical results of reconstructing distance matrices, which significantly improve the results given in our previous papers.
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来源期刊
Cybernetics and Physics
Cybernetics and Physics Chemical Engineering-Fluid Flow and Transfer Processes
CiteScore
1.70
自引率
0.00%
发文量
17
审稿时长
10 weeks
期刊介绍: The scope of the journal includes: -Nonlinear dynamics and control -Complexity and self-organization -Control of oscillations -Control of chaos and bifurcations -Control in thermodynamics -Control of flows and turbulence -Information Physics -Cyber-physical systems -Modeling and identification of physical systems -Quantum information and control -Analysis and control of complex networks -Synchronization of systems and networks -Control of mechanical and micromechanical systems -Dynamics and control of plasma, beams, lasers, nanostructures -Applications of cybernetic methods in chemistry, biology, other natural sciences The papers in cybernetics with physical flavor as well as the papers in physics with cybernetic flavor are welcome. Cybernetics is assumed to include, in addition to control, such areas as estimation, filtering, optimization, identification, information theory, pattern recognition and other related areas.
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