利用三角生物医学框架分析引文对PubMed数据库分类传播的影响

IF 3.5 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics Pub Date : 2025-02-25 DOI:10.1016/j.joi.2025.101648

Gerson Pech , Aleksandra Mreła , Veslava Osińska , Oleksandr Sokolov

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引用次数: 0

摘要

处理科学文献元数据使我们能够验证文章对预定义类别的分配。生物医学三角（TB）是根据人类、动物和分子细胞分支学科考虑生物医学论文位置的方便空间。使用引用的PubMed论文在TB中的位置，以及更有趣的是，论文位置变化的动态（由于引用）至今尚未得到检验。本文提出了一种以PubMed数据库提供的MeSH术语共享为组成部分的直接被引论文的引文向量查找方法。引文向量允许找到论文在TB中的位置，并将其与出版物的原始位置进行比较。对引用向量集的分析可以定位它们在翻译线上的位置，以显示人类研究与动物分子研究之间的距离。此外，利用信息熵，研究了四组不同文章的熵动态。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

The triangle of biomedicine framework to analyze the impact of citations on the dissemination of categories in the PubMed database

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The triangle of biomedicine framework to analyze the impact of citations on the dissemination of categories in the PubMed database

Processing scientific literature metadata allows us to verify the assignment of articles to predefined categories. The Triangle of Biomedicine (TB) is a convenient space for considering the positions of biomedical papers according to human, animal, and molecular-cellular subdisciplines. The placement of PubMed papers in the TB using citations and, what is more interesting, the dynamics of the changing positions of papers (because of citations) have not been examined to date. This research presents a method for finding the article citation vectors of directly cited papers whose components are the MeSH terms shares offered by the PubMed database. The citation vectors allow finding the paper's position in the TB and comparing it with the original position of the publication. The analysis of sets of citation vectors enables locating their position on the translational line to show the distance between human research and animal-molecular studies. Moreover, applying information entropy, the dynamics of entropies in four different sets of articles are studied.

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来源期刊

Journal of Informetrics Social Sciences-Library and Information Sciences

CiteScore

6.40

自引率

16.20%

发文量

期刊介绍： Journal of Informetrics (JOI) publishes rigorous high-quality research on quantitative aspects of information science. The main focus of the journal is on topics in bibliometrics, scientometrics, webometrics, patentometrics, altmetrics and research evaluation. Contributions studying informetric problems using methods from other quantitative fields, such as mathematics, statistics, computer science, economics and econometrics, and network science, are especially encouraged. JOI publishes both theoretical and empirical work. In general, case studies, for instance a bibliometric analysis focusing on a specific research field or a specific country, are not considered suitable for publication in JOI, unless they contain innovative methodological elements.