RESEARCH ARTICLE


Bibliometric and Semantic Analysis of the Global Research on Biomarkers in Personalized Medicine



Aida Khakimova1, *, Fakher Rahim2, Oleg Zolotarev3
1 Department of Development of Scientific and Innovation Activities, Russian New University, Moscow, Russia
2 Health Research Institute, Thalassemia and Hemoglobinopathies Research Center, Ahvaz University of Medical Sciences, Ahvaz, Iran
3 Department of Information Systems in Economics and Management, Russian New University, Moscow, Russia


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Creative Commons License
© 2022 Khakimova et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Development of Scientific and Innovation Activities, Russian New University, Moscow, Russia; Tel: +79053729615; E-mail: aida_khatif@mail.ru


Abstract

Aim:

The aims of the research were to study the citation history of popular articles in the field of biomarkers in personalized medicine, to study the use of terms in the sections of articles, and to consider the key terminology of the most-cited articles and its visualization.

Background:

The article describes approaches to the analysis of publication activity in the field of biomarkers and personalized medicine based on the data from the Web of Science.

Objective:

The aim of this study is a bibliometric and semantic analysis of the investigation field related to the application of biomarkers for the purposes of personalized medicine.

Methods:

The evaluation of a number of publications and its’ citations was carried out. The key terms extracted from the most-cited articles were divided into thematic groups. The number of citations of the most popular articles since 2011 was estimated.

Results:

The citation histories of the top ten articles were considered. Analysis of key terms from different parts of the most-cited articles included statistics and thematic ranking. The comparison of key terms from the most-cited article and the citing articles allowed us to show that the key terminology of the cited article extends to the citing articles. We presented the key terms of the most-cited articles as a terminological map.

Conclusion:

The study of citation of the articles in the field of personalized medicine and biomarkers was based on a survey on the Web of Science. Based on the analysis of a number of citations the trends and citation histories were constructed. The statistical and thematic analysis of the use of keywords in different sections of articles was done. We have shown that the citing articles spread the key terms of the cited article to identify trends in knowledge development which could be presented as a terminological map.

Others:

We presented the results in the form of a terminological map of the latest developments in the field of biomarkers in personalized medicine based on proposed principles.

Keywords: Bibliometrics, Research trends, Biomarkers, Personalized medicine, Key terms, Citation.