Study of the concepts’ knowledge map in bioinformatics based on the indexed articles in Clarivate database

An examination of the thematic process of the articles reveals the process of thematic growth and development of a scientific field over time. This process enables researchers to compare the research conducted in thematic aspects and certain periods in each field. Thus, they can study and analyze the relationships of the concepts with the help of a knowledge map. The knowledge map identifies concepts and connections between concepts in a scientific field. Illustrating the internal structure of a scientific field helps users to quickly have a clear understanding of the structure of the field by observing the concepts, relations, and distances. One of the main motivations for this research was the ambiguity of the main and important concepts in bioinformatics. Also, the uncertainty of the relationship between the thematic concepts of the published articles was another issue that led to this research. As a result, the uncertainty of the relationship between the concepts used in the articles in bioinformatics led to doing this research. In this study, an attempt was made to identify the main topics (core) considered by researchers and specialists in this field by examining the thematic process of articles. Also, the relationships of these concepts should be drawn in the form of thematic maps and knowledge maps. In the present study, all articles related to bioinformatics, which has been published for about 43 years and has been indexed in the Clarivate database, were reviewed. The present study aimed to draw a thematic map and knowledge of bioinformatics articles with the help of co-word analysis and using semantic exploration tools.
This study is descriptive-analytical research performed by using co-word analysis and semantic search tools called Yewno to draw thematic maps and knowledge of bioinformatics. The research community of the present study was all articles in the field of bioinformatics that were indexed in the Clarivate database from 1975 to 2018. As a result of the search for these documents, 53,740 articles were extracted. Data analysis was performed by using Ravar PreMap, BibExcel, VOSviewer software, and Yewno semantic exploration tools.
The findings show that 741 keywords were identified as the main topics (core) of the articles in bioinformatics, which are used as the most widely used topics in the articles in this field. According to the findings, the topics of "MicroRNA", "Proteomics", "Medical informatics", "Computational Biology", "Microarray", "Gene expression" with the most frequency of application the most important issues identified. As the findings show, a total of 7 clusters were obtained, indicating that bioinformatics articles are thematically 7-axis. The findings show that the concept of "Bioinformatics" is, directly and indirectly, related to the seven main concepts, and this indicates the direct dependence of all the concepts sought in this field. There is a direct and indirect relationship between the concepts of different clusters. According to the findings of the concept "Gene expression" (cluster 1), there is an indirect relationship with the concept of "Metabolomics" (cluster 6) due to the existence of the concept of "Chemical biology" which is not one of the main thematic clusters and is a sub-concept. Also, the concept of "Metabolomics" (cluster 6) is indirectly related to the concept of Bioinformatics through the same concept of "Chemical biology". Also, the indirect connection between the concept "Gene expression" (cluster 1) and "Bioinformatics" concept is established through the two concepts of "RNA-Seq" and "Chemical biology".
The results show that in the formed knowledge map, each concept has a direct relationship with the sub-concepts of its own cluster. There is no direct or indirect relationship between the sub-concepts of each cluster and other sub-concepts of the cluster. Thematic maps and knowledge are drawn in this field can be provided to researchers as a pattern for determining research priorities.