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Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

The co-words analysis, as a scientometric method, helps us to discover the conceptual relationships between scientific texts and application of such relationships as a means to demonstrate important links that may be difficult to discover in other ways. This article is intended to use an analytical approach and identify research areas and to reveal the intellectual structure of knowledge in laser researches in biomedicine literature during 2009-2018. In this descriptive-analytical study with a scientometric approach, the data about laser were retrieved from PubMed in MEDINE format, including 7,682 articles with 18,227 keywords. By using co-word analysis, clustering methods, and strategic diagram; the data were analyzed with the help of SPSS 20, UcInet, and VOSviewer software. This study found that a steady growth could be seen on the laser research and 19.47% of publications are found in 5 journals. The most frequent keywords in the field of laser have been "lasers, solid-state", "laser therapy", "lasers, excimer", etc. The results of cluster analysis shaped the concepts of laser in 7 clusters. The cluster "laser, excimer" was well developed and it plays an effective role. In contrast, "Photo chemotherapy", "skin diseases with Emphasis on Lasers, gas", "Laser Effects", "Laser dentistry "and "Corneal Surgery, Laser" were relatively immature, which necessitates potential research space for future study. This research used co-word networks that indicate the important links between keywords of research areas. Disclosing the thorough context of issues which were encountered at the time of investigating, these networks will be used in locating knowledge gaps. Majority of research approaches include treatments or therapeutic aspects of laser in ophthalmology. The lack of some topics such as cardiology, oncology, optogenetics etc. shows that they have not been considered. Moreover, nevertheless of the practice site, application, or system in use, there is the goal of making and keeping a lasersafe environment for the staff, the user, and patient. These subjects have not been sufficiently considered. [ABSTRACT FROM AUTHOR]