Analysis of Persian Bioinformatics Research with Topic Modeling.
Biomed Res Int
; 2023: 3728131, 2023.
Artículo
en Inglés
| MEDLINE | ID: covidwho-2294565
ABSTRACT
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.
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Bibliometría
/
Biología Computacional
Tipo de estudio:
Estudio pronóstico
País/Región como asunto:
Asia
Idioma:
Inglés
Revista:
Biomed Res Int
Año:
2023
Tipo del documento:
Artículo
País de afiliación:
2023
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