Analysis of Persian Bioinformatics Research with Topic Modeling.
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 attenti...
| Publicado en: | BioMed Research International pp. 1 - 9 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
4/17/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=163167816&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163167816 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/17/2023 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 163167816 163167816 163167816 10.1155/2023/3728131 163167816 ppf: 1 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Analysis of Persian Bioinformatics Research with Topic Modeling. aug: au: Ebrahimi, Fezzeh Dehghani, Mohammad Makkizadeh, Fatemah affil: Department of Scientometrics, Faculty of Social Sciences, Yazd University, Yazd, Iran sug: subj: Research, Medical Bioinformatics Iran Models, Theoretical Information Technology Mathematics Biological Science Disciplines Natural Language Processing Citation Analysis Descriptive Research Exploratory Research Algorithms Iran Bibliometrics Language Processing Gene Expression Models, Molecular Biological Markers ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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