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Updated: Jan 17, 2026
07:50
Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
Published on: April 18, 2025
879
Exploring biological research hotspots through a novel bibliometric approach
Shan Chen1, Junsha Wang2, Xinyu Huang3
1Science of Learning in Education Centre, National Institute of Education, Nanyang Technological University, Singapore City 637616, Singapore.
Computational Biology and Chemistry
|September 13, 2025
Summary
This study introduces a novel bibliometric analysis using R to map biological research trends. It suggests integrating big data and machine learning for deeper insights and future predictions in biology.
Area of Science:
- Biological Sciences
- Life Sciences Research
Background:
- Biological research is fundamental, influencing all aspects of human life.
- Understanding research trends is vital for scientific advancement.
Purpose of the Study:
- To identify current research hotspots and future trends in biology.
- To introduce an innovative bibliometric analysis method using the R programming language.
- To propose future research directions integrating big data and machine learning.
Main Methods:
- Bibliometric analysis using the R programming language.
- Comparison with traditional methods like VOSviewer.
- Data integration and machine learning model training.
Main Results:
- Identification of key research areas and emerging trends in biological studies.
- Demonstration of the R-based bibliometric method's effectiveness for in-depth analysis.
- Validation of the proposed integration of big data and machine learning.
Conclusions:
- The R-based bibliometric approach offers a novel perspective on biological research trends.
- Integrating big data and machine learning holds significant potential for future biological insights and predictions.
- This study provides a roadmap for advanced bibliometric analysis in life sciences.
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