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Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
Published on: April 18, 2025
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A Bibliometric Analysis on Cancer Population Science with Topic Modeling
Ding-Cheng Li1, Majid Rastegar-Mojarad1, Janet Okamoto2
1Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN.
Summary
Bibliometric analysis, using author topic modeling and dynamic topic modeling, identified research trends in cancer population science. This quantitative method helps guide strategic planning by revealing topic distributions and evolving research interests.
Area of Science:
- Library and Information Science
- Cancer Population Science
Background:
- Bibliometric analysis is a quantitative research method used to evaluate research performance and identify trends.
- Understanding research landscapes is crucial for strategic planning in scientific fields.
Purpose of the Study:
- To conduct a bibliometric analysis of publications from investigators funded by the National Cancer Institute's Division of Cancer Prevention (DCP) and Division of Cancer Control and Population Science (DCCPS).
- To inform the institutional strategic plan for cancer population science by identifying current and emerging research trends.
Main Methods:
- Applied two topic modeling techniques: Author Topic Modeling (AT) and Dynamic Topic Modeling (DTM).
- Analyzed publications from investigators funded by DCP and DCCPS.
Main Results:
- Author Topic Modeling (AT) effectively identified investigators' research interests, topic distributions, and popularity.
- Dynamic Topic Modeling (DTM) revealed evolving research trends by tracking keyword proportion changes, aligning with Medical Subject Headings (MeSH) updates.
Conclusions:
- Bibliometric analysis using AT and DTM provides valuable insights into research landscapes.
- These methods can effectively guide strategic planning in cancer population science by highlighting established and emerging research areas.
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