Emerging trends and hotspots of tRNA-derived small RNAs in tumours: a bibliometric analysis via VOSviewer and CiteSpace
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: TRNA-derived small RNAs(tsRNAs) play an important role in many biological processes, and their dysregulation is closely related to the progression of cancer, but the research trend and future direction are not clear. This study aims to identify the leading contributors, collaboration networks, and emerging research trends in tsRNAs and their role in oncology, providing a more comprehensive and intuitive reference for researchers in this field. MATERIALS AND METHODS: Related publications related to tsRNA in the field of oncology from 1990 to 2022 were collected from the Science Citation Index Expanded through the Web of Science Core Collection (WOSCC) database on 6 December 2022. RESULTS: There were 2,108 publications related to tsRNAs in oncology. The articles came from 69 countries/regions, 2,218 institutions, 11,340 authors, and 200 journals, and included 9,530 keywords. The annual total number of papers and total global citation score increased steadily every year over the study period. Among the articles related to tsRNAs in oncology, the United States had the highest number of publications with 732 articles, and the United States, China, Japan, Canada, and South Korea had the highest number of collaborations. Seoul National University Sun and the journal Nucleic Acids Research had the most publications at 81 and 63 articles, respectively, and the keyword "tRF" was a hotspot. CONCLUSION: This study provides an in-depth analysis of the research status and development trends of tsRNAs in the field of cancer from a bibliometric perspective. Offering possible guidance for researchers to explore hot topics and frontiers, select suitable journals, and partners in this field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it