Hotspots and development frontiers of circRNA based on bibliometric analysis
Bibliographic record
Abstract
Background and purpose: Circular RNAs (circRNAs) are a big group of members of the noncoding RNA family following long non-coding RNA and microRNA. They play a regulatory role in many biological processes. Analyzing their current research status and future development trends is conducive to a more comprehensive understanding of circRNAs and contributes to the dedication to the biological field. Methods: The literature on circRNA from 2000 to 2021 in the Web of Science Core Collection of the Web of Science database with "circular RNA" as the subject was searched. R Studio's Bibliometrix package and biblioshiny software were used for publication trend analysis, citation analysis, keyword analysis, author analysis, research institution analysis, source analysis, country analysis, and collaboration analysis for all documents and highly cited documents. Results: From 2000 to 2021, 3,186 circRNA-related articles were published worldwide, of which 193 were highly cited. The number of published articles had shown an explosive increase after 2013. These articles were mainly from Chinese research institutions and authors, but the country with the highest average number of citations per year in highly cited documents was Germany. Scientific research institutions came from countries represented by Germany, USA, China, Australia and Canada all had different degrees of cooperation. The theme and key points of the research had evolved over time from expression to the role and mechanism of circRNA in diseases, especially in cancer. CDR1as, circFOXO3, circHIPK3, circITCH, circMTO1, circSMARCA5 and circZNF609 are circRNAs that are mainly studied currently, their studies mainly involve cell biology, biological functions and cancer. The future research direction and trend would still be the application of circRNA in diseases. Conclusion: The basic situation and development trend of circRNA related research we described provide a direction for future research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.135 | 0.132 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".