Recent Trends on Multi-omics Studies in Cancer Research: A Bibliometric Study
Bibliographic record
Abstract
The integration of multi-omics approaches has revolutionized cancer research by providing a comprehensive understanding of cancer pathogenesis beyond single-omics methods. By combining diverse omics data types, multi-omics analyses improve precision in identifying intricate disease-related mechanisms. Despite increasing interest, bibliometric analyses on multi-omics research in oncology remain limited. This study addresses this gap by conducting a bibliometric analysis of multi-omics cancer research trends over the past six years (2019 to February 2025), utilizing data from the Web of Science Core Collection (WoSCC) accessed on 28 February 2025, and analysing it with VOSviewer. The analysis of 3386 publications indexed in WoSCC reveals a significant surge in multi-omics research. China leads with 2055 publications, while the University of Toronto in Canada and the Institut National de la Santé et de la Recherche Médicale (Inserm) in France emerge as major contributors, each accounting for more than 50% of their country's total publications in this domain. Dominant keywords such as multi-omics, prognosis, immunotherapy, machine learning and tumor microenvironment highlight current research priorities. This study provides a comprehensive overview of publication trends, offering valuable insights to guide future research in multi-omics cancer studies. By highlighting major contributors and emerging focal points, this study aspires to foster advancements and inspire future exploration in this pivotal domain.
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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: Empirical 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 | Observational | 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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.135 | 0.264 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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.
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".