Exploring the landscape of Parkinson’s disease transcriptomics: a quantitative review of research progress and future directions
Bibliographic record
Abstract
Introduction: This study leverages bibliometric analysis to uncover the research landscape and spotlight emerging trends in the field of Parkinson's disease (PD) transcriptomics. Methods: The relevant literature on Parkinson's disease and transcriptomics was retrieved from the Web of Science Core Collection database. Bibliometric analysis was conducted using VOSviewer, CiteSpace, and the Bibliometrix R package. Results: A total of 208 research articles were retrieved from January 2011 to March 2025. The number of publications has shown a steady increase, particularly from 2020 to 2024, with an average annual publication rate of 29 articles during this period. The United States and China were the leading countries in terms of publication counts, while the University of Luxembourg and McGill University were the top contributing institutions. The most impactful journals included "Nature Communications" and "NPJ Parkinson's Disease." The co-occurrence analysis of keywords revealed that "Parkinson's disease," "transcriptomics," "neurodegeneration," and "biomarkers" are current research hotspots. Citation burst analysis identified key references related to genetics, transcriptomics, and data analysis tools that have significantly influenced the field. Conclusion: This study offers the first comprehensive bibliometric analysis of Parkinson's disease (PD) transcriptomics research from 2011 to 2025. We reveal a significant surge in research activity, particularly since 2020, driven by advancements in single-cell and spatial transcriptomics. The United States and China lead in publication output, with key contributions from the University of Luxembourg and McGill University. Research hotspots include neuroinflammation, biomarker discovery, and machine learning applications, indicating a shift toward translational research. However, challenges such as data heterogeneity and high biomarker validation failure rates persist. Future research should focus on standardizing methodologies and enhancing clinical relevance. Strategic directions include multi-omics integration, global collaboration, and linking transcriptomic signatures to clinical outcomes, aiming to improve early diagnosis and personalized therapies for PD.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".