The Parkinson’s Disease DNA Variant Browser
Bibliographic record
Abstract
Abstract Parkinson’s disease (PD) is a genetically complex neurodegenerative disease with ~20 genes known to contain mutations that cause PD or atypical parkinsonism and 90 common genetic risk factors. Large-scale next-generation sequencing projects have revolutionized genomics research. Applying these data to PD, many genes have been reported to contain putative disease-causing mutations. In most instances, however, the results remain quite limited and rather preliminary, in large part because of an inability of any single group to validate findings in a large independent series of sequenced patients. We present here the Parkinson’s Disease Sequencing Browser: a Shiny-based web application that presents comprehensive summary-level frequency data from multiple large-scale genotyping and sequencing projects. The data is aggregated and involves a total of 102,127 participants, including 30,103 PD cases (including 1,650 proxy cases) and 72,024 controls. Our aim is to assist researchers on their search for PD-risk genes and variant candidates with an easily accessible and open summary-level genomic data browser for the PD research community, https://pdgenetics.shinyapps.io/VariantBrowser/ .
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.019 |
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 source (direct Gemma or distilled Codex), 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".