MDSGene: Closing Data Gaps in Genotype-Phenotype Correlations of Monogenic Parkinson’s Disease
Bibliographic record
Abstract
Given the rapidly increasing number of reported movement disorder genes and clinical-genetic desciptions of mutation carriers, the International Parkinson's Disease and Movement Disorder Society Gene Database (MDSGene) initiative has been launched in 2016 and grown to become a large international project (http://www.mdsgene.org). MDSGene currently contains >1150 variants described in ∼5700 movement disorder patients in almost 1000 publications including monogenic forms of PD clinically resembling idiopathic (PARK-PINK1, PARK-Parkin, PARK-DJ-1, PARK-SNCA, PARK-VPS35, PARK-LRRK2), as well as of atypical PD (PARK-SYNJ1, PARK-DNAJC6, PARK-ATP13A2, PARK-FBXO7). Inclusion of genes is based on standardized published criteria for determining causation. Clinical and genetic information can be filtered according to demographic, clinical or genetic criteria and summary statistics are automatically generated by the MDSGene online tool. Despite MDSGene's novel approach and features, it also faces several challenges: i) The criteria for designating genes as causative will require further refinement, as well as time and support to replace the faulty list of 'PARKs'. ii) MDSGene has uncovered extensive clinical data gaps. iii) The quickly growing body of clinical and genetic data require a large number of experts worldwide posing logistic challenges. iv) MDSGene currently captures published data only, i.e., a small fraction of the available information on monogenic PD available. Thus, an important future aim is to extend MDSGene to unpublished cases in order to provide the broad data base to the PD community that is necessary to comprehensively inform genetic counseling, therapeutic approaches and clinical trials, as well as basic and clinical research studies in monogenic PD.
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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 | 0.037 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".