Neural networks reveal novel gene signatures in Parkinson disease from single-nuclei transcriptomes
Bibliographic record
Abstract
Parkinson disease (PD) is a progressive neurodegenerative disease with an incompletely understood genetic architecture that necessitates novel discovery methods. We introduce an explainable machine learning framework that uses single-cell/nuclei RNA sequencing (sc/snRNAseq) to identify molecular markers of diseased cells and nominate candidate genes for targeted genomic analysis. Application to four snRNAseq datasets characterizing the post-mortem midbrain identified cell type-specific gene sets that consistently distinguished PD from healthy cells across all datasets (mean balanced accuracy = 0.92) and highlighted ten novel candidate genes in PD. Among these, GPC6 was identified as a marker of PD dopaminergic neurons and a member of the heparan sulfate proteoglycan family, implicated in the intracellular accumulation of α-synuclein preformed fibrils-a hallmark of PD. We further validated the enrichment of rare GPC6 variants in PD across three case-control cohorts. This open-source framework is broadly applicable across diseases and promises to accelerate gene discovery in complex diseases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".