Bibliographic record
Abstract
Neurodevelopmental disorders (NDDs) constitute a broad spectrum of rare conditions arising from various genetic and environmental factors. A subset of NDDs are caused by pathogenic variants in a single gene or locus and are termed Mendelian NDDs. These disorders present with diverse symptoms and exhibit significant clinical heterogeneity. In recent years, advancements in genetic technologies and transitioning from phenotype-first to genotype-first approaches have driven a surge in novel gene and Mendelian NDD discoveries, identifying the epigenetic machinery encoding genes as one of the major gene groups associated with Mendelian NDDs. This highlighted that the epigenetic gene regulation is essential for the normal neurodevelopment and can result in Mendelian NDD, when disrupted by a pathogenic genetic variant. Despite technological advancements, interpreting genetic data remains challenging, so a large proportion of the variants are being classified as variants of uncertain significance (VUS). Because of the rarity of these conditions and diagnostic challenges, the true clinical and molecular spectrum of the most Mendelian NDDs largely remain unknown, despite increasing application of the next-generation sequencing. As result, this complicates 1) genetic variant interpretation, as well as 2) diagnosed patient care, requiring further research to characterize these conditions. Therefore, this thesis is aimed to comprehensively characterize the clinical, molecular, and DNA methylation spectrum and features of several Mendelian NDDs, focusing on the disorders of the epigenetic machinery.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".