Epigenetic signatures in overgrowth syndromes: Translational opportunities
Bibliographic record
Abstract
In recent years, numerous overgrowth syndromes have been found to be caused by pathogenic DNA sequence variants in "epigenes," genes that encode proteins that function in epigenetic regulation. Epigenetic marks, including DNA methylation (DNAm), histone modifications and chromatin conformation, have emerged as a vital genome-wide regulatory mechanism that modulate the transcriptome temporally and spatially to drive normal developmental and cellular processes. Evidence suggests that epigenetic marks are layered and engage in crosstalk, in that disruptions of any one component of the epigenetic machinery impact the others. This interdependence of epigenetic marks underpins the recent identification of gene-specific DNAm signatures for a variety of disorders caused by pathogenic variants in epigenes. Here, we discuss the power of DNAm signatures with respect to furthering our understanding of disease pathophysiology, enhancing the efficacy of molecular diagnostics and identifying new targets for therapeutics of overgrowth syndromes. These findings highlight the promise of the field of epigenomics to provide unprecedented insights into disease mechanisms generating a host of opportunities to advance precision medicine.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".