OUP accepted manuscript
Bibliographic record
Abstract
Sex is a modulator of health that has been historically overlooked in biomedical research. Recognizing this knowledge gap, funding agencies now mandate the inclusion of sex as a biological variable with the goal of stimulating efforts to illuminate the molecular underpinnings of sex biases in health and disease. DNA methylation (DNAm) is a strong molecular candidate for mediating such sex biases; however, a robust and well characterized annotation of sex differences in DNAm is yet to emerge. Beginning with a large (n = 3795) dataset of DNAm profiles from normative adult whole blood samples, we identified, validated and characterized autosomal sex-associated co-methylated genomic regions (sCMRs). Strikingly, sCMRs showed consistent sex differences in DNAm over the life course and a subset were also consistent across cell, tissue and cancer types. sCMRs included sites with known sex differences in DNAm and links to health conditions with sex biased effects. The robustness of sCMRs enabled the generation of an autosomal DNAm-based predictor of sex with 96% accuracy. Testing this tool on blood DNAm profiles from individuals with sex chromosome aneuploidies (Klinefelter [47,XXY], Turner [45,X] and 47,XXX syndrome) revealed an intimate relationship between sex chromosomes and sex-biased autosomal DNAm.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.832 | 0.777 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".