Additional file 2 of A novel approach to risk exposure and epigenetics—the use of multidimensional context to gain insights into the early origins of cardiometabolic and neurocognitive health
Bibliographic record
Abstract
Additional file 2. Supplementary results [143–149]. Figure S1. Scree plot of factor analysis of maternal smoking profiles data. Figure S2. Heat map of confounder variables represented by top 20 singular value decomposition (SVD) components. Figure S3. Correlation matrix of DNAm components and sex-related singular value decomposition principal components. Figure S4. Correlation matrix of DNAm components and singular value decomposition principal components related to social confounders, V13 and V19. Figure S5. Correlation matrix of gwDNAm patterns and estimated cell count proportions. Figure S6. Blood pressure (z-score by sex), systolic and diastolic. Figure S7. Fat mass (z-score by sex) obtained through dual-energy x-ray absorptiometry (DEXA) scanning. Figure S8. Lean mass (z-score by sex) obtained through DEXA scanning. Figure S9. Weight (z-score by sex). Figure S10. Denver Developmental Screening Test – II performance (parental report). Figure S11. School performance as assessed on the UK Department of Education scores from standard assessment tests linked to ALSPAC subjects for ages 5-7, 8-11, and 12-14 years. Figure S12. Weschler Intelligence Scale for Children-III (WISC) performance at age 8 years. Table S1. Random forest metrics – a comparison of three models using cord blood DNAm components and waist circumference as the outcome. Figure S13. Replication of gwDNAm patterns at birth– Model testing in peripheral blood at age 7 and age 17. Table S2. Performance metrics comparing models in DNAm data (Model 3) at birth and mid- and late childhood in ARIES. Table S3. Performance metrics of Model 1 (risk-related variables) in ARIES. Figure S14. Component 9 overlaps with DNase I hypersensitivity sites (DHSs) sites more than randomly expected in the genome. Figure S15. Component 7 versus control group: meta-EWAS (44). Figure S16. Component 19 versus control group: meta-EWAS (44). Figure S17. Component 18 versus control group: meta-EWAS (44). Figure S18. Locus overlap enrichment analysis (LOEA) using tissue-clustered DHSs (47) (available from LOLA core database) for meta-EWAS and Components 7, 9 and 18. Figure S19. Locus overlap enrichment analysis (LOLA) using chromatin marks (available from LOLA Roadmap database) for Component 4. Figure S20. All CpG sites overlapping between EWAS candidates from Richmond (48) and each DNAm component.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.847 | 0.139 |
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".