Additional file 2 of 3D chromatin-based variant-to-gene maps across 57 human cell types reveal the cellular and genetic architecture of autoimmune disease susceptibility
Bibliographic record
Abstract
Additional file 2. Supplementary Figures S1-S19.Figure S1. Full S-LDSC parameters across diverse cell types’ cREs annotation. Figure S2. Bar plots show number of significant heritability enrichment.Figure S3. Bar plots show number of significant conditional effect sizes. Figure S4. Intersections of V2G genes. Figure S5. Comparative predictive power of orthogonal V2G approaches. Figure S6. Cytokine/receptor gene enrichment across trait and cell type. Figure S7. Salmonella infection gene enrichment across trait and cell type. Figure S8. Sharing of V2G genes in enteroids across UC, CRO, and IBD. Figure S9. Gene ontology enrichment of cell type-specific V2G genes across cell type. Figure S10. Shared eGenes across different eQTL datasets with V2G. Figure S11. Shared eGenes across different eQTL datasets per locus. Figure S12. Proportion of eGenes identified by eQTL. Figure S13. Disrupted transcription factor binding motifs. Figure S14. Dot-plot shows effect sizes of SLE and RA variants on FDFT1 expression. Figure S15. Expression of BLK. Figure S16. Effect of lapaquistat on T cell activation. Figure S17. Gating strategies for Immune cells. Figure S18. Phenotypic characterization of the sorted Immune cells. Figure S19. Functional characterization of the expanded T-helper subsets.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.822 | 0.145 |
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".