Additional file 1 of Identifying novel regulatory effects for clinically relevant genes through the study of the Greek population
Bibliographic record
Abstract
Additional file 1: Figure S1. GM individuals map close to Italian and Spanish populations. Figure S2. eQTL mapping results in GM. Figure S3. Comparison of direction of effect for shared SNP-genes in both GM and GTEx-am, per tissue. Figure S4. Example of a Genotype×Sex regulatory interaction in GM S. Figure S5. Enrichment of GTEx-am eQTLs in adipose tissue functional annotations. Figure S6. eQTL plots for protein coding eGenes detected in GM only, displaying distinct expression patterns across populations. Figure S7. eQTL plots for non protein-coding eGenes detected in GM only, displaying distinct expression patterns across populations. Figure S8. Differences in expression patterns of eGenes detected in GM, but not in GTEx-am. Figure S9. Overlap of colocalizing GWAS SNPs between GM and GTEx-am. Figure S10. Secondary eQTL for THNSL2 in GM S colocalizes with a GWAS signal associated with CRP levels.
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 | 0.002 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.852 | 0.117 |
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".