Additional file 1 of Ancestry and frequency of genetic variants in the general population are confounders in the characterization of germline variants linked to cancer
Bibliographic record
Abstract
Additional file 1 : Supplemental Figure S1. UCSC Genome Browser view of the NEGR1 locus. Layered ChIP-seq tracks for the active enhancer histone mark H3K27Ac, DNase clusters (corresponding to accessible chromatin) and transcription (Txn) factor binding data from the ENCODE project are shown. The data indicate that the deleted region upstream of NEGR1 in pHGG patients may harbor regulatory regions. Supplemental Figure S2. The deletion upstream of NEGR1 appears to have specific effects on the transcription of this gene. (A-B) Transcription of ZRANB2, the gene immediately downstream of NEGR1, is detected in our scRNA-seq datasets. (C) Single-cell transcriptomics data for 14,963 cells isolated from human hippocampus and cortex. This tSNE plot describes the clustering of different cell populations present in these brain regions. (D) Transcription of NEGR1 is detected in single-cells isolated from human hippocampus and cortex. Supplemental Table S1. Patient and sample information.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.841 | 0.126 |
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".