Analysis of allelic differential expression in the human genome using allele-specific serial analysis of gene expression tags
Bibliographic record
Abstract
Recent reports have demonstrated that a significant proportion of human genes display allelic differential expression (ADE). ADE is associated with phenotypic variability and may contribute to complex genetic diseases. Here, we present a computational analysis of ADE using allele-specific serial analysis of gene expression (SAGE) tags representing 1295 human genes. We identified 472 genes for which unequal representation (>3-fold) of allele-specific SAGE tags was observed in at least one SAGE library, suggesting the occurrence of ADE. For 235 out of these 472 genes, the difference in the expression level between both allele-specific SAGE tags was statistically significant (p < 0.05). Eleven candidate genes were then subjected to experimental validation and ADE was confirmed for 8 out of these 11 genes. Our results suggest that at least 25% of the human genes display ADE and that allele-specific SAGE tags can be efficiently used for the identification of such genes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".