GAGA protein: a multi-faceted transcription factorThis paper is one of a selection of papers published in this Special Issue, entitled 27th International West Coast Chromatin and Chromosome Conference, and has undergone the Journal's usual peer review process.
Bibliographic record
Abstract
The transition from transcription activation to repression is regulated at multiple levels by the DNA sequence and DNA modification to its compaction through chromatin packaging. The GAGA factor (GAF) is one of a few transcription factors that can regulate gene expression at multiple levels. It displays both activator/antirepressor and repressor activity, depending on its target genomic location. The GAF-mediated modulation of expression appears to be intimately linked with modifications of the chromatin structure. The GAF can associate with highly compacted heterochromatin, contributing to gene repression, or participate in nucleosome remodeling to activate specific genes. In this review, we are attempting to elucidate the contribution(s) of the various domains of the GAF to the recruitment of its functional partners, leading to seemingly opposite functions. We surveyed the current scientific literature for evidence of GAF involvement in regulatory events associated with changes of chromatin composition or conformation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".