Subnuclear compartmentalization of sequence-specific transcription factors and regulation of eukaryotic gene expression
Bibliographic record
Abstract
To date, the majority of the research regarding eukaryotic transcription factors has focused on characterizing their function primarily through in vitro methods. These studies have revealed that transcription factors are essentially modular structures, containing separate regions that participate in such activities as DNA binding, protein-protein interaction, and transcriptional activation or repression. To fully comprehend the behavior of a given transcription factor, however, these domains must be analyzed in the context of the entire protein, and in certain cases the context of a multiprotein complex. Furthermore, it must be appreciated that transcription factors function in the nucleus, where they must contend with a variety of factors, including the nuclear architecture, chromatin domains, chromosome territories, and cell-cycle-associated processes. Recent examinations of transcription factors in the nucleus have clarified the behavior of these proteins in vivo and have increased our understanding of how gene expression is regulated in eukaryotes. Here, we review the current knowledge regarding sequence-specific transcription factor compartmentalization within the nucleus and discuss its impact on the regulation of such processes as activation or repression of gene expression and interaction with coregulatory factors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".