Transcription and the Control of Gene Expression
Bibliographic record
Abstract
Transcription, the initial step of gene expression is a tightly regulated process. In addition to variability in the core promoter region of the RNA polymerase II transcribed genes, which can stabilize or destabilize the basal machinery and influence transcription rates, promoters contain enhancer regions which can be far upstream from the gene transcribed. These enhancers and their DNA binding factors are highly variable and can lead to the recruitment of unique co-activator complexes that can influence the initiation and progression of the polymerase through the nucleosomal structure of chromatin. In essence then, every promoter becomes a unique microenvironment, the sum of several enhancer elements, core promoter elements and chromatin structure. The gene’s transcription rate is then dependent on the efficiency of these interactions – an average of the effects of each enhancer and co-activator leading to a fine tuning of transcriptional responses according to cellular needs. It is therefore not surprising that transcription is the major checkpoint for gene expression in the cell. The next step for the new mRNA molecule is post-transcriptional modification, which augments the stability of the messenger mRNA export to the cytoplasm (in eukaryotes) and translation into proteins.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".