Bibliographic record
Abstract
Inactivation of p53 is critical for the formation of most tumors. Illumination of the key function(s) of p53 protein in protecting cells from becoming cancerous is therefore a worthy goal. Arguably p53's most important function is to act as a transcription factor that directly regulates perhaps several hundred of the cell's RNA polymerase II (RNAP II)-transcribed genes, and indirectly regulates thousands of others. Indeed p53 is the most well studied mammalian transcription factor. The p53 tetramer binds to its response element where it can recruit diverse transcriptional coregulators such as histone modifying enzymes, chromatin remodeling factors, subunits of the mediator complex, and components of general transcription machinery and preinitiation complex (PIC) to modulate RNAPII activity at target loci (Laptenko and Prives 2006). The p53 transcriptional program is regulated in a stimulus-specific fashion (Murray-Zmijewski et al. 2008; Vousden and Prives 2009), whereby distinct subsets of p53 target genes are induced in response to different p53-activating agents, likely allowing cells to tailor their response to different types of stress. How p53 is able to discriminate between these different loci is the subject of intense research. Here, we describe key aspects of the fundamentals of p53-mediated transcriptional regulation and target gene promoter selectivity.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".