Signaling epigenetics: Novel insights on cell signaling and epigenetic regulation
Bibliographic record
Abstract
Cells must be able to respond rapidly and precisely not only to changes in their external environment but also to developmental and differentiation cues to determine when to divide, die, or acquire a particular cell fate. Signal transduction pathways are responsible for the integration and interpretation of most of such signals into specific transcriptional states. Those states are achieved by the modulation of chromatin structure that activates or represses transcription at particular loci. Although a large variety of signal transduction pathways have already been described, much less is known about the crosstalk between signal transduction and its consequent changes in chromatin structure and, therefore, gene expression. Here we present some examples of the relationship between chromatin-associated proteins and important signal transduction pathways during critical processes like development, differentiation, and disease. There is a great diversity of epigenetic mechanisms that have unexpected interactions with signaling pathways to establish transcriptional programs. Moreover, there are also particular cases where signaling pathways directly affect important components of the epigenetic machinery. Based on such examples, we further propose future research directions linking cell signaling and epigenetics. It is foreseeable that analyzing the relationship between cell signaling and epigenetics will be a huge area for future development that will help us understand the complex process by which a cell is able to induce transcriptional changes in response to external and internal signals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".