Integrating post‐transcriptional regulation into the embryonic stem cell gene regulatory network
Bibliographic record
Abstract
Stem cell behavior is orchestrated as a multilayered, concert of gene regulatory mechanisms collectively referred to as the gene regulatory network (GRN). Via cooperative mechanisms, transcriptional, epigenetic, and post-transcriptional regulators activate and repress gene expression to finely regulate stem cell self-renewal and commitment. Due to their tractability, embryonic stem cells (ESCs) serve as the model stem cell to dissect the complexities of the GRN, and discern its relation to stem cell fate. By way of high-throughput genomic analysis, targets of individual gene regulators have been established in ESCs. The compilation of these discrete networks has revealed convergent, multi-dimensional gene regulatory mechanisms involving transcription factors, epigenetic modifiers, non-coding RNA (ncRNA), and RNA-binding proteins. Here we highlight the seminal genomic studies that have shaped our understanding of the ESC GRN and describe alternate post-transcriptional gene regulatory mechanisms that require in depth analyses to draft networks that fully model ESC behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".