Chromatin state distribution of residue-specific histone acetylation in early myoblast differentiation
Bibliographic record
Abstract
Abstract Dynamic changes in epigenetic landscape reflect a critical command of lineage-specific gene expression. In an effort to discern the epigenetic regulatory networks of myogenic differentiation, we have used systematic and integrated approaches to explore multi-omics datasets on global myogenic gene expression, histone acetylation and acetyltransferase occupancy in view of distinct chromatin states. In this brief report, we present a comprehensive assessment regarding the experimental design, quality control, filtering steps and data processing. We also define a gene-level overlap between RNA-seq and ChIP-seq datasets through integrated analyses to offer strategies for future use of the data. Furthermore, our analyses provide a blueprint of chromatin states associated with residue-specific histone acetylation, histone acetyltransferase p300-concomitant epigenetic landscape in committed skeletal myoblasts and differential histone acetylation signatures in early myoblast differentiation. These datasets can be further utilized to delineate the function of muscle-specific regulatory elements governed by other muscle myogenic regulators or signaling molecules.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".