Chromatin remodeling: The interface between extrinsic cues and the genetic code?
Bibliographic record
Abstract
The successful completion of the human genome project ushered a new era of hope and skepticism. However, the promise of finding the fundamental basis of human traits and diseases appears less than fulfilled. The original premise was that the DNA sequence of every gene would allow precise characterization of critical differences responsible for altered cellular functions. The characterization of intragenic mutations in cancers paved the way for early screening and the design of targeted therapies. However, it has also become evident that unmasking genetic codes alone cannot explain the diversity of disease phenotypes within a population. Further, classic genetics has not been able to explain the differences that have been observed among identical twins or even cloned animals. This new reality has re-ignited interest in the field of epigenetics. While traditionally defined as heritable changes that can alter gene expression without affecting the corresponding DNA sequence, this definition has come into question. The extent to which epigenetic change can also be acquired in response to chemical stimuli represents an exciting dimension in the "nature vs nurture" debate. In this review I will describe a series of studies in my laboratory that illustrate the significance of epigenetics and its potential clinical implications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".