The danger of epigenetics misconceptions (epigenetics and stuff…)
Bibliographic record
Abstract
Within the past two decades, the fields of chromatin structure and function and transcription regulation research started to fuse and overlap, as evidence mounted to support a very strong regulatory role in gene expression that was associated with histone post-translational modifications, DNA methylation, as well as various chromatin-associated proteins (the pillars of the "Epigenetics" building). The fusion and convergence of these complementary fields is now often simply referred to as "Epigenetics". During these same 20 years, numerous new research groups have started to recognize the importance of chromatin composition, conformation, and its plasticity. However, as the field started to grow exponentially, its growth came with the spreading of several important misconceptions, which have unfortunately led to improper or hasty conclusions. The goal of this short "opinion" piece is to attempt to minimize future misinterpretations of experimental results and ensure that the right sets of experiment are used to reach the proper conclusion, at least as far as epigenetic mechanisms are concerned.
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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.028 | 0.055 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".