Epigenotoxicity: Decoding the epigenetic imprints of genotoxic agents and their implications for regulatory genetic toxicology
Bibliographic record
Abstract
Regulatory genetic toxicology focuses on DNA damage and subsequent gene mutations. However, genotoxic agents can also affect epigenetic marks, and incorporation of epigenetic data into the regulatory framework may thus enhance the accuracy of risk assessment. Additionally, epigenetic alterations may identify non-genotoxic carcinogens that are not captured with the current battery of tests. Epigenetic alterations could also explain long-term consequences and potential transgenerational effects in the absence of DNA mutations. Therefore, at the 2022 International Workshops on Genotoxicity Testing (IWGT) in Ottawa (Ontario, Canada), an expert workgroup explored whether including epigenetic endpoints would improve regulatory genetic toxicology. Here we summarize the presentations and the discussions on technical advancements in assessing epigenetics, how the assessment of epigenetics can enhance strategies for detecting genotoxic and non-genotoxic carcinogens and the correlation between epigenetic alterations with other relevant apical endpoints.
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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 | 0.000 | 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) | 0.000 | 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".