Involvement of gene–diet/drug interaction in DNA methylation and its contribution to complex diseases: from cancer to schizophrenia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most biological processes, including diseases, involve genetic and non-genetic factors. Also, the realization of a genetic potential may depend on environmental factors by directly affecting the expression of gene(s). Exactly how different environmental factors affect gene expression is not well understood. One of the mechanisms may involve DNA methylation and thereby gene expression. Diet, chemicals, and metals are known to affect DNA methylation and other epigenetic processes but are just beginning to be elucidated. For example, methylation of cytosine(s) in the promoter region could prevent the binding of transcription factors or create binding sites for complexes that deacetylate neighboring histones that in turn compact the chromatin, encouraging a gene to become silent. This article will discuss DNA methylation as an epigenetic mechanism of gene regulation and examine how factors like diet, chemicals, and metals may affect DNA methylation. The effect of alterations in DNA methylation may include aberrant expression of genes or genomes and chromosomal instability, which in turn may contribute to the etiology of complex multifactorial diseases. A similar mechanism is now recognized in a number of cancers. There is also indirect evidence to suggest that methylation could apply to a number of complex diseases, including schizophrenia.
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.
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 | 0.000 |
| 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 it