Involvement of gene–diet/drug interaction in DNA methylation and its contribution to complex diseases: from cancer to schizophrenia
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.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".