Epigenetic Pathways in Breast Cancer: Diagnostic Markers, Transgenerational Impacts, and Emerging Therapies
Bibliographic record
Abstract
Breast cancer affects the lives of millions of women each year, and there is still no cure for this disease.There are known links between breast cancer and epigenetics, which is the mechanism of altering gene expression and affecting biological activities without changing the gene sequence.Given this, researchers are investigating breast cancer development, diagnosis, and treatment.This paper will investigate how transgenerational epigenetic changes affect the growth of breast cancer, diagnosis, and what are therapeutic approaches to mitigate these effects.Understanding the epigenetic links to breast cancer is key to improving diagnosis and treatment.Through a thorough examination of the existing literature, this paper analyzes the transgenerational epigenetic development of breast cancer, the diagnosis of epigenetic markers of breast cancer, and therapeutic approaches for treating epigenetic markers of breast cancer.Several therapeutic approaches are developed targeting epigenetic markers, including the development of epidrugs, which inhibit key parts of the epigenetic pathways leading to breast cancer; these are comprehensively studied in this paper.Overall, understanding and targeting epigenetic mechanisms underlying breast cancer is essential for advancing diagnosis, developing effective treatments, and potentially reducing the transgenerational impact of the disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".