The Role of Epigenetics in Cancer: From Molecular Function to High-Throughput Assays
Bibliographic record
Abstract
The notion of epigenetics encompasses various modifications of chromatin, including DNA methylation and post-translational modifications of histone proteins that can be stably transmitted through mitosis. Epigenetics plays a fundamental role in normal cell physiology as it is molecularly involved in virtually all chromatin-associated processes, including gene expression, DNA replication and repair. Alterations in the global profile of epigenetic modifications are commonly observed in cancer and are believed to be associated with the establishment and clonal maintenance of an aberrant gene expression pattern. Recent technological advances have enabled to assess the epigenetic signature of a given cell type in a genome-wide manner. These comparative epigenome studies have significantly increased our understanding of the oncogenic process. In addition, they constitute promising tools for improved classification and diagnosis of cancer patients, ultimately leading to the design of personalised therapies. In this chapter, we focus on the role of epigenetics in normal and pathological cell development. We outline recent large-scale assays of epigenetic profiling in normal and cancer tissue samples as well as pertinent new discoveries linking epigenetics and cancer. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".