Exploring The Frontiers Of Epigenetics: Understanding The Role Of Epigenomic Modifications In Gene Expression, Development, And Disease Pathogenesis
Bibliographic record
Abstract
DNA methylation, histone modifications, and non-coding RNA are the three primary epigenetics elements involved in gene expression, development, and diseases. The bibliometric analysis aims to systematically present state-of-the-art research on epigenomic modifications and their roles during development and diseases. The specific objectives are to understand the functions of DNA methylation, histone modifications, and non-coding RNA in gene expression regulation their functions in cellular differentiation and function, and their link with diseases such as cancer, neurological, and metabolic disorders. The study was done with the help of bibliometric data from recent research and methodologies through citation analysis, co-occurrence analysis, and trend analysis using Bibliometrix and VOSviewer tools from PubMed, Web of Science, and Scopus databases. The citation indicators and the research productivity were compared using descriptive analysis with the help of R software and Python. The present research results demonstrated that DNA methylation and histone modifications were changed between healthy individuals and patients with diseases. Cancer patients had the highest mean DNA methylation levels of 52. 1% while the healthy controls were at 35%. 2% with neurological and metabolic disorder patients also having slightly higher methylation levels. Further alterations in the histone modifications and gene expression also pointed towards the need for epigenomic modifications in disease. In this paper, it is concluded that these epigenetic modifications play a role in disease mechanisms and the identification of treatment approaches. Subsequent research should involve epigenomic, transcriptomic, and proteomic analysis to enhance the understanding and application of these processes in clinical practice.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".