Identifying Differential Methylation in Cancer Epigenetics via a Bayesian Functional Regression Model
Bibliographic record
Abstract
Abstract DNA methylation plays an essential role in regulating gene activity, modulating disease risk, and determining treatment response. Researchers can obtain insight into methylation patterns at a single nucleotide level utilizing next-generation sequencing technologies. However, complex features inherent in the data obtained via these technologies pose challenges beyond the typical big data problems. Identifying differentially methylated cytosines (dmc) or regions is one of such challenges. Current methodologies for identifying dmcs fall short in handling low read-depth data and missing values, capturing functional data patterns, granting multiple covariates (categorical, continuous, or combination), and multiple group comparisons. We have developed an efficient method to identify dmcs based on a Bayesian functional regression approach, termed DMCFB , that tackles these shortcomings. Through simulation studies, we establish that DMCFB outperforms current methods and results in better smoothing, and efficient imputation. We apply the proposed method to analyze a dataset containing patients with acute promyelocytic leukemia and control samples. With DMCFB , we discovered many new dmcs, and more importantly, exhibited enhanced consistency of differential methylation within islands and at their adjacent shores. Furthermore, we detected differential methylation at more of the binding sites of the fused gene involved in this cancer.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".