Application of Microarrays for DNA Methylation Profiling
Bibliographic record
Abstract
Comprehensive analyses of the human epigenome may be of critical importance in understanding the molecular mechanisms of complex diseases, development, aging, tissue specificity, parental origin effects, and sex differences, among other systemic aspects of human biology. However, traditional DNA methylation methods allowed for screening of only relatively short DNA fragments. The advent of microarrays has provided new possibilities in DNA methylation analysis, because this technology is able to interrogate a very large number of loci in a highly parallel fashion. There are several permutations of the microarray application in DNA methylation profiling, and such include microarray analysis of bisulfite modified DNA and also the enriched unmethylated or hypermethylated DNA fractions using methylation-sensitive restriction enzymes or antibodies against methylated cytosines. The method described in detail here is based on the analysis of the enriched unmethylated DNA fraction, using a series of treatments with methylation-sensitive restriction enzymes, adaptor ligation, PCR amplification, and quantitative mapping of unmethylated DNA sequences using microarrays. The key advantages of this approach are the ability to investigate DNA methylation patterns using very small DNA amounts and relatively high informativeness in comparison to the other restriction-enzyme- based strategies for DNA methylation profiling [1].
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".