Cell-Specific DNA Methylation Signatures in Asthma
Bibliographic record
Abstract
Asthma is a complex trait, often associated with atopy. The genetic contribution has been evidenced by familial occurrence. Genome-wide association studies allowed for associating numerous genes with asthma, as well as identifying new loci that have a minor contribution to its phenotype. Considering the role of environmental exposure on asthma development, an increasing amount of literature has been published on epigenetic modifications associated with this pathology and especially on DNA methylation, in an attempt to better understand its missing heritability. These studies have been conducted in different tissues, but mainly in blood or its peripheral mononuclear cells. However, there is growing evidence that epigenetic changes that occur in one cell type cannot be directly translated into another one. In this review, we compare alterations in DNA methylation from different cells of the immune system and of the respiratory tract. The cell types in which data are obtained influences the global status of alteration of DNA methylation in asthmatic individuals compared to control (an increased or a decreased DNA methylation). Given that several genes were cell-type-specific, there is a great need for comparative studies on DNA methylation from different cells, but from the same individuals in order to better understand the role of epigenetics in asthma pathophysiology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".