A COMPARISON OF TWO SETS OF MICROARRAY EXPERIMENTS TO DEFINE ALLERGIC ASTHMA EXPRESSION PATTERN
Bibliographic record
Abstract
Allergic asthma is a complex trait. Several approaches have been used to identify biomarkers involved in this disease. This study aimed at demonstrating the relevance and validity of microarrays in the definition of allergic asthma expression pattern. The authors compared the transcript expressions of bronchial biopsy of 2 different microarray experiments done 2 years apart, both including nonallergic healthy and allergic asthmatic subjects (n = 4 in each experiment). U95Av2 and U133A GeneChips detected respectively 89 and 40 differentially expressed genes. Fifty-five percent of the U133A genes were previously identified with the U95Av2 arrays. The immune signaling molecules and the proteolytic enzymes were the most preserved categories between the 2 experiments, because 3/4 of the genes identified by the U133A were also significant in the U95Av2 study for both categories. These results demonstrate the relevance of microarray experiments using bronchial tissues in allergic asthma. The comparison of these GeneChip studies suggests that earlier microarray results are as relevant as actual ones to target new genes of interest, particularly in function categories linked to the studied disease. Moreover, it demonstrates that microarrays are a valuable technology to target novel allergic asthma pathways as well as biomarkers.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".