Toward a Comprehensive Set of Asthma Susceptibility Genes
Bibliographic record
Abstract
Epidemiological and twin studies have demonstrated that asthma is under genetic and environmental influences. Numerous candidate gene association studies as well as genome-wide linkage scans have followed, aiming to elucidate the genetic architecture underlying this complex disease. Several promising asthma susceptibility genes were identified, and a comprehensive catalogue of these genes seems a realistic goal within 5 to 10 years. However, a key challenge is to understand the combination of genes and environmental factors that gives rise to the disease in a specific individual. Currently, most of the reports of asthma susceptibility genes are either preliminary or controversial, with little knowledge about the genetic mechanisms leading to abnormal function of the gene that promotes the development of asthma. Replications of published associations are relatively few. Many factors, including the inherent complexity of asthma as well as methodological issues, can explain these inconsistencies. Promising genetic tools are emerging with the completion of the International HapMap Project that will increase the scope of gene-discovery investigations. It is hoped that these tools, combined with validation studies in additional populations, will enable the creation of a comprehensive catalogue of susceptibility genes for asthma. Notwithstanding the difficulties in making sense of the vast amount of new genetic data, we already see the emergence of new biological pathways of atopy, airway remodeling, and asthma that may lead to novel therapeutic approaches.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".