Strategies to Query and Display Allergy-Derived Epitope Data from the Immune Epitope Database
Bibliographic record
Abstract
The recognition of specific epitopes on allergens by antibodies and T cells is a key element in allergic processes. Analysis of epitope data may be of interest for basic immunopathology or for potential application in diagnostics or immunotherapy. The Immune Epitope Database (IEDB) is a freely available repository of epitope data from infectious disease agents, as well as epitopes defined for allergy, autoimmunity, and transplantation. The IEDB curates the experiments associated with each epitope and thus provides a variety of different ways to search the data. This review aims to demonstrate the utility of the IEDB and its query strategies, including searching by epitope structure (peptidic/nonpeptidic), by assay methodology, by host, by the allergen itself, or by the organism from which the allergen was derived. Links to tools for visualization of 3-D structures, epitope prediction, and analyses of B and T cell reactivity by host response frequency score are also highlighted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".