Challenges and future directions of AIRR-seq-based diagnostics
Bibliographic record
Abstract
Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a promising diagnostic method across various clinical conditions, yet its widespread implementation faces several challenges. This perspective examines the current landscape of AIRR-seq diagnostics and outlines key obstacles and opportunities for advancement. Critical challenges include the need for standardized quality controls, privacy protection under General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) frameworks, and the development of clinically compatible bioinformatics pipelines. Machine learning approaches offer potential solutions for interpreting complex repertoire signatures, though these models must balance accuracy with interpretability for clinical adoption. Future applications may include early disease detection, prognosis, and monitoring of treatment and vaccine responses. However, successful clinical integration will require sustained collaboration among funding bodies, regulatory agencies, researchers, diagnosticians, and clinicians to establish clear guidelines and expand existing repositories with well-characterized patient samples. The collaborative efforts of the AIRR Diagnostics Working Group and the AIRR Community's initiatives are working towards unlocking the potential of AIRR-seq in precision medicine and enhancing diagnostic capabilities.
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 | 0.086 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".