ArticleImmunoinformatics (Amsterdam, Netherlands)2025
Challenges and future directions of AIRR-seq-based diagnostics.
Article in Immunoinformatics (Amsterdam, Netherlands), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Machine learning in AIRR diagnostics: Advances and applications.Immunoinformatics (Amsterdam, Netherlands) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.