Machine learning in AIRR diagnostics: Advances and applications

Aslı Semerci, Celine AlBalaa, Brian Corrie, Dylan Duchen, Gisela Gabernet, Jinwoo Leem, Enkelejda Miho, Ulrik Stervbo, Justin Barton, Pieter Meysman,
Machine learning in AIRR diagnostics: Advances and applications,
ImmunoInformatics,
Volume 20,
2025,
100062,
ISSN 2667-1190,
https://doi.org/10.1016/j.immuno.2025.100062.
(https://www.sciencedirect.com/science/article/pii/S2667119025000151)
Abstract: Recent advancements in sequencing technologies have led to an exponential increase in adaptive immune receptor repertoire (AIRR) data. These receptors, crucial to the adaptive immune system, are believed to have strong potential for diagnostic applications. The immune repertoires represent a wealth of data, creating a growing demand for robust computational methods to analyze and interpret this vast amount of information. In this review, we examine the application of machine learning algorithms for the classification and analysis of AIRR-seq data for different diagnostic applications. We provide a high-level division of current approaches based on their focus on repertoire-level or sequence-level features. We provide an overview of the current state of public AIRR data sets available for model training. Finally, we briefly highlight what lessons can be learned from successful AIRR diagnostic approaches and what hurdles still must be overcome.
Keywords: Machine learning; Adaptive immune receptor repertoire (AIRR); Diagnostic