Machine learning in AIRR diagnostics: Advances and applications
Abstrak
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.
Topik & Kata Kunci
Penulis (10)
Aslı Semerci
Celine AlBalaa
Brian Corrie
Dylan Duchen
Gisela Gabernet
Jinwoo Leem
Enkelejda Miho
Ulrik Stervbo
Justin Barton
Pieter Meysman
Akses Cepat
PDF tidak tersedia langsung
Cek di sumber asli →- Tahun Terbit
- 2025
- Sumber Database
- DOAJ
- DOI
- 10.1016/j.immuno.2025.100062
- Akses
- Open Access ✓