Machine learning in AIRR diagnostics: Advances and applications.

Area of Science:

• Immunoinformatics
• Computational Biology
• Machine Learning

Background:

• Sequencing technologies have rapidly increased adaptive immune receptor repertoire (AIRR) data.
• AIRR data holds significant potential for developing novel diagnostic tools.
• Analyzing vast immune repertoire data requires advanced computational methods.

Purpose of the Study:

• To review machine learning applications for classifying and analyzing AIRR-seq data in diagnostics.
• To categorize current AIRR-seq analysis approaches.
• To discuss the availability of public AIRR datasets and future directions.

Main Methods:

• Literature review of machine learning algorithms applied to AIRR-seq data.
• Classification of methods based on repertoire-level vs. sequence-level features.
• Overview of publicly available AIRR datasets for training models.

Main Results:

• Machine learning shows promise for classifying AIRR-seq data for diagnostic purposes.
• Current approaches are broadly divided into repertoire-level and sequence-level analyses.
• Publicly available AIRR datasets are crucial for developing and validating models.

Conclusions:

• Machine learning offers powerful tools for leveraging AIRR data in diagnostics.
• Understanding feature-level approaches and data availability is key.
• Further research is needed to overcome challenges and realize the full diagnostic potential of AIRR-seq.