Applications of Machine and Deep Learning in Adaptive Immunity.

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• Adaptive immunity relies on B and T lymphocytes with diverse receptors to recognize pathogens.
• Major histocompatibility complexes (MHCs) present peptide antigens for immune cell recognition.
• Advances in deep sequencing and proteomics have generated large datasets of immune receptor repertoires and peptide-MHC data.

Purpose of the Study:

• To introduce adaptive immune repertoires and machine/deep learning for biological sequence data.
• To summarize applications of these methods in immunology.
• To highlight the potential for predicting host immunological status and receptor specificity.

Main Methods:

• Utilizing deep sequencing for adaptive immune receptor repertoire data generation.
• Employing proteomics techniques for peptide-MHC presentation data.
• Training machine and deep learning models on large-scale biological sequence datasets.

Main Results:

• Identification of complex, high-dimensional patterns within immune repertoires.
• Development of models capable of predicting immunological status.
• Enabling prediction of antigen specificity for individual receptors.

Conclusions:

• Machine and deep learning are powerful tools for analyzing adaptive immune repertoire data.
• These approaches facilitate a deeper understanding of immune responses.
• Applications range from diagnostics to the engineering of novel immunotherapeutics.