1. Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning.
- Author
-
O'Donnell TJ, Kanduri C, Isacchini G, Limenitakis JP, Brachman RA, Alvarez RA, Haff IH, Sandve GK, and Greiff V
- Subjects
- Animals, Humans, Receptors, Antigen, T-Cell immunology, Receptors, Immunologic immunology, Receptors, Immunologic metabolism, Software, Adaptive Immunity immunology, Machine Learning
- Abstract
The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts., Competing Interests: Declaration of interests V.G. declares advisory board positions in aiNET GmbH, Enpicom B.V., Absci, Omniscope, and Diagonal Therapeutics. V.G. is a consultant for Adaptive Biosystems, Specifica Inc., Roche/Genentech, immunai, LabGenius, and FairJourney Biologics. T.J.O., G.I., J.P.L., R.A.B., R.A.A., and V.G. are employees of Imprint LLC. T.J.O is a consultant for CDI Labs., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
- Published
- 2024
- Full Text
- View/download PDF