O'Donnell Timothy J, Kanduri Chakravarthi, Isacchini Giulio, Limenitakis Julien P, Brachman Rebecca A, Alvarez Raymond A, Haff Ingrid H, Sandve Geir K, Greiff Victor
Imprint Labs, LLC, New York, NY, USA.
Department of Informatics, University of Oslo, Oslo, Norway; UiO:RealArt Convergence Environment, University of Oslo, Oslo, Norway.
Cell Syst. 2024 Dec 18;15(12):1168-1189. doi: 10.1016/j.cels.2024.11.006.
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.
适应性免疫系统以B细胞和T细胞受体序列的形式保存着关于过去和当前免疫反应的宝贵信息，但我们解码这些信息的能力有限。机器学习方法正在积极研究中，用于一系列与理解和操纵适应性免疫受体库相关的任务，包括将受体与其结合的抗原进行匹配、生成用作治疗剂的抗体或T细胞受体，以及根据患者的受体库进行疾病诊断。这些任务的进展有可能大幅改善疫苗、治疗剂和诊断方法的开发，并增进我们对基本免疫学原理的理解。我们概述了该领域的关键挑战，强调了软件基准测试、有针对性的大规模数据生成和协调研究工作的必要性。
