Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs), can recognize and respond to pathogens and diseased cells. In recent years, advances in deep sequencing have led to a massive increase in the amount of adaptive immune receptor repertoire data; additionally, proteomics techniques have led to a wealth of data on peptide-MHC presentation. These large-scale data sets are now making it possible to train machine and deep learning models, which can be used to identify complex and high-dimensional patterns in immune repertoires. This article introduces adaptive immune repertoires and machine and deep learning related to biological sequence data and then summarizes the many applications in this field, which span from predicting the immunological status of a host to the antigen specificity of individual receptors and the engineering of immunotherapeutics. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 12 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

中文翻译：

---

机器学习和深度学习在自适应免疫中的应用。

适应性免疫是由淋巴细胞B和T细胞介导的，它们分别表达广泛多样的B细胞和T细胞受体，并且与通过主要组织相容性复合物（MHC）呈递的肽抗原一起，可以识别和应对病原体和疾病细胞。近年来，深度测序的进展已导致自适应免疫受体库数据的数量大量增加。此外，蛋白质组学技术已经导致了有关肽-MHC呈递的大量数据。这些大规模数据集现在使训练机器和深度学习模型成为可能，该模型可用于识别免疫库中的复杂和高维模式。本文介绍了与生物序列数据有关的适应性免疫库以及机器和深度学习，然后总结了该领域的许多应用，从预测宿主的免疫状态到单个受体的抗原特异性以及免疫疗法的工程化。《化学和生物分子工程年度评论》第12卷的最终最终在线发布日期为2021年6月。有关修订的估算，请参见http://www.annualreviews.org/page/journal/pubdates。