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Applications of Machine and Deep Learning in Adaptive Immunity
Annual Review of Chemical and Biomolecular Engineering ( IF 8.4 ) Pub Date : 2021-06-07 , DOI: 10.1146/annurev-chembioeng-101420-125021
Margarita Pertseva, Beichen Gao, Daniel Neumeier, Alexander Yermanos, Sai T. Reddy

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.

中文翻译:


机器学习和深度学习在自适应免疫中的应用

适应性免疫由淋巴细胞 B 和 T 细胞介导,它们分别表达大量多样的 B 细胞和 T 细胞受体,并结合通过主要组织相容性复合物 (MHC) 的肽抗原呈递,可以识别病原体和患病的细胞。近年来,深度测序的进步导致适应性免疫受体库数据量的大量增加;此外,蛋白质组学技术已经产生了大量关于肽-MHC 呈递的数据。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,这些模型可用于识别免疫组库中的复杂和高维模式。

更新日期:2021-06-08
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