The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.

外周免疫细胞中可量化的相互关联的细胞信号反应的密集网络提供了丰富的可操作的免疫学见解。尽管高通量单细胞分析技术（包括多色流式细胞术和质谱流式细胞术）已经成熟到能够在众多临床环境中对患者进行详细免疫分析的程度，但有限的队列规模和高维数据增加了假阳性的可能性发现和模型过度拟合。我们引入了一个通用的机器学习平台，免疫学弹性网络（iEN），它将免疫学知识直接融入到预测模型中。重要的是，该算法保持了高维数据集的探索性，即使与先验知识不一致，也允许包含具有强大预测能力的免疫特征。在三项独立研究中，我们的方法证明了从全血生成的质谱流式数据以及大型模拟数据集对临床相关结果的预测得到了改进。 iEN 可根据开源许可证使用。