Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions.

ABSTRACT: 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 th

SUBMITTER: Culos A 

PROVIDER: S-EPMC7720904 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature