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

Anthony Culos, Amy S. Tsai, Natalie Stanley, Martin Becker, Mohammad S. Ghaemi, David R. McIlwain, Ramin Fallahzadeh, Athena Tanada, Huda Nassar, Camilo Espinosa, Maria Xenochristou, Edward Ganio (+19 others)
2020 Nature Machine Intelligence  
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
more » ... 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.
doi:10.1038/s42256-020-00232-8 pmid:33294774 pmcid:PMC7720904 fatcat:kptdfh4mgjahjojfccqrhhbvbi