Integration of Mechanistic Immunological Knowledge into a Machine Learning Pipeline Increases Predictive Power

Abstract

+ Co-lead authorship * Co-senior authorshipThe dense network of interconnected cellular signaling responses quantifiable in peripheral immune cells provide a wealth of actionable immunological insights. While 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, limited cohort size together with the high dimensionality of data increases the possibility of false positive discoveries and model overfitting. We introduce a 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 power even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictive power for clinically-relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset.

Single-cell peripheral immunoprofiling of Alzheimer’s and Parkinson’s diseases

“…The stimulations and antibody probes generated a total of 4200 intracellular signaling responses (35 PMBC subtypes under eight stimulating conditions and assayed for 15 intracellular responses), which were used to identify the potential immune features with the aid of cell signaling knowledge, machine learning methods, and statistical analysis. (20), and examined its predictive power on unseen data as an alternative to multiple univariate testing (Fig. 3).…”

“…The iEN added on to the com monly used EN algorithm the capability to incorporate knowledge of intracellular signal transduction on the generation of the mass cytometry data. We have recently reported in detail that this model demonstrates increased predictive power relative to multiple tra ditional methods (20). Briefly, the iEN algorithm optimized the coefficient () for each associated feature by minimizing the cost function…”