Identifying interactions in omics data for clinical biomarker discovery using symbolic regression

The identification of predictive biomarker signatures from omics data for clinical applications is an active area of research. Recent developments in assay technologies and machine learning (ML) methods have led to significant improvements in predictive performance. However, most high-performing ML methods suffer from complex architectures and lack interpretability. Here, we present the application of a novel symbolic-regression-based algorithm, the QLattice, on a selection of clinical omics data sets. This approach generates parsimonious high-performing models that can both predict disease outcomes and reveal putative disease mechanisms. Due to their high performance, simplicity and explicit functional form, these biomarker signatures can be readily explained, thereby making them attractive tools for high-stakes applications in primary care, clinical decision making and patient stratification.