An explainable approach to learning feature representations of omics data using optimal transport to improve phenotype stratification

High-throughput omics data offer unprecedented opportunities to advance our understanding of human health and disease. However, their analysis remains challenging due to high dimensionality, noise, heterogeneity, and sparsity—factors that severely limit both the predictive accuracy and the biological interpretability of conventional machine learning approaches, particularly in patient stratification tasks. Microbiome data exemplify these challenges, as their extreme dimensionality and complex structure often obscure mechanistic insights into disease processes.