Enhancing Interpretability in Multivariate Metabolomic Modeling through Network-Guided Perturbation-Based Explanations

Multivariate modeling is crucial for uncovering complex patterns in metabolomic data, yet the interpretability of such models remains a major challenge. Here, we propose a network-guided framework that enhances perturbation-based explanations by grouping metabolites according to communities identified in metabolic networks, rather than relying on predefined pathways. Applied to postprandial plasma metabolomic data as a model example, the method revealed both established and novel functional modules relevant to glucose metabolism. The use of metabolite communities derived from network representation in perturbation-based models serves as a complementary tool for the biochemical interpretation of multivariate models, extending beyond fixed, stablished pathways. The strategy is model-agnostic and readily transferable across omics domains, offering a robust tool for improving model interpretability and hypothesis generation in complex biological datasets.