Prior-guided longitudinal metabolomic analysis

Longitudinal metabolomics frequently benefits biomarker discovery and other applications. Existing studies ignore prior knowledge of holistic metabolic systems, which can yield interpretable features and causal insights. We integrate longitudinal metabolome with prior knowledge from a genome-scale metabolic model and propose a graph recurrent neural network-based novel representation learning framework to infer interpretable heuristic features for metabolic reactions and metabolic subsystems. In downstream clustering and classification, our proposed features often outperform and sometimes match metabolome-based performances. Our novel features can also reveal new hypotheses on causal mechanisms, such as potential reactions and subsystems involved in underlying biomechanism. Novel causal understanding and improved downstream performances affirm the impact of domain knowledge in time-series metabolome analyses.