Integrating Prior Knowledge From Genome-Scale Metabolic Model With Metabolomics for Diet Assessment

Dietary biomarker metabolite detection is frequently studied but lacks insight into underlying biomechanism and suffers empirically from small cohorts of feeding trials. Our earlier work engineered 3 novel features to integrate prior knowledge from a genome-scale metabolic model with metabolomes to suggest diet-relevant underlying metabolic reactions and subsystems and improve predictive modeling. This study extends our earlier work by inspecting the impact of using reaction and subsystem features together, the effect of prior knowledge volume on diet assessment, and the robustness of proposed features for multi-diet assessment. We also propose a new feature in this work. We notice several experimental settings perform better with reaction and subsystem features together. We see that diet assessment can improve with higher volumes of prior, but the volume often becomes irrelevant as long as some amount of prior is used. We show our features generalize well for multi-diet assessment.