A high-dimensional benchmark of objective functions and biological resolutions for personalized metabolic phenotyping.

Pathway-level minimum reaction fluxes outperform aggregate pathway activity in personalized metabolic modeling, achieving high diagnostic accuracy across diverse human diseases.

• Why it matters: Accurate metabolic phenotyping is crucial for understanding complex diseases, but the optimal modeling strategies and objective functions remain unclear, limiting clinical translation.
• What they did: Benchmarking 57,600 configurations across six pathologies, the study evaluated combinations of objective functions, biological resolutions, and classifiers, focusing on pathway-level fluxes and feature selection strategies.
• The result: Isolating bottleneck reactions yielded the best diagnostic signals, with predictive accuracy largely unaffected by feature density, enabling robust, personalized metabolic phenotyping with flexible modeling approaches.