MeLSI framework learns optimal distance metrics for microbiome community comparison, detecting shifts undetected by standard methods.

A new machine learning framework, MeLSI, learns which specific microbes drive differences between groups in microbiome community comparisons. On the DietSwap dataset, MeLSI was the only method to achieve significance at α=0.05, detecting diet-induced shifts missed by standard me…

• Framework uses ensemble learning and permutation testing to optimize data-adaptive distance metrics; validated on synthetic benchmarks and real datasets for type I error control and power.
• On DietSwap data, MeLSI was the sole method detecting significant community shifts (α=0.05) where fixed ecological metrics failed (exact power/effect-size figures not reported in source).
• Key limitation: Reported validation relies on existing public datasets and synthetic benchmarks; prospective clinical validation for specific diagnostic or therapeutic biomarker targets has not yet been performed.

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