Decoding the Microbiome-Disease Axis with Interpretable Graph Neural Networks

By bridging the gap between predictive accuracy and biological interpretability, GIM overcomes a key limitation in current approaches, offering a unified framework to both predict dysbiosis-associated disease states and identify actionable microbial targets for therapeutic intervention. This dual capability represents a critical advance toward precision microbiome engineering and scalable hypothesis generation in translational microbiome research.