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SAILS fills a notable gap in the eXplainable AI (XAI) toolbox, going beyond detecting interactions alone to characterizing their functional form, with limitations under strong feature correlations and higher-order interactions.
This framework introduces kernel-based output representations that enable time-dependency-aware explanations at multiple levels of temporal granularity, including time-specific, time-resolved, and time-aggregated, while providing a unified view in which existing methods arise as special cases.
This framework demonstrates that plant nuclear ITS-derived host relatedness carries a partial predictive signal for seed-associated bacterial microbiome composition, providing a foundation for low-input predictive modelling of seed-associated bacteria and may help prioritise microbiome predictions for unsampled plant s...
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