Interpretable multi-omics machine learning reveals drought-driven shifts in plant-microbe interactions
Hayato Yoshioka, Pavla Debeljak, Soizic Prado, Yushiro Fuji, Yasunori Ichihashi, Hiroyoshi Iwata
The paper uses interpretable machine learning to analyze multi-omics data from soybean under drought stress, identifying key biomarkers and interactions influencing plant phenotypes.
Environmental Microbiome · Apr 10, 2026Peer-reviewedBioinformatics