Spatial transcriptomic

In conclusion, SpaPheno demonstrates that interpretable machine learning, grounded in spatial principles of tissue organization, can systematically reveal where and why clinical phenotypes arise within complex tumor ecosystems. By integrating spatial embeddings, cell-type composition, and phenotype-informed attribution, SpaPheno moves beyond descriptive or black-box models, establishing a rigorous framework for mechanistic spatial inference. This approach is broadly applicable across diseases and data modalities, offering a scalable path toward spatially informed biomarker discovery and artificial intelligence assisted precision medicine.