Yongheng Wang

Deep interpretable learning of sample representations for characterizing disease states in single-cell transcriptomics

Phenoverse is introduced, an interpretable deep learning framework that learns sample-level disease state representations through cell type-aware residual encoding, prototype learning, and Perceiver-based aggregation that demonstrates that trajectory-derived genes reveal cross-cohort molecular programs and show consistently higher reproducibility than traditional case-control comparisons.