Open access
Jul 2026
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.
Manoj M Wagle, Yongheng Wang, Soham Samanta et al.
· bioRxiv · 0 citations