PATHCAST: PATHWAY-STRUCTURED GENE EXPRESSION FORECASTING IN CARDIAC ORGANOIDS

Forecasting gene expression during cardiac organoid development can support experimental planning and the study of developmental programs. However, sparse, irregularly sampled RNA measurements provide limited evidence for the coordinated expression changes that emerge at later stages. We introduce PATHCAST, a pathway-structured Transformer that integrates biological pathway memberships and gene descriptions to forecast group-level expression beyond the observed RNA history. The central design couples complementary representations of pathway dynamics: a history branch uses temporal and cross-pathway differential attention, while a state branch summarizes reconstructable pathway states and recent trends. Their fusion predicts continuous-time state increments, which a pathway-constrained dynamic decoder translates into future expression changes with explicit pathway and gene-level contributions. Across within-condition and reference-assisted forecasting on three cardiac organoid datasets and one hepatic differentiation dataset, PATHCAST achieves the lowest overall mean squared error among the reported baselines. Ablations assess the contributions of pathway structure and complementary modeling components. Pathway analyses and uncertainty-based risk ranking characterize forecast behavior. Together, these findings support pathway-structured representations for interpretable forecasting from limited transcriptomic observations.