Physics-informed AI with chemical master equation dynamics for driver-gene subclone detection and risk labeling.

ABSTRACT: Subclonal populations shape progression and therapy response but are difficult to resolve in bulk transcriptomes, where rare signals are diluted by dominant clones. Existing methods often treat samples as static mixtures, infer lineages without expression-defined states, or rank genes without specifying when to evaluate, yielding unstable signatures. We propose magicSubclonal, a physics-informed framework that embeds driver-gene dynamics using a Chemical Master Equation. From gene expression, we estimate decay, burst initiation, and burst size; automatically select the time of maximal rare-state separation; and identify subclones whose extremes lie within calibrated predictive envelopes. Driver-timed states are then linked to non-driver genes with False Discovery Rate control, and c

SUBMITTER: Atitey K 

PROVIDER: S-EPMC12604972 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature