Minimal Intervention Search in Cancer Gene Regulatory Networks via Daopological Structural Annealing and Correctable Structural Intelligence

We formulate the discovery of minimal cancer gene regulatory network (GRN) interventions as a discrete topological search problem, and solve it with Daopological Structural Annealing (DSA) combined with Correctable Structural Intelligence (CSI). DSA replaces gradient-based optimization with structurally meaningful annealing over discrete topological lattices; CSI provides a corrigible, auditable decision layer that enforces biological safety constraints. Across three cancer GRN benchmarks---p53 (pan-cancer), ESR1 (ER+ breast cancer), and KRAS (pancreatic ductal adenocarcinoma)---the DSA+CSI framework yields consistent relative topological improvements of 14.6%–18.0%. A controlled ablation shows that a deterministic Beam Search reaches the same plateau, while an unconstrained greedy search reaches a higher topological distance (0.0842) but proposes biologically over-intervened sets. The 0.02 gap to unconstrained search is thus the deliberate cost of CSI-enforced biological safety, not a search failure. This work establishes a gradient-free, discrete, and auditable alternative to conventional continuous-optimization approaches in network medicine.