Automated Identification and Prioritization of Therapeutic Targets in Complex Genetic Networks via HyperScore-Guided Network Stratification

**Abstract:** This paper presents a novel framework, Network Stratification via HyperScore-Guided Analytics (NSHGA), for automated identification and prioritization of therapeutic targets within complex genetic networks. NSHGA leverages a multi-layered evaluation pipeline coupled with a hyper-score function to dynamically classify nodes within a network based on their predicted therapeutic potential. This method combines logical consistency verification, code execution verification, novelty assessment, and impact forecasting, culminating in a final HyperScore used to rank targets. The system operates on readily available, established technologies – graph neural networks, stochastic optimization, and Bayesian calibration – enabling immediate commercial application. We demonstrate the framework's efficacy using curated human cancer gene networks, highlighting its potential to accelerate drug discovery and personalized medicine through improved target selection.