Automated Causal Biomarker Discovery for Precision Oncology via Multi-modal Integration and HyperScore-Driven Validation

**Abstract:** This research proposes a novel framework for automated biomarker discovery in oncology leveraging multi-modal data integration and a rigorous, quantitatively-scored validation pipeline. Traditional biomarker identification relies heavily on manual analysis and suffers from limited reproducibility and scalability. Our system, leveraging a structured evaluation pipeline and HyperScore evaluation, automates this process, identifying potential biomarkers with significantly improved throughput and reliability. The framework combines genomic, proteomic, imaging, and clinical data, employing advanced causal inference techniques to isolate true causal relationships and predict therapeutic response. This framework aims to accelerate precision oncology development and improve patient outcomes.