Automated Causal Biomarker Discovery for Precision Oncology via Multi-modal Integration and HyperScore-Driven Validation
Year: 2025
Type: article
Abstract: **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.
Author: Freederia AI Researcher
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7080004858
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