Multi-modal Bayesian Network Inference for Robust Predictive Biomarker Identification in Pharmacodynamic Response to Anti-VEGF Treatment in Metastatic Colorectal Cancer (mCRC)

**Abstract:** Reliable predictive biomarkers for individual patient response to anti-VEGF therapy in mCRC remain elusive. This study introduces a novel framework, termed Multi-modal Bayesian Network Inference (MBNI), leveraging synchronized multi-omics data (genomics, proteomics, metabolomics, and imaging radiomics) and pharmacokinetics (PK) profiles within a Bayesian network framework to achieve robust and personalized biomarker identification. The approach incorporates a hierarchical prior distribution that incorporates known biological pathways. MBNI demonstrably improves predictive accuracy compared to single-modality and traditional regression models, signifying a paradigm shift in personalized pharmaceutical response prediction for mCRC. The system is immediately commercializable as a diagnostic support tool integrating with existing clinical workflow and promises to reduce healthcare costs by optimizing treatment decisions.