Federated Multi-Omics Intelligence for Predictive and Personalized Cancer Care

Abstract

The integration of big data analytics, artificial intelligence (AI), and multi-omics profiling is transforming oncology by facilitating personalized, data-informed treatment. This study builds on the systematic review by Ahmed et al. (2025) and presents a computationally validated framework—OncoData-Fusion—that amalgamates genomics, transcriptomics, proteomics, metabolomics, electronic health records (EHRs), and real-time wearable data within a federated-learning architecture. Public repositories (TCGA, METABRIC, MIMIC-IV) and simulated sensor streams were examined utilizing deep neural networks and gradient-boosted ensembles. The federated model attained 92% accuracy and an area-under-curve (AUC) of 0.94 for treatment-response prediction, exceeding EHR-only baselines by 27%. SHAP-based explainability elucidated biologically relevant biomarkers (TP53, BRCA1, PTEN) and clarified the rationale of the model. The research additionally investigates governance, interoperability, and equality concerns that affect the clinical use of AI-driven oncology. The results indicate that the incorporation of multi-omics with privacy-preserving and explainable AI significantly improves predictive accuracy and ethical viability in cancer treatment.