International Journal on Science and Technology

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BioBrain: An Explainable Federated AI Framework for Multi-Omics Disease Risk Prediction in Life Science

Author(s) Mr. Rakesh Kumar Agrawal
Country United States
Abstract The increasing availability of multi-omics and clinical data has created major opportunities for AI-driven disease risk prediction in precision medicine. However, centralized learning pipelines remain constrained by privacy regulations, institutional data silos, heterogeneous modalities, and limited interpretability. To address these challenges, we propose BioBrain, a privacy-preserving federated AI framework for disease risk prediction across distributed life science institutions. BioBrain integrates genomics, transcriptomics, proteomics, metabolomics, and electronic health record (EHR) data using modality-specific neural encoders, graph neural network–based biomolecular reasoning, and adaptive cross-omics attention fusion. An explainable AI layer further provides biomarker-level attribution and pathway relevance maps to improve clinical trust and biological interpretability. Experimental benchmarking on cancer and cardiovascular cohorts demonstrates that BioBrain consistently outperforms local, centralized, and conventional federated baselines, achieving superior AUC and F1-score while preserving privacy under distributed settings. Clinically, BioBrain enables privacy-preserving cross-hospital disease risk scoring, biomarker discovery, and precision medicine decision support.
Keywords bioinformatics, disease risk prediction, explainable AI, federated learning, multi-omics
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-04-09
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