DeepImmuneMet: An Explainable Multi-Omics AI Framework for Linking Immune-Gene Polymorphisms to Exercise-Induced Metabolic Adaptation and Weight-Loss Outcomes
Keywords:
explainable artificial intelligence, multi-omics, immune gene polymorphisms, exercise-induced metabolic adaptation, weight loss, federated learning, fairness, precision health, socio-technical systemsAbstract
The intersection of immune genetics, multi-omics profiling, and exercise physiology presents a frontier for precision health interventions. Existing machine learning frameworks that integrate genomic, transcriptomic, proteomic, and metabolomic data often lack the explanatory depth required to translate high-dimensional biological signals into clinically actionable insights. This paper introduces DeepImmuneMet, an explainable artificial intelligence framework designed to model how polymorphisms in immune-related genes modulate metabolic adaptation and weight-loss outcomes in response to structured exercise regimens. The architecture combines a deep neural network with attention-based mechanisms and a post-hoc interpretability layer that produces biologically grounded feature attributions. We address critical design trade-offs involving model complexity, data heterogeneity, and generalizability across populations. The framework is situated within a broader socio-technical infrastructure that includes federated learning for privacy-preserving data sharing, dynamic data governance protocols, and fairness-aware calibration procedures to mitigate biases arising from ancestral diversity in immune gene repertoires. We further discuss deployment challenges in clinical and community settings, emphasizing the need for robust, sustainable, and ethically aligned AI systems. Through case illustrations drawn from multi-center longitudinal studies, we demonstrate how DeepImmuneMet can uncover polymorphic drivers of interindividual variability in exercise responses, thereby enabling personalized exercise prescriptions. The paper concludes with a forward-looking assessment of policy implications, regulatory considerations, and the potential for integrating such frameworks into digital health ecosystems.
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