Graph Neural Network-Based Integration of Long-Read Immune Genotyping and Skeletal Muscle Transcriptomics for Precision Nutrition and Personalized Exercise Response Prediction
Keywords:
Graph neural networks, long-read sequencing, immune genotyping, transcriptomics, precision nutrition, exercise response, personalized medicine, systems architectureAbstract
The convergence of long-read sequencing technologies, high-resolution immune genotyping, and skeletal muscle transcriptomics offers an unprecedented opportunity to model the complex biological interactions that govern individual responses to nutrition and exercise. This paper proposes a graph neural network (GNN)-based integrative framework that fuses polymorphic immune gene profiles, such as human leukocyte antigen and killer-cell immunoglobulin-like receptor alleles, with transcriptomic signatures from skeletal muscle biopsies. By representing biological entities and their functional relationships as a heterogeneous graph, the GNN architecture captures nonlinear dependencies among genetic variation, gene expression, and environmental stimuli, enabling the prediction of personalized outcomes in dietary intervention and exercise regimens. We examine the system-level design trade-offs involved in building such a multimodal pipeline, including data heterogeneity, graph construction strategies, scalability constraints, and model interpretability. Deployment considerations addressing computational infrastructure, real-time inference, and integration with wearable sensor streams are discussed. The governance landscape is analyzed with respect to data privacy, algorithmic fairness across diverse populations, and regulatory oversight for clinical translation. Cross-domain comparisons with similar integrative approaches in drug discovery and multi-omics oncology illustrate structural parallels and unique challenges. Forward-looking perspectives emphasize the need for federated learning architectures, robust causal inference methods, and longitudinal validation frameworks. The proposed GNN-based paradigm moves beyond single-modality biomarker analysis toward a systems-level understanding of human variability, with profound implications for precision health, sports science, and public nutrition policy.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
