Integrating Graph Neural Networks and Multi-Omics Data Fusion for Robust Identification of Disease-Associated Molecular Interaction Networks

Abstract

The systemic complexity of human pathologies necessitates a definitive shift from traditional reductionist single-omics investigations toward integrated, multi-layered analytical paradigms. This paper evaluates the systemic integration of graph neural networks and multi-omics data fusion as an advanced architectural framework for identifying robust, disease-associated molecular interaction networks. By combining genomic, transcriptomic, proteomic, and metabolomic profiles into unified topological structures, graph neural networks can resolve non-linear, cross-modality correlations that traditional statistical aggregations routinely obscure. We examine the structural trade-offs between early, late, and intermediate fusion strategies, detailing how intermediate graph-level fusion preserves the conditional dependencies inherent in complex biological systems. Beyond algorithmic composition, this research explicitly addresses the socio-technical, computational, and institutional infrastructures required to deploy these deep learning models within clinical and translational pipelines. We dissect the systemic vulnerabilities of these architectures, including data heterogeneity, batch effects, and adversarial vulnerabilities, while proposing frameworks for model robustness and algorithmic fairness across diverse demographic cohorts. Furthermore, the paper analyzes the governance frameworks, data privacy paradigms, and cross-institutional policy mandates essential for sustaining large-scale computational medicine. By situating deep graph architectures within the broader realities of clinical deployment, computational sustainability, and regulatory oversight, this study provides a comprehensive blueprint for the scalable, ethical, and structurally sound translation of multi-omics graph intelligence into actionable therapeutic and diagnostic discovery workflows.