Graph Neural Network Analysis of Gene Regulatory Networks in Cancer-Associated Transcriptional Dysregulation
Keywords:
graph neural networks, gene regulatory networks, transcriptional dysregulation, cancer genomics, systems biology, interpretability, robustness, data governanceAbstract
The study of gene regulatory networks (GRNs) in the context of cancer-associated transcriptional dysregulation presents a complex systems-level challenge that requires advanced computational modeling. Graph neural networks (GNNs) have emerged as a powerful class of architectures for learning representations from relational data, making them particularly suited for analyzing the intricate wiring diagrams of gene interactions. This paper examines the application of GNNs to GRN analysis, focusing on the structural and functional trade-offs inherent in modeling high-dimensional, noisy, and dynamic biological networks. We discuss architectural considerations such as message-passing schemes, attention mechanisms, and graph pooling, and evaluate their implications for model interpretability, scalability, and robustness. The paper further explores the deployment of GNN-based models in translational research, including considerations of data governance, algorithmic fairness, and the sustainability of computational infrastructure in resource-constrained settings. Through a critical synthesis of recent advances, we highlight how GNNs can reveal mechanistic insights into transcriptional dysregulation while simultaneously raising important questions about reproducibility, transparency, and ethical deployment. We conclude by outlining future directions for integrating multi-omics data, incorporating temporal dynamics, and building more robust and equitable analytical pipelines for cancer genomics.
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