Graph Neural Networks for Deciphering Condensate-Mediated Gene Regulatory Networks in YAP-MAML2–Associated Tumorigenesis - Pierre Nieminen (International Journal of Clinical and Translational Medicine, 2026)
The emergence of biomolecular condensates as a fundamental organizing principle in cellular regulation has reshaped the understanding of transcriptional control, particularly in oncogenic contexts where aberrant phase separation drives aberrant gene expression. This paper presents a systems-level framework that integrates graph neural networks (GNNs) with high-throughput sequencing and imaging data to decode condensate-mediated gene regulatory networks (GRNs) in YAP-MAML2–associated tumorigenesis. We argue that conventional network inference approaches, which treat regulatory interactions as static and pairwise, are insufficient to capture the dynamic, multivalent, and cooperative nature of condensate-driven transcription. GNNs, by virtue of their relational inductive biases and ability to propagate information over graph structures, offer a principled mechanism to model the interplay between phase-separated complexes and downstream transcriptional programs. The paper systematically examines the architectural trade-offs between expressivity and trainability when applying GNNs to spatially resolved genomic data, and discusses the computational infrastructure required for scalable deployment across heterogeneous clinical cohorts. Furthermore, we analyze the governance and fairness implications of deploying such models in precision oncology, emphasizing the need for interpretable predictions and robustness to distributional shifts arising from diverse patient populations. By situating the technical methodology within broader socio-technical considerations, this work provides a roadmap for building sustainable, equitable, and scientifically rigorous artificial intelligence systems that can elucidate the regulatory logic of liquid–liquid phase separation in cancer.