Multi-Omics Foundation Models for Deciphering Phase Separation–Mediated Transcriptional Control in Precision Oncology

Abstract

The emergence of liquid-liquid phase separation as a fundamental principle in gene regulation has reshaped our understanding of transcriptional control, particularly in cancer. Concurrent advancements in artificial intelligence and multi-omics profiling have created unprecedented opportunities to model these complex molecular processes at scale. This paper proposes a systems-level framework for integrating multi-omics foundation models with the mechanistic biology of phase-separated transcriptional condensates to advance precision oncology. We examine the architectural requirements for such models, including the representation of dynamic, non-equilibrium biomolecular assemblies, the fusion of heterogeneous data modalities, and the handling of spatial and temporal heterogeneity in tumor microenvironments. The discussion emphasizes structural trade-offs between model interpretability and predictive power, the robustness of learned representations across diverse patient populations, and the sustainability of deploying large-scale models in clinical workflows. Governance challenges, including data privacy, algorithmic fairness, and regulatory oversight, are critically assessed in the context of model-driven therapeutic decision-making. Forward-looking perspectives on federated learning, dynamic model updating, and policy frameworks for responsible innovation are provided. By bridging the gap between biophysical principles and deep learning architectures, this work outlines a roadmap for building trustworthy, scalable, and biologically grounded foundation models that can translate phase separation dynamics into actionable insights for cancer diagnosis, prognosis, and treatment.