AI-Enabled Multi-Omics Integration for Characterizing Dynamic Gene Expression Programs in Tumor Cells - Shane Sanders; Ross Gregory; Vishal L. Pandey; Varun Subramanian (Computational Intelligence Systems, 2026)
The rapid accumulation of multi-omics data from tumor samples has created an unprecedented opportunity to understand the dynamic gene expression programs that drive cancer progression, metastasis, and treatment resistance. However, the high dimensionality, heterogeneity, and temporal sparsity of such data present fundamental computational challenges that conventional statistical methods cannot address. This paper presents a comprehensive systems-level examination of how artificial intelligence, particularly deep learning architectures, can be harnessed to integrate multi-omics layers for the characterization of dynamic gene expression programs in tumor cells. We explore the architectural design space of integrative models, including autoencoders, graph neural networks, and transformer-based approaches, and analyze the structural trade-offs between predictive accuracy, interpretability, and computational cost. Infrastructure considerations such as distributed computing, data storage, and energy consumption are discussed in the context of sustainability and scalability. We further examine critical issues of robustness and fairness, focusing on how biases in training data, model calibration across heterogeneous patient populations, and adversarial vulnerabilities can undermine clinical translation. Governance and policy implications are addressed through the lens of regulatory frameworks for AI-driven diagnostics and the ethical deployment of omics-level predictions. By synthesizing methodological advances with socio-technical challenges, this paper provides a roadmap for the responsible integration of AI-enabled multi-omics systems into precision oncology.