AI-Enabled Multi-Omics Modeling of Aberrant Gene Regulatory Programs in Tumor Development
Keywords:
multi-omics integration, gene regulatory networks, artificial intelligence, cancer genomics, phase separation, algorithmic fairness, health infrastructure, robustness, governance, precision oncologyAbstract
The integration of artificial intelligence with multi-omics data has opened transformative avenues for deciphering the complex gene regulatory programs that drive tumor development. This paper presents a systems-level examination of AI-enabled multi-omics modeling, focusing on how machine learning architectures can capture aberrant regulatory mechanisms from genomics, transcriptomics, proteomics, and epigenomics data. We argue that the central challenge lies not only in predictive accuracy but in the structural trade-offs between model interpretability, robustness, fairness, and scalability within socio-technical infrastructures. The paper systematically dissects the architectural choices for integrating heterogeneous omics layers, including graph-based and transformer models, and evaluates their capacity to represent non-linear regulatory interactions such as phase separation and chromatin remodeling. We then discuss the critical governance and policy implications, including data sovereignty, algorithmic bias, and equitable deployment across global health systems. By situating technical modeling within broader infrastructural and ethical considerations, we provide a comprehensive framework for deploying AI-omics systems in translational oncology. The analysis draws on recent advances in deep learning, regulatory genomics, and health informatics, and concludes with forward-looking perspectives on sustainable, fair, and robust AI-driven discovery in cancer biology.
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