AI-Enabled Multi-Omics Integration for Characterizing Dynamic Gene Expression Programs in Tumor Cells
Keywords:
multi-omics integration, artificial intelligence, dynamic gene expression, tumor transcriptomics, deep learning architecture, systems biology, precision oncology, fairness, sustainability, governanceAbstract
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.
References
1. Argelaguet, R., Cuomo, A. S., Stegle, O., & Marioni, J. C. (2021). Computational principles and challenges in single-cell data integration. Nature Biotechnology, 39(10), 1202–1215.
2. Eraslan, G., Avsec, Ž., Gagneur, J., & Theis, F. J. (2019). Deep learning: new computational modelling techniques for genomics. Nature Reviews Genetics, 20(7), 389–403.
3. Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLOS Medicine, 15(11), e1002689.
4. Hawe, J. S., Theis, F. J., & Heinig, M. (2022). Inferring interaction networks from multi-omics data. Frontiers in Genetics, 13, 856245.
5. Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M., & Klein, A. M. (2018). Fundamental limits on dynamic inference from single-cell snapshots. Proceedings of the National Academy of Sciences, 115(10), E2467–E2476.
6. Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2019). scGen predicts single-cell perturbation responses. Nature Methods, 16(8), 715–721.
7. Gayoso, A., Steier, Z., Lopez, R., Regier, J., Nazor, K. L., Streets, A., & Yosef, N. (2022). A Python library for probabilistic analysis of single-cell omics data. Nature Biotechnology, 40(2), 163–166.
8. Zitnik, M., Agrawal, M., & Leskovec, J. (2018). Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 34(13), i457–i466.
9. Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., ... & Ellinor, P. T. (2023). Transfer learning enables predictions in network biology. Nature, 618(7965), 616–624.
10. Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics, 8(8), 832.
11. Wang, T., Huang, J., Yu, T., Xu, J., & Li, S. (2023). Generalizable and transferable gene expression predictions using a transformer model. Nature Communications, 14, 5563.
12. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–3650.
13. Yang, J., Chung, C. I., Koach, J., Liu, H., Navalkar, A., He, H., ... & Shu, X. (2024). MYC phase separation selectively modulates the transcriptome. Nature Structural & Molecular Biology, 31(10), 1567-1579.
14. Patterson, D., Gonzalez, J., Holzle, U., & Le, Q. (2021). The carbon footprint of machine learning training will plateau, then shrink. Computer, 54(8), 18–28.
15. Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., ... & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35.
16. Finlayson, S. G., Bowers, J. D., Ito, J., Zittrain, J. L., Beam, A. L., & Kohane, I. S. (2019). Adversarial attacks on medical machine learning. Science, 363(6433), 1287–1289.
17. Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019). Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics, 51(4), 584–591.
18. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., ... & Gebru, T. (2019). Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220–229.
19. Hinkson, I. V., Davidsen, T., Hoadley, K. A., Morin, G. B., Mesirov, J. P., & Mills, G. B. (2017). A comprehensive genomic characterization of the human tumor microenvironment. Cell, 170(5), 916–929.
20. Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., ... & Natarajan, V. (2023). Large language models encode clinical knowledge. Nature, 620(7972), 172–180.
21. Global Alliance for Genomics and Health. (2016). A federated ecosystem for sharing genomic, clinical data. Science, 352(6291), 1278–1280.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Computational Intelligence Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
