AI-Driven Epigenomic Profiling of Stem Cell Senescence and Stemness for Regenerative Dental Medicine
Keywords:
artificial intelligence; epigenomics; cellular senescence; dental pulp stem cells; regenerative medicine; data governanceAbstract
Regenerative dental medicine is increasingly dependent on understanding the molecular states of dental pulp stem cells, particularly the balance between senescence and stemness that determines reparative capacity. Epigenomic profiling offers high-resolution insight into the regulatory programs governing these states, but the resulting datasets are large, heterogeneous, and context dependent. Artificial intelligence provides powerful representational capacities to model such complexity, yet successful translation into dental practice requires system-level design rather than isolated model development. This paper examines AI-driven epigenomic profiling for stem cell senescence and stemness across infrastructure, model architecture, governance, deployment, robustness, fairness, sustainability, and policy. A successful system must integrate multimodal data harmonization, interpretable deep learning, federated and privacy-preserving learning, rigorous biological validation, and staged clinical evaluation. Dental settings present distinctive constraints including limited computational resources, small cohort sizes, and the need to align with broader oral health equity goals. The discussion emphasizes structural trade-offs between centralized accuracy and decentralized confidentiality, between model complexity and clinical explainability, and between rapid translation and long-term safety monitoring. By addressing these dimensions, AI-driven epigenomic profiling can evolve from a promising computational concept into a trustworthy component of regenerative dental medicine.
References
1. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29.
2. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
3. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
4. 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.
5. Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016). Deep learning for computational biology. Molecular Systems Biology, 12(7), 878.
6. Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biology, 14(10), R115.
7. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013). The hallmarks of aging. Cell, 153(6), 1194–1217.
8. Laird, P. W. (2010). Principles and challenges of genome-wide DNA methylation analysis. Nature Reviews Genetics, 11(3), 191–203.
9. Ziller, M. J., Gu, H., Müller, F., Donaghey, J., Tsai, L. H. Y., Kohlbacher, O., De Jager, P. L., Rosen, E. D., Bennett, D. A., Bernstein, B. E., Gnirke, A., & Meissner, A. (2013). Charting a dynamic DNA methylation landscape of the human genome. Nature, 500(7463), 477–481.
10. Teschendorff, A. E., & Relton, C. L. (2018). Statistical and integrative system-level analysis of DNA methylation data. Nature Reviews Genetics, 19(3), 129–147.
11. Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., & Kelley, D. R. (2021). Effective gene expression prediction from sequence by integrating long-range interactions. Nature Methods, 18(10), 1196–1203.
12. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
13. Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P. M., Zietz, M., Hoffman, M. M., Xie, W., Rosen, G. L., Lengerich, B. J., Israeli, J., Lanchantin, J., Woloszynek, S., Carpenter, A. E., Shrikumar, A., Xu, J., ... Greene, C. S. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of the Royal Society Interface, 15(141), 20170387.
14. Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358.
15. Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., & Bakas, S. (2020). Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 10(1), 12598.
16. Warnat-Herresthal, S., Schultze, H., Shastry, K. L., Manamohan, S., Mukherjee, S., Garg, V., Sarveswara, R., Händler, K., Pickkers, P., Aziz, N. A., Ktena, S., ... Schultze, J. L. (2021). Swarm learning for decentralized and confidential clinical machine learning. Nature, 594(7862), 265–270.
17. Beaulieu-Jones, B. K., Wu, Z. S., Williams, C., Lee, R., Bhavnani, S. P., Byrd, J. B., & Greene, C. S. (2019). Privacy-preserving generative deep neural networks support clinical data sharing. Circulation: Cardiovascular Quality and Outcomes, 12(7), e005122.
18. Wang, X., Wang, L., Zhou, L., Chen, L., Shi, J., Ge, J., ... & Zou, D. (2025). NUP62 alleviates senescence and promotes the stemness of human dental pulp stem cells via NSD2-dependent epigenetic reprogramming. International Journal of Oral Science, 17(1), 34.
19. Takahashi, K., & Yamanaka, S. (2006). Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell, 126(4), 663–676.
20. Sharpless, N. E., & DePinho, R. A. (2007). How stem cells age and why this makes us grow old. Nature Reviews Molecular Cell Biology, 8(9), 703–713.
21. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
22. Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., Ossorio, P. N., Thadaney-Israni, S., & Goldenberg, A. (2019). Do no harm: A roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337–1340.
23. Kaeberlein, M., Rabinovitch, P. S., & Martin, G. M. (2015). Healthy aging: The ultimate preventative medicine. Science, 350(6265), 1191–1193.
24. Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., ... Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589.
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