Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Advances in Computer, Signals and Systems
  4. Vol 10, Issue 2, 2026

Advances and Future Prospects of Diffusion Models in Cellular Perturbation Modeling

Download as PDF

DOI: 10.23977/acss.2026.100216 | Downloads: 0 | Views: 17

Author(s)

Hanze Bao 1

Affiliation(s)

1 School of Software, Taiyuan University of Technology, Taiyuan, 030000, China

Corresponding Author

Hanze Bao

ABSTRACT

Due to the fast development of single-cell transcriptomics technologies, society is provided unprecedented resolution for analyzing cellular heterogeneity and researching gene regulatory networks, and individuals believe that effective solutions to adequately reproduce the cellular perturbations should be urgently identified. Nonetheless, conventional computational algorithms can not efficiently describe non-linear behavior, transitions, or extrapolate across perturbations, so it becomes difficult to satisfy the practical requirements of precision medicine research. Recently, it has been shown that deep generative models (especially diffusion models) have tremendous benefits in cell perturbation prediction because of the excellent distribution learning and strong generation quality of these models. The provided paper is a systematic literature review of the underlying theoretical principles and technological advancements of diffusion models. It fully details all major contributions of representative models like Squidiff in predictions of responses to genetic perturbation and drug simulation. Moreover, it discusses the challenges of the field and its research opportunities depending on computational capabilities, biological insight, and the incorporation of multi-omics.

KEYWORDS

Diffusion models; Cellular perturbations; Single-cell transcriptomics; Generative models; Virtual cells

CITE THIS PAPER

Hanze Bao. Advances and Future Prospects of Diffusion Models in Cellular Perturbation Modeling. Advances in Computer, Signals and Systems (2026). Vol. 10, No. 2, 147-153. DOI: http://dx.doi.org/10.23977/acss.2026.100216.

REFERENCES

[1] Kingma, D. P. & Welling, M. (2014). Auto-encoding variational Bayes. In 2nd International Conference on Learning Representations (ICLR 2014) (eds Bengio, Y. & LeCun, Y.).
[2] Fuest, Michael, et al. (2026). "Diffusion models and representation learning: A survey." IEEE Transactions on Pattern Analysis and Machine Intelligence.
[3] He, S., Zhu, Y., Tavakol, D.N. et al. (2026). Squidiff: predicting cellular development and responses to perturbations using a diffusion model. Nat Methods 23, 65–77. 
[4] Lei Sun, Jingwen Wang, Peng Hu, Xiuqing Mao, Cuiyun Hu, Zhihong Wang. (2026). IG-3D: Integrated-Gradients 3D Optimization for Private Transformer Inference[J]. Computers, Materials & Continua, 87 (5): 1158-1176.
[5] Clough, Emily, and Tanya Barrett. (2016). "The gene expression omnibus database." Statistical genomics: methods and protocols. New York, NY: Springer New York,  93-110.
[6] Sicilia, M. A., García-Barriocanal, E., & Sánchez-Alonso, S. (2017). Community curation in open dataset repositories: insights from Zenodo. Procedia Computer Science, 106, 54-60.
[7] Srivatsan, S. R., McFaline-Figueroa, J. L., Ramani, V., Saunders, L., Cao, J., Packer, J., ... & Trapnell, C. (2020). Massively multiplex chemical transcriptomics at single-cell resolution. Science, 367 (6473), 45-51.
[8] Singh J. (2011). Journal of pharmacology & pharmacotherapeutics, [J]. FigShare, 2(2): 138.
[9] Dai B, Wipf D. Diagnosing and enhancing VAE models[J]. arXiv preprint arXiv:1903.05789, 2019.
[10] Wang, Kunfeng, et al. (2017). "Generative adversarial networks: introduction and outlook." IEEE/CAA Journal of Automatica Sinica 4.4 : 588-598.
[11] Zhang, Qinsheng, Molei Tao, and Yongxin Chen. (2022). "gddim: Generalized denoising diffusion implicit models." arXiv preprint arXiv:2206.05564.
[12] Song, Y., & Dhariwal, P. (2024). Improved techniques for training consistency models. In International Conference on Learning Representations (Vol. 2024, pp. 15078-15097).
[13] Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., ... & Quake, S. R. (2024). How to build the virtual cell with artificial intelligence: Priorities and opportunities. Cell, 187(25), 7045-7063.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 50477
Visits: 1287380

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.