Publications

You can also find my full publication list on my Google Scholar

Selected Publications

AI-Driven Dynamical Virtual Cells and its Mathematical Theory

• stVCR: Reconstructing Spatio-Temporal Dynamics of Cell Development Using Optimal Transport
  Z. Zhang, T. Li, P. Zhou. (2024).
  arXiv:2410.00844

• Modeling Cell Dynamics and Interactions with Unbalanced Mean Field Schrödinger Bridge
  Z. Zhang, Z. Wang, Y. Sun, T. Li, P. Zhou. (2025).
  arXiv:2505.11197

• Variational Regularized Unbalanced Optimal Transport: Single Network, Least Action
  Y. Sun, Z. Zhang, Z. Wang, T. Li, P. Zhou. (2025).
  arXiv:2505.11823

• Joint Velocity-Growth Flow Matching for Single-Cell Dynamics Modeling
  D. Wang, Y. Jiang, Z. Zhang, X. Gu, P. Zhou, J. Sun. (2025).
  arXiv:2505.13413

• Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-seq Data Analysis
  Z. Zhang, Y. Sun, Q. Peng, T. Li, P. Zhou. (2025).
  Entropy, 27(5), 453

• Spatial Transition Tensor of Single Cells
  Zhou, P., Bocci, F., Li, T., & Nie, Q. (2024).
  Nature Methods, 21, 1053–1062

• Learning Stochastic Dynamics from Snapshots Through Regularized Unbalanced Optimal Transport
  Z. Zhang, T. Li, P. Zhou. (2025).
  ICLR 2025 Oral (Top 1.8%)

• Reconstruct Growth and Dynamic Trajectories from Single-Cell Transcriptomics
  Yutong Sha, Yuchi Qiu, Peijie Zhou, and Qing Nie. (2024).
  Nature Machine Intelligence, 6, 25–39.

• On the Mathematics of RNA Velocity II: Algorithmic Aspects
  Li, T., Wang, Y., Yang, G., & Zhou, P. (2024).
  CSIAM Transactions on Applied Mathematics, 5, 182–220

• On the Mathematics of RNA Velocity I: Theoretical Analysis
  Li, T., Shi, J., Wu, Y., & Zhou, P. (2021).
  CSIAM Transactions on Applied Mathematics, 2(1), 1–55

• Dissecting Transition Cells in Single-Cell Transcriptome Data by Multi-Scale Reduction of Stochastic Dynamics
  Zhou, P., Wang, S., Li, T., & Nie, Q. (2021).
  Nature Communications, 12, 5609

• Inference and Multiscale Model of Epithelial-to-Mesenchymal Transition via Single-Cell Transcriptomic Data
  Sha, Y., Wang, S., Zhou, P. & Nie, Q. (2020).
  Nucleic Acids Research, 48(17), 9505–9520

• Construction of the Landscape for Multi-Stable Systems: Potential Landscape, Quasi-Potential, A-Type Integral and Beyond
  Zhou, P., & Li, T. (2016).
  The Journal of Chemical Physics, 144(9), 094109

Multi-Omics AI Models and Biomedical Applications

• A Perturbation Proteomics-Based Foundation Model for Virtual Cell Construction
  R. Sun, L. Qian, Y. Li, H. Cheng, Z. Xue, X. Zhang, L. Tan, Y. Zhan, W. Hu, P. Zhou, et al. (2025).
  bioRxiv, 2025.02.07.637070

• Genetic Deconvolution of Embryonic and Maternal Cell-Free DNA in Spent Culture Medium of Human Preimplantation Embryos Through Deep Learning
  Z. Zhang, J. Qiao, Y. Chen, P. Zhou. (2025), accepted by Advanced Science.

• Single-Cell Transcriptomics Reveals Aberrant Skin-Resident Cell Populations and Identifies Fibroblasts as a Determinant in Rosacea
  M. Chen, L. Yang, P. Zhou, et al. (2024).
  Nature Communications, 15(1), 8737

• The Multiple Activations in Budding Yeast S-Phase Checkpoint Are Poisson Processes
  Xin Gao, Peijie Zhou, and Fangting Li. (2023).
  PNAS Nexus, 2(11), pgad342.

• exFINDER: Identify External Communication Signals Using Single-Cell Transcriptomics Data
  Changhan He, Peijie Zhou, and Qing Nie. (2023).
  Nucleic Acids Research, 2023.

• Preneoplastic Stromal Cells Promote BRCA1-Mediated Breast Tumorigenesis
  Kevin Nee, Dennis Ma, Quy H. Nguyen, …, Peijie Zhou, Qing Nie, …, Kai Kessenbrock. (2023).
  Nature Genetics, 2023, 1–12.

• Stochasticity Triggers Activation of the S-Phase Checkpoint Pathway in Budding Yeast
  Zhou, P., Gao, X., Li, X., et al. (2021).
  Physical Review X, 11(1), 011004

• Single-Cell RNA-Seq Analysis Reveals the Acquisition of Cancer Stem Cell Traits and Increase of Cell–Cell Signaling During EMT Progression
  Bocci, F., Zhou, P., & Nie, Q. (2021).
  Cancers, 13(22), 5726

• DURIAN: An Integrative Deconvolution and Imputation Method for Robust Signaling Analysis of Single-Cell Transcriptomics Data
  Karikomi, M., Zhou, P., & Nie, Q. (2022).
  Briefings in Bioinformatics

• Integrating Single-Cell Datasets with Ambiguous Batch Information by Incorporating Molecular Network Features
  Dong, J., Zhou, P., Wu, Y., et al. (2021).
  Briefings in Bioinformatics

• Inference of Intercellular Communications and Multilayer Gene-Regulations of Epithelial–Mesenchymal Transition from Single-Cell Transcriptomic Data
  Sha, Y., Wang, S., Bocci, F., Zhou, P., & Nie, Q. (2021).
  Frontiers in Genetics, 11, 604585

Generative Models in AI for Science

• Using Deep Learning to Generate Key Variables in Global Climate Change Mitigation Scenarios
  Li, P., Zhu, R., McJeon, H., Byers, E., Zhou, P., & Ou, Y. (Accepted).
  Nature Climate Change.