9 citations · 12 across the 8 of their papers we have counts for
11 papers
TracingFlow: A Simulation-Free Trajectory Inference Framework Based on Second-Order Dynamics
Yuhao Sun, Zekun Wu, Zixun Huang +1
Inferring continuous system evolution from sparse temporal snapshots is a key challenge in generative modeling and single-cell omics. While Optimal Transport (OT) is popular, exist…
Beyond Continuity: Simulation-free Reconstruction of Discrete Branching Dynamics from Single-cell Snapshots
Junda Ying, Yuxuan Wang, Bowen Yang +2
Inferring cellular trajectories from destructive snapshots is complicated by the challenges of stochasticity and non-conservative mass dynamics such as cell proliferation and apopt…
Multiscale Supervised Unbalanced Optimal Transport Flow Matching
Qiangwei Peng, Lezhi Chen, Peijie Zhou
Unbalanced optimal transport (UOT) provides a principled framework for modeling single-cell transitions and birth-death dynamics, but its high computational cost limits scalability…
WFR-MFM: One-Step Inference for Dynamic Unbalanced Optimal Transport
Xinyu Wang, Ruoyu Wang, Qiangwei Peng +2
Reconstructing dynamical evolution from limited observations is a fundamental challenge in single-cell biology, where dynamic unbalanced optimal transport provides a principled fra…
WFR-FM: Simulation-Free Dynamic Unbalanced Optimal Transport
Qiangwei Peng, Zihan Wang, Junda Ying +5
The Wasserstein-Fisher-Rao (WFR) metric extends dynamic optimal transport (OT) by coupling displacement with change of mass, providing a principled geometry for modeling unbalanced…
CellStream: Dynamical Optimal Transport Informed Embeddings for Reconstructing Cellular Trajectories from Snapshots Data
Yue Ling, Peiqi Zhang, Zhenyi Zhang +1
Single-cell RNA sequencing (scRNA-seq), especially temporally resolved datasets, enables genome-wide profiling of gene expression dynamics at single-cell resolution across discrete…
Joint Velocity-Growth Flow Matching for Single-Cell Dynamics Modeling
Dongyi Wang, Yuanwei Jiang, Zhenyi Zhang +3
Learning the underlying dynamics of single cells from snapshot data has gained increasing attention in scientific and machine learning research. The destructive measurement techniq…
Variational Regularized Unbalanced Optimal Transport: Single Network, Least Action
Yuhao Sun, Zhenyi Zhang, Zihan Wang +2
Recovering the dynamics from a few snapshots of a high-dimensional system is a challenging task in statistical physics and machine learning, with important applications in computat…
Modeling Cell Dynamics and Interactions with Unbalanced Mean Field Schrödinger Bridge
Zhenyi Zhang, Zihan Wang, Yuhao Sun +2
Modeling the dynamics from sparsely time-resolved snapshot data is crucial for understanding complex cellular processes and behavior. Existing methods leverage optimal transport, S…
Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-seq Data Analysis
Zhenyi Zhang, Yuhao Sun, Qiangwei Peng +2
Understanding the dynamic nature of biological systems is fundamental to deciphering cellular behavior, developmental processes, and disease progression. Single-cell RNA sequencing…
Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport
Zhenyi Zhang, Tiejun Li, Peijie Zhou
Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning. Here, we introduce a new deep lea…