Wenjun Zhao

The paper introduces a method to quantify biological shape heterogeneity using a stratified Wasserstein kernel, demonstrating its application to 2D and 3D biological datasets.

The paper introduces MorphCurveVAE, a method to infer continuous morphological trajectories from static 3D microscopy images, demonstrating its effectiveness on mitosis data.

The paper introduces ctOTVelo, a method for inferring cell type-specific gene regulatory networks from single-cell transcriptomics data, achieving state-of-the-art performance.

CycleGRN infers gene regulatory networks from single-cell RNA-seq data by modeling cell cycle dynamics as a stochastic differential equation, achieving strong performance on synthetic and biological datasets.

The paper improves gene regulatory network inference by integrating cell cycle positions into oscillatory network analysis, showing better performance for early progenitor cells.

OTVelo estimates gene velocities using optimal transport to infer dynamic gene regulatory networks from time-stamped single-cell gene expression data.