Ge Yang

This paper introduces a Transformer-based tracker for accurate particle and cell tracking in microscopy, outperforming existing methods in accuracy and robustness.

SCARF is a foundation model for integrating single-cell ATAC-seq and RNA-seq data, achieving state-of-the-art performance in cell representation and cross-omics tasks.

scCompass provides a cross-species scRNA-seq database with standardized preprocessing, enabling AI-ready data for research and analysis.

DeepETPicker improves 3D particle picking in cryo-electron tomography with weakly supervised deep learning, achieving higher accuracy and speed than existing methods.

The paper introduces IsoVEM, a transformer-based algorithm for isotropic reconstruction in volume electron microscopy, improving axial resolution and repairing damaged sections.

CellPolaris uses transfer learning to generate cell-type-specific gene regulatory networks from transcriptomic data, achieving state-of-the-art results in predicting regulatory factors and simulating cell fate transitions.

This paper introduces a Transformer-based method for single particle tracking in fluorescence microscopy images, showing improved performance over existing methods.

The paper uses spatial statistical analysis to show lysosomes have stable, distinct distributions in cells and form dynamic clusters that increase interactions with endosomes.