Yixin Wang

DANCE 2.0 automates and interprets single-cell preprocessing by recommending optimal pipelines for various tasks, improving performance across six major analyses.

This paper introduces a conditional diffusion model to denoise 7T MRI scans, improving image quality and outperforming existing methods.

This paper introduces Tabula, a privacy-preserving foundation model for single-cell transcriptomics using federated learning and tabular modeling, achieving better performance with less data and revealing regulatory logic across biological systems.

This paper develops a pipeline for precise MRI-histology coregistration using blockface imaging, achieving high alignment accuracy for neurodegenerative disease research.

DANCE provides a benchmark platform for single-cell analysis with 32 methods across 21 datasets, enabling easy reproduction and development of deep learning models.

The paper introduces a GAN-based method to generate high-quality membranes for cells with poor staining in fluorescent microscopy images, improving segmentation accuracy.

The paper introduces a large-scale spatial transcriptomic dataset and a Graph Neural Network-based method for cell type deconvolution in human immuno-oncology.