Yixin Wang

DANCE 2.0: Transforming single-cell analysis from black box to transparent workflow

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

Denoising 7T Structural MRI with Conditional Generative Diffusion Models

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

Toward a privacy-preserving predictive foundation model of single-cell transcriptomics with federated learning and tabular modeling

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.

Precise MRI-Histology Coregistration of Paraffin-Embedded Tissue with Blockface Imaging

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

DANCE: A Deep Learning Library and Benchmark Platform for Single-Cell Analysis

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

MEM-GAN: A Pseudo Membrane Generator for Single-cell Imaging in Fluorescent Microscopy

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

SpatialCTD: a large-scale TME spatial transcriptomic dataset to evaluate cell type deconvolution for immuno-oncology

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