Hongzhi Wen

Longitudinal modality prediction learns gene regulatory patterns: insights from a single-cell competition

The paper presents a longitudinal multimodal benchmarking dataset and competition to evaluate modality prediction methods for gene regulatory inference, showing top models outperform existing approaches.

MARVEL: Microenvironment Annotation by Supervised Graph Contrastive Learning

MARVEL improves microenvironment annotation by using supervised graph contrastive learning to embed spatial cell relationships into a continuous space, outperforming existing methods in three tasks.

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.

A General Single-Cell Analysis Framework via Conditional Diffusion Generative Models

This paper introduces a general single-cell analysis framework using conditional diffusion models, showing competitive performance across tasks with added flexibility via prior information.

CellPLM: Pre-training of Cell Language Model Beyond Single Cells

Treating cells as tokens and tissues as sentences, with spatial transcriptomics added during pre-training, gives a single-cell foundation model the authors report outperforms existing models and runs far faster.

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.