Chaozhong Liu

This paper introduces modality-chain reasoning for multimodal protein modeling and design, showing improved performance in structure prediction, inverse folding, and fitness prediction compared to existing models.

The paper investigates how exercise affects Alzheimer's disease in mice by analyzing gene expression and chromatin changes, finding that growth factor signaling pathways are key to exercise's benefits.

SEAGAL is a Python package that detects and visualizes spatial gene correlations in spatial transcriptomics data using the L-index method.

scGREAT is a tool for analyzing regulatory elements in single-cell multi-omics data, enabling heterogeneity analysis and regulatory state inference.

This paper introduces MinNet, a Siamese network with graph-based contrastive loss for integrating single-cell multi-omics data, showing its effectiveness in removing batch effects and improving downstream analysis.

This paper introduces ReSort, a method to improve cell type deconvolution in spatial transcriptomics by reducing batch effects, and shows it enhances accuracy in mouse breast cancer data.

The paper identifies a latent factor in brain RNA-seq data that reflects cell type and clinical heterogeneity, improving the identification of biological pathways and biomarkers.