Hua Qin

DCAF improves single-cell perturbation simulation by addressing data, evaluation, and model validity, outperforming baselines in generalization and recapitulating regulatory heterogeneity in mouse brain.

RegFormer improves single-cell RNA sequencing analysis by integrating gene regulatory networks with Mamba-based modeling, outperforming existing models in clustering, integration, and annotation.

BiOmics introduces an AI agent that improves multi-omics interpretation by combining reasoning and embedding spaces, outperforming existing methods in variant discovery, cell annotation, and drug repurposing.

DynPerturb models spatiotemporal perturbation dynamics in single-cell and spatial transcriptomics, outperforming existing methods in regulatory link prediction and uncovering dynamic regulatory rewiring in biological systems.

BioLLM integrates and benchmarks single-cell foundation models, revealing scGPTs' robust performance and highlighting limitations of scBERT in gene-level tasks.

UniCell improves cell type annotation and cross-dataset harmonization by combining Cell Ontology with transcriptomic data, outperforming existing tools in accuracy and rare cell detection.

The paper updates a software tool to generate single-cell spatial gene expression profiles using cell boundary images from Stereo-seq data, showing improved performance on mouse liver and Arabidopsis seed datasets.