Jing Su

The paper shows that NIH grant deadlines influence the timing of preprint publications, with a peak in submissions before deadlines.

SpaGene improves spatial transcriptomics by imputing missing gene expressions, achieving higher accuracy than existing methods and revealing immune dynamics in lung tumors.

This study identifies risk factors and patterns of immune-related adverse events (irAEs) in patients receiving immune checkpoint inhibitors using real-world data.

SpaIM improves spatial transcriptomics by imputing missing gene expressions using scRNA-seq data, outperforming 12 existing methods across 53 datasets.

xSiGra is a graph-based AI model that identifies interpretable features in spatial single-cell data, showing that cell activity is influenced by neighboring cells and revealing endothelial-tumor interactions.

SiGra integrates spatial images and transcriptomics data to enhance spatial domain detection and improve characterization of cellular heterogeneity and communication in tissues.

SpaRx identifies spatial cellular heterogeneity in drug responses across tumor lesions and reveals mechanisms of drug resistance through a graph-based domain adaptation model.

scGCN transfers labels across single-cell omics datasets with superior accuracy by using a graph-based AI model.

scLM detects consensus gene clusters across multiple single-cell datasets, improving accuracy over existing methods and enabling novel comparative analysis.

DSTG deconvolutes spatial transcriptomics data to reveal cell compositions and spatial architecture of cellular heterogeneity in tissues.

This study uses EMRs and GWAS data to map CKD progression trajectories in African Americans with type 2 diabetes, identifying high-risk and low-risk pathways linked to APOL1 genotypes.