Jeongbin Park

This paper develops SIRPα-expressing extracellular vesicles from engineered stem cells to treat acute liver failure by resolving necroptosis and promoting liver regeneration, showing reduced liver damage and improved survival in models.

IAMSAM is a web-based tool for analyzing spatial transcriptomics data, enabling tissue segmentation and downstream analysis like differential expression and cell type prediction.

The paper analyzes unmapped RNA-seq data from cancer spatial transcriptomes to study microbial RNA distribution and function in cancer tissues.

CELLama transforms single-cell and spatial transcriptomics data into embeddings using language model abilities, enabling flexible analysis without manual reference data.

SpatialSPM compares gene expression patterns across spatial transcriptomic datasets by aligning and analyzing multi-dimensional image matrices to identify differentially expressed regions.

This paper introduces SuperST, a deep learning method that reconstructs high-resolution spatial gene expression images from low-resolution barcode-based spatial transcriptomics data, improving alignment with immunofluorescence images.

The paper identifies molecular markers associated with nanomedicine distribution in tumor tissue using spatial transcriptomics and fluorescence imaging.

The paper identifies potential imaging and therapeutic targets for severe COVID-19 inflammation by analyzing single-cell RNA-seq data from patient samples.