Kyoung‐Jae Won

DeepMAST, an AI system for spatial transcriptomics, identifies ICAM-1 as a therapeutic target in lung fibrosis through evidence-based analysis and validation.

The paper evaluates the reliability of confidence measures from single-cell models on new data and shows that raw confidence scores are unreliable, requiring recalibration with target-specific labels.

CellNeighborEX v2 identifies context-specific cell-cell interaction genes from spatial transcriptomics data without relying on ligand-receptor databases.

This study identifies Fusobacterium nucleatum-produced indole metabolites that activate the AHR-TERT axis, promoting CRC cell proliferation and revealing FTIN as a potential biomarker.

SC2Spa maps single-cell RNA-seq data to spatial coordinates using deep learning, improving resolution for low-resolution spatial transcriptomics data and identifying spatially variable genes.

This paper introduces an interpretable cell mapping strategy to align and compare heterogeneous spatial data across different scales and contexts, showing spatiotemporal decoupling during development and identifying subpopulations in cancer patient data.

CellNeighborEX identifies gene expression influenced by neighboring cells in spatial transcriptomics data, revealing new insights into cell-cell interactions in mouse embryos, brain, and liver cancer.

The paper introduces PIC-seq to study cell-cell contact-specific gene expression during mouse embryonic development and identifies novel gene expression signatures dependent on neighboring cell types.

scmTE infers gene regulatory networks from scRNA-seq data by reducing false predictions, identifying known and novel regulatory interactions in biological processes.

Vesalius identifies tissue structures and cell-type-specific gene expression in spatial transcriptomics data by applying image analysis techniques.

The paper shows that decreased KATP channel surface density in early postnatal rat beta-cells lowers the glucose threshold for insulin secretion, which normalizes by postnatal day 14.

This study shows that early postnatal activation of the HIF1A pathway in rats causes hyperinsulinism and reduced β-cell proliferation, potentially increasing diabetes risk.

VeTra infers cell trajectories using RNA velocity and identifies key regulators by grouping cells based on transition direction.

The paper identifies a core set of developmentally and evolutionarily conserved PDX1-bound genes in mouse and human pancreas development, showing stable PDX1 binding across ages with context-dependent regulatory roles.

TENET reconstructs gene regulatory networks from scRNA-seq data using transfer entropy, identifying key regulators with better performance than existing methods.

SHARP processes large-scale scRNA-seq data efficiently, outperforming existing methods in speed and accuracy for datasets over 40,000 cells.

The paper identifies Shh responsive genes and regulatory sequences involved in cochlear morphogenesis using genomic approaches.

The paper identifies how flanking nucleotides influence CEBP binding and transcriptional activity, revealing sequence biases and heterodimerization effects in mammalian bZip TFs.