Jeongbin Park

This paper introduces SSAF-T, a segmentation-free framework for cell-type inference in spatial transcriptomics that avoids cell segmentation and enables accurate cell-type-guided segmentation.

The paper introduces Autopipe, a platform that enables reproducible and shareable bioinformatics pipelines generated from natural-language prompts using LLMs.

BOMS improves cell segmentation in spatial transcriptomics by using mRNA spot locations and gene labels without auxiliary stains, achieving comparable performance to existing methods with faster execution.

The paper investigates the role of EZH2 in Duchenne muscular dystrophy and shows that inhibiting EZH2 improves muscle phenotype in Dmd mutant mice when combined with glucocorticoids.

CWAS-Plus identifies noncoding variant associations in whole-genome sequencing data using cell-type-specific functional data, finding links to autism and Alzheimer's disease.

READRetro is a bio-retrosynthesis tool that improves pathway prediction for natural products by balancing generalizability and memorability, outperforming existing models.

This study evaluates six algorithms for cell type matching in spatial transcriptomics data and presents consensus strategies for improved analysis.

This paper introduces SSAM, a cell segmentation-free method for cell-type identification and tissue domain detection in in situ transcriptomics data, showing it detects known and discovers new cell types in mouse brain tissues.

The paper introduces two web tools, BE-Designer and BE-Analyzer, for designing CRISPR base editor targets and analyzing next-generation sequencing data.

The paper introduces pheno-seq, a method that links morphological features to gene expression in 3D cell cultures, showing its ability to identify morphology-specific genes and regulatory states not captured by scRNA-seq.

HTSlib-js provides a JavaScript interface for processing BAM and VCF files in web browsers, enabling fast and interactive analysis of high-throughput sequencing data.

The paper questions the claim that unexpected SNVs and indels in CRISPR-treated mice are caused by CRISPR-Cas9, highlighting the lack of validation for these off-target effects.

The paper introduces a method to disentangle multi-omics data and identifies regulatory mechanisms in human blood cells.