Min‐Sik Kim

The paper uses an AI-driven framework to model perturbations in brain organoids and identifies candidate genes for autism using genomic and clinical data.

This study evaluates the quality and suitability of long-term stored tissues for multi-omics analyses, showing that while transcriptomic data show some changes, core gene expression and proteomic measurements remain reliable.

This study identifies RBP4 as an antagonistic ligand of DR6, showing it prevents DR6-driven neurodegeneration by promoting dimerization and blocking prodegenerative interactions.

The paper analyzes time-resolved transcriptome and proteome data of Xanthomonas oryzae pv. oryzae to reveal differences in transcriptional and translational expression peaks during early pathogenesis.

The paper develops a mathematical model combining mRNA splice variants and time-delay to predict protein abundance from RNA-Seq, improving biomarker discovery in immune diseases.

The paper establishes the landscape of human cell-specific microRNA expression using small RNA-seq data from diverse cell types and tissues, identifying novel microRNA loci and patterns correlated with super-enhancer activity.