Youngil Koh

The paper analyzes sequencing data from gastric MALT lymphoma patients to identify genetic mutations, gene fusions, and microbiome changes associated with H. pylori infection and disease progression.

The paper develops a computational workflow to predict metabolite-gene-pathway associations in cancers using genome-scale metabolic models and mutation data, showing significant associations with specific somatic mutations.

The paper identifies new cancer predisposing genes by analyzing rare germline variants in Mendelian disease genes across cancer cohorts.

The paper predicts BH3 mimetic efficacy by analyzing PPI complexes in AML, identifying BCL2-BAX and BCLxL-BAK as key mediators of drug response and resistance.

ETCHING is an ultra-fast somatic structural variation caller that reduces mapping costs and false positives using pan-genome k-mer sets and a Random Forest classifier.

The paper identifies dsRNA-binding proteins that mediate decitabine's effects by stabilizing ERV RNAs, showing low Stau1 and TINCR expression correlates with poor treatment outcomes in AML patients.

The paper analyzes whole-genome sequencing data from cancer samples to identify genomic footprints of telomere maintenance mechanisms, finding specific telomere variant repeat patterns associated with ATRX/DAXX mutations.

The paper examines how somatic structural variations affect TAD boundaries and gene regulation in human cancers using whole genome sequencing data.

This study analyzes chromothripsis in 2,658 cancers using whole-genome sequencing, finding it prevalent across multiple cancer types with complex structural alterations.

This study identifies germline mutations in lysosomal storage disease genes as risk factors for cancer, showing increased cancer risk and earlier onset in mutation carriers.

The paper analyzes immuno-genomic data across 31 cancer types to identify immune escape mechanisms and immuno-editing histories.