Jaegil Kim

This paper clusters T2D genetic loci into ten groups using bNMF, identifying distinct biological mechanisms and clinical associations.

This study identifies molecular and immune features associated with response to anti-PD-(L)1 therapy in NSCLC using a cohort of 393 patients.

The paper develops DNA- and RNA-based models combining immune and tumor features to predict outcomes of checkpoint inhibition in melanoma.

This study identifies genetic loci associated with daytime napping and finds potential causal links between napping and cardiometabolic traits.

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.

ActivePathways integrates multivariate omics data to identify enriched pathways, revealing cancer driver genes and prognostic pathways in breast cancer.

This paper demonstrates that GPU-accelerated machine learning libraries significantly speed up and reduce the cost of computational genomics tasks like QTL mapping and Bayesian non-negative matrix factorization.

This paper shows that many cancer hotspots are passengers, not drivers, and introduces a new model to better identify true driver mutations.

This study analyzes RNA-seq data from 6,700 normal tissues to identify somatic mutations and their association with age and tissue proliferation, finding tissue-specific cancer mutation hotspots.

This study identifies 96 genome-wide significant loci associated with dietary intake variation using a multi-trait GWAS meta-analysis across 283,119 individuals.

PhylogicNDT statistically reconstructs cancer driver timing, subclonal phylogenies, and population dynamics from single- or multi-timepoint sequencing, revealing subtype-specific trajectories and therapy resistance evolution across 498 lung adenocarcinomas.

The paper predicts cancer cell-of-origin from DNA sequences by matching mutational landscapes to chromatin modification patterns in normal tissues.

This paper clusters T2D genetic loci using multi-trait associations to identify disease mechanisms and subtypes, finding distinct clinical outcomes linked to each cluster.

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.