Il Bin 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.

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.

The paper analyzes low-level somatic mutations across multiple organs and developmental stages, finding stage- and tissue-specific mutational features.

The paper identifies noncoding de novo mutations contributing to autism spectrum disorder by altering chromatin interactions and gene expression in neural stem cells.