Artem Kim

This paper introduces CT-FM and CT-FM-SNP methods to identify causal cell types and variants for diseases, finding most SNPs affect traits via a single cell type set.

The paper introduces a machine-learning framework to model disease effect sizes from GWAS data, identifying constrained regulatory variants as key functional components and improving variant prioritization.

jaxQTL improves sc-eQTL mapping by using count-based models, identifying more eGenes and explaining more SNP-heritability than bulk-eQTLs.

The paper introduces SuShiE, a model that improves multi-ancestry fine-mapping of cis-molQTLs, identifying more genes and prioritizing functionally enriched variants.

FactorGo identifies pleiotropic components across thousands of human diseases and traits using GWAS summary statistics, outperforming existing methods in capturing latent factors.

This study identifies novel HPE genes and oligogenic inheritance patterns in holoprosencephaly by integrating clinical data with exome sequencing and gene prioritization.