Grant Goldman

The paper generates functional genome editors by combining deep mutational scanning, structural analysis, and evolutionary mining with a generative protein language model.

The paper uses a protein language model to analyze amino acid deletions in the human proteome, identifying sites with differential tolerance to deletions versus substitutions.

This paper uses a deep protein language model to predict the functional impact of all human missense variants, outperforming existing methods and providing isoform-specific annotations.

The paper constructs polygenic indexes for 47 phenotypes across 11 datasets and provides a tool to correct errors-in-variables bias when using PGIs as proxies for the additive SNP factor.

This paper introduces MAMA, a method for multi-ancestry GWAS meta-analysis, which identifies novel genetic associations and improves power across populations.