James Stevenson

The paper introduces Cat-VRS, a framework for computable representation of genomic variant categories to improve data interoperability and clinical interpretation.

The paper introduces dgiLIT, an AI-driven method to prioritize and curate drug-gene interactions from biomedical literature for inclusion in DGIdb, achieving high validation rates.

The paper evaluates challenges in genomic variant representation across resources and develops normalization methods to improve clinical outcome associations.

The paper maps MAVE data to human reference sequences to improve variant functional assay utility for genomics applications.

The paper defines AI-readiness criteria for biomedical data, encompassing seven dimensions, and evaluates them across flagship datasets to ensure ethical and explainable AI practices.