Madison Darmofal

Generalist large language models complement tailor-made predictors for tumor genomics interpretation

This paper evaluates generalist large language models (LLMs) in tumor genomic interpretation tasks, finding they can complement or outperform task-specific models in some cases.

Integrated histopathologic modeling of detailed tumor subtypes and actionable biomarkers

The paper develops Mosaic, a machine learning system that classifies tumors into detailed subtypes and identifies molecular phenotypes using histopathologic and genomic data from a large patient cohort.

Novel patterns of complex structural variation revealed across thousands of cancer genome graphs

The paper identifies three novel complex structural variation patterns in cancer genomes using genome graph analysis across 2,833 tumor WGS.