Karl Pichotta

The paper presents a pan-cancer ex vivo drug screen atlas with 2.1M experiments, identifying tissue-specific drug sensitivities and demonstrating their predictive value for clinical profiles.

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.

The paper uses machine learning to identify cancer driver mutations, finding that methods incorporating protein structure or functional genomic data outperform those using only evolutionary data, and validates their predictions using real-world patient cohorts.