Molecular phenotypes, or subtypes, can describe cancer as distinct diseases within primary tissues-of-origin. Machine learning (ML) can be applied to this molecular taxon [...]

Cancers are among the most deadly and intractable of human diseases, for which the standard of care may involve aggressive yet only partially effective therapies. Immunot [...]

This dissertation develops machine learning methods to address key barriers in precision oncology, focusing on spatial biology, multimodal data integration, and clinical [...]

This dissertation quantitatively assesses the single-cell spatial landscape of the tumor microenvironment for three cancer types. Statistical and machine learning approac [...]