The Graim lab develops computational tools that integrate large-scale genomic data to identify mechanisms driving human disease. We develop strategies for studying molecular processes that are often disguised by noisy data and low sampling rates. Our research facilitates learning the biological basis of these deadly diseases and, ultimately, deduces novel treatments and identifies early-stage risk factors to allow for early treatment of invasive human diseases.
We are studying the evolution of cancer across mammalia, designing machine learning models that integrate data from ~250 species to understand how cancer has evolved. Our comparative oncology approach provides a unique opportunity to learn the evolution of biological mechanisms driving cancer and other human diseases.
Knowledge gained through application of our tools identifies insights to develop precision medicine therapies.