MIDAS Seminar Series, MiCHAMP, and Precision Health Co-Present: Casey Greene, School of Medicine, University of Pennsylvania

Casey Greene

Professor, Biochemistry and Molecular Genetics, University of Colorado

Founding Director, Center for Health AI, University of Colorado School of Medicine

Director, Childhood Cancer Data Lab

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Machine learning can help to realize the bounty of the commons

Abstract:

Biomedical research disciplines are awash in data. These data, generated by new technologies as well as old approaches, provide the opportunity to systematically extract biological patterns that were previously difficult to observe. I’ll share vignettes focusing on three areas: 1) how we can use large-scale public data to better understand data for which few observations are available; 2) some work to understand why large-scale integrative analyses are beneficial; and 3) how machine learning can help to produce more datasets suitable for integration while maintaining participant privacy.

Dr. Casey Greene is an Associate Professor of Systems Pharmacology and Translational Therapeutics in the Perelman School of Medicine at the University of Pennsylvania and the Director of the Childhood Cancer Data Lab, powered by Alex’s Lemonade Stand Foundation. His lab develops machine learning methods that integrate distinct large-scale datasets to extract the rich and intrinsic information embedded in such integrated data. This approach reveals underlying principles of genetics, cellular environments, and cellular responses to that environment. Casey’s devotion to the analysis of publicly available data doesn’t stop in the lab. In 2016, Casey established the “Research Parasite Awards” after an editorial in the New England Journal of Medicine deemed scientists who analyze other scientists’ data “research parasites.” These honors, accompanied by a cash prize, are awarded to scientists who rigorously reanalyze other people’s data to learn something new.