In collaboration with QIMR Berghofer and the Riken Center for Integrative Medical Sciences, we used federated methods to analyze genomic data from the BioBank Japan in situ to classify variants of uncertain significance while preserving privacy. With the Department of Laboratory Medicine and Pathology at the University of Washington, we developed a statistical model that demonstrates using responsibly shared clinical evidence alone can classify variants of uncertain significance which occur at the rate of 1 in 100,000 people within just a few years. With researchers from McGill University, we reviewed the state of the art in federated computing technologies and how well they satisfy the privacy restrictions from the General Data Protection Regulation. With researchers from NASA, Amazon, and Intel, we developed a federated learning framework to run between terrestrial and space-borne compute infrastructure, laying the groundwork for subsequent experiments, which preclude the need to transfer large datasets across astronomical distances. Finally, at NASA, we used a causal inference machine learning ensemble to infer robust correlation between mouse liver gene expression and a corresponding lipid density phenotype in space-flown mice.

Event Host: James Casaletto, Ph.D. Candidate, Biomolecular Engineering & Bioinformatics

Advisor: Benedict Paten

Join us in person or on Zoom: https://ucsc.zoom.us/j/91562764120?pwd=RTl3V0ZPVndPeDNxcG1WQW1iUnI4QT09
Passcode: 190302