Thesis Defense: Biomedical Data Sharing and Analysis at Scale
, Berger Lab
Date: Wednesday, April 24, 2019
Time: 9:45 AM to 10:45 AM
Event Type: Thesis Defense
Host: Bonnie Berger, Vinod Vaikuntanathan and Devavrat Shah
Contact: Patrice Macaluso, 617-253-3037, email@example.com
Speaker URL: None
TALK: Biomedical Data Sharing and Analysis at Scale
Abstract: Researchers around the globe are gathering biomedical information at a massive scale. However, growing privacy concerns and computational overhead limit researchers' access to these data. In this talk, I will present novel computational methods that help overcome these barriers to improve the scalability of essential biomedical analysis pipelines. First, I will describe how modern cryptographic tools present a path toward broader data sharing and collaboration in biomedicine as demonstrated by my recent work on secure genome-wide association studies (GWAS) and pharmacological machine learning. For each domain, I will introduce our efficient privacy-preserving analysis protocol to achieve state-of-the-art accuracy while ensuring the input data remain private throughout the protocol. Our protocols newly achieve scalability to a million genomes or drug compounds by drawing on a set of techniques aimed at reducing redundancy in computation. Second, I will describe how the increasing redundancy in and structure of single-cell transcriptomic data can be leveraged to build a compact geometric sketch of the data, which can be used to accelerate and enhance the utility of downstream analysis. Our approach facilitates the sharing and analysis of emerging large-scale single-cell omics datasets including millions of cells. These results lay a foundation for more effective and collaborative biomedical research where individuals and institutes across nations share their data to enable novel life-saving discoveries.
Created by Patrice Macaluso at Monday, April 22, 2019 at 3:23 PM.