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YVES MOREAU, KU LEUVEN & IMEC
PROFESSOR


Yves Moreau is a Professor of Engineering at the University of Leuven, Belgium. His research focuses on developing computational methods for (1) governance of clinical genomic data, including variant discovery and privacy management, (2) data fusion of omics data for gene and variant prioritization in Mendelian and oligogenic disorders, and (3) data fusion for the prediction of drug-target interactions in drug discovery. Methodologically, he focuses on kernel methods, probabilistic graphical models, and deep learning for the fusion of heterogeneous omics data. More recently, he has been extending this work towards privacy-preserving data fusion algorithms.

 

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Presentation abstract

MINING A MILLION GENOMES:FEDERATED  ANALYTICS FOR THE DISCOVERY OF DISEASE-CAUSING GENOMIC VARIANTS

In 2000, the sequence of the human genome was completed after a three-billion-dollar investment and a decade of work. Today, we can sequence the genome of a patient for $1,000 in a day. As a result, clinics now massively adopt whole-genome sequencing as a routine clinical tool with applications across all fields of medicine. In the next decade, we will sequence the genomes of millions of patients, hereby piling up exabytes of data that need to be organized, analyzed, searched, and shared across the world. While one approach is to centralize such data as much as possible, this raises serious privacy and confidentiality issues. To alleviate those concerns, we developed NGS-Logistics, a platform for federated analysis of genomic data for the discovery of disease-causing variants. Identifying how often a given mutation is present in a population is a key step to establish whether this particular mutation causes a genetic disease. In our federated platform, the query is not run against a centralized database, but rather distributed to those centers that control the DNA samples. Each individual center processes personal data but only returns derived nonpersonal data to the central server for aggregation, hereby preserving the privacy of individual patients. 

 

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