Igor Jurisica, PhD, DSc is a Senior Scientist at Osteoarthritis Research
Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute and
Data Science Discovery Centre for Chronic Diseases, Krembil Research
Institute, Professor at University of Toronto and Visiting Scientist at
IBM CAS. Since 2021 he is a scientific director of the World Community
Grid.
His research focuses on integrative informatics and the representation,
analysis and visualization of high-dimensional data to identify
prognostic/predictive signatures, determine clinically relevant
combination therapies, and develop accurate models of drug mechanism of
action and disease-altered signaling cascades. He has published
extensively on data mining, visualization and integrative computational
biology, including multiple papers in Science, Nature, Nature Medicine,
Nature Methods, J Clinical Oncology, J Clinical Investigations. He has
been included in Thomson Reuters 2014, 2015 & 2016 lists of Highly Cited
Researchers (http://highlycited.com), and The World’s Most Influential
Scientific Minds: 2015 & 2014 Reports. In 2019, he has been included in
the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare list
(Deep Knowledge Analytics, http://analytics.dkv.global). In 2023,
he has been included in the Top 100 AI in
Oncology leaders: https://platform.dkv.global/map/reports/ai-in-oncology-leaders/
Day 1: Dec 5, 2023
Day 2: Dec 6, 2023
4:15 pm
CASE STUDY: MULTI-HOSPITAL FEDERATED LEARNING WITH REVENUE SHARING
From Siloed Data to Precision Insights: How a Multi-Omic Federated AI Platform Is Advancing Psoriatic Arthritis Care
Psoriatic Arthritis affects nearly 300,000 Canadians, yet treatment selection remains a trial-and-error process due to fragmented datasets, strict privacy constraints, and limited computational infrastructure. To address this, our team built a federated, multi-omic learning platform that brings models to the data—spanning genomics, proteomics, metabolomics, and clinical records across multiple hospitals. The approach preserves patient privacy, accelerates precision-medicine research, and introduces a revenue-sharing model that lets institutions participate directly in the value they help create.
Leave with a practical strategy to:
- Deploy federated learning across hospitals while keeping all patient data on-premise
- Combine multi-omic data streams to improve treatment-response prediction for complex diseases
- Leverage validated genomic markers (HLA-B27, HLA-C06, TNFAIP3, IL23R, TYK2) within scalable ML pipelines
- Achieve strong predictive performance using ensemble approaches, with retrospective AUCs up to 0.96
- Align hospitals, researchers, and industry partners using an equitable, revenue-sharing commercialization model
Fuel more efficient clinical research, stronger predictive models, and scalable precision-medicine programs through federated multi-omic AI.
