Igor Jurisica, PhD, DSc

Senior Scientist, Krembil Research Institute (Krembil)

Keywords: integrative computational biology, precision medicine, drug:target prediction and analysis, microRNA:target analysis, protein crystallography, data mining, machine learning, prognostic factors, biomarkers, protein-protein interactions

Dr. Igor Jurisica is a Senior Scientist at Krembil Research Institute, Professor at University of Toronto and Visiting Scientist at IBM CAS. He is also an Adjunct Professor at the School of Computing, Pathology and Molecular Medicine at Queen's University, Computer Science at York University, an adjunct scientist at the Institute of Neuroimmunology, Slovak Academy of Sciences and an Honorary Professor at Shanghai Jiao Tong University. Since 2015, he has also served as Chief Scientist at the Creative Destruction Lab, Rotman School of Management.

He has published extensively on data mining, visualization and cancer informatics, including multiple papers in Science, Nature, Nature Medicine, Nature Methods, J Clinical Investigations, J Clinical Oncology, and has over 12,299 since 2014, including 848 highly influential citations (SemanticScholar), and 13,795 lifetime citations (WOS) with h-index 52.

Dr. Jurisica has won numerous awards, including a Tier I Canada Research Chair in Integrative Cancer Informatics, the IBM Faculty Partnership Award (3-time recipient), and IBM Shared University Research Award (4-time recipient). He has been included in Thomson Reuters 2016, 2015 & 2014 list of Highly Cited Researchers, and The World's Most Influential Scientific Minds: 2015 & 2014 Reports. In 2019 he was included in the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare list (Deep Knowledge Analytics).

The main theme of my research is to develop and apply integrative computational tools across major high-throughput data in multigenic diseases in order to identify prognostic/predictive signatures, find clinically relevant combination therapies, and develop accurate models of disease-altered signaling cascades and drug mechanism of action. My team has built and continues to expand powerful computational infrastructure, unique programs and data integration portals for physical protein interactions, pathways, miRNAs, TFs, prognostic signatures, drug predictions, and bio-assay data analysis. We apply it to mostly arthritis, brain, and cancer research.

Intelligent molecular medicine
Technologies to measure gene, protein and microRNA activity offer the opportunity to improve our understanding of tumourigenesis and patient treatment. However, molecular profiling alone is not sufficient to achieve intelligent molecular medicine. Computational advances and computing power to analyze, manage and use genomic/proteomic information combined with information about drugs, their targets and mode of action are required to turn data into knowledge for hypotheses generation for further research or to render them readily comprehensible for patient outcome prediction and treatment selection. Our focus is on algorithm and tools development, their application and evaluation.
Many techniques for the analysis of genomic/proteomic data are available, yet none offers an integrated and comprehensive approach, by combining results from gene/protein expression data in the context of protein-protein interactions. We address this bottleneck in multiple cancers by systematic, unbiased analysis and visualization of data integrated from multiple high-throughput platforms under the hypothesis that such information will create insight not appreciable from the component parts.
The results of this research will help to fathom biological mechanisms of cancer, and will be applicable to improve disease classification, diagnostic measures, therapy planning and treatment prognosis. Improving the treatment could in turn improve quality of life for cancer patients.
Using the proposed tools and methodology, physicians will have more relevant information available at the time of diagnosis and treatment planning, and the patient will have a better explanation of the disease, its origin, progression path and treatment alternatives.
Structure-function relationship in protein interaction networks
It has been established that despite inherent noise present in protein-protein interaction (PPI) data sets, systematic analysis of resulting networks uncovers biologically relevant information, such as lethality, functional organization, hierarchical structure and network-building motifs. These results suggest that PPI networks have strong structure-function relationships. We are developing novel graph theory based algorithms for systematic analysis of PPI networks (both predicted and experimentally determined). We use this information to build predictive models and to integrate this information with gene/protein expression profiles.

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Igor Jurisica, PhD, DSc

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