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Research > Researchers
Dr. Jüri Reimand
Dr. Jüri Reimand
Contact
Dr. Jüri Reimand
Mid-Career Investigator

juri.reimand@oicr.on.ca

Mid-Career Investigator

Current cancer research routinely generates large-scale datasets, however understanding the data remains a great challenge. Dr. Reimand’s lab develops and applies computational and machine learning methods to analyze molecular pan-cancer datasets and interpret these in the context of biology and patient information. The major areas of research include driver and passenger mutations of the cancer genome, multi-omics data integration using biological pathways and molecular interaction networks, and discovery of innovative molecular biomarkers.

External Links
Experience & Education
  • Investigator I, OICR
  • Postdoctoral Fellow, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto
  • Marie Curie Pre-doctoral Research Fellow, EMBL, European Bioinformatics Institute
  • PhD, Computer Science, University of Tartu, Estonia
  • M.Sc., Computer Science, University of Tartu, Estonia
  • Postdoctoral fellow, Donnelly centre, University of Toronto
  • PhD, computer science, University of Tartu
Current Affiliations
  • Mid-Career Investigator, OICR
  • Assistant Professor, Department of Medical Biophysics, University of Toronto
Select Publications

See Dr. Reimand’s recent publications on PubMed or on Google Scholar.

Research Areas
Biology Informatics and Data Standards
Disease Areas
All
Opportunities to Collaborate
Collaborate with us
  • Pathway and network analysis
  • Cancer driver discovery
  • Mutation analysis

Visit OICR’s Collaborative Research Resources directory for more opportunities to collaborate with OICR researchers.

Programs
Computational Biology
Utilizes datasets from multiple sources for the development and application of algorithms for cancer genome analysis, biomarker discovery, cancer pathway discovery, data integration and tumour evolution research.
Learn More
Tools & Software
  • g:Profiler -a web server for functional interpretation of gene lists
  • ActiveDriver – a computational method for identifying ‘active’ sites in proteins
In the News
Unravelling the secrets of the non-coding cancer genome
Unravelling the secrets of the non-coding cancer genome
Jun 11, 2021
 New prognostic biomarkers and cancer-driving genes found in the dark matter of the cancer genome
New prognostic biomarkers and cancer-driving genes found in the dark matter of the cancer genome
Nov 15, 2021
 Study shows that smoking ‘stops’ cancer-fighting proteins, causing cancer and making it harder to treat
Study shows that smoking ‘stops’ cancer-fighting proteins, causing cancer and making it harder to treat
Nov 03, 2023
 AI helps find new leads for brain cancer drug discovery
AI helps find new leads for brain cancer drug discovery
Jan 30, 2024