Jüri Reimand

Our research involves computational biology and cancer research. We analyze multi-omics datasets and develop statistical and machine-learning tools to enable these analyses. We believe that we can learn about cancer biology, its vulnerabilities and novel biomarkers by focusing on understudied intersections of datasets of different layers of the central dogma (i.e., genomics, transcriptomics, proteomics, epigenomics). Our recent projects are in three areas: drivers and passengers of the cancer genome, multi-omics data integration through pathways and networks, and cancer biomarker discovery. We often perform pan-cancer analyses to learn common oncogenic drivers and pathways in well-powered datasets and also focus on particular cancer types such as brain, prostate, and liver cancer.

Cross-affiliations

• Ontario Institute for Cancer Research