Welcome to the Bioinformatics and Computational Genomics Laboratory of
The Bioinformatics and Computational Genomics Laboratory is part of the Princess Margaret Cancer Centre – University Health Network, located in the heart of the Toronto Discovery District. Our research focuses on the development of novel computational approaches to best characterize carcinogenesis and drug mechanisms of action from high-throughput genomic data. We have strong expertise in prognostic and predictive biomarkers identification and drug repurposing.
The Princess Margaret Cancer Centre is a teaching hospital within the University Health Network and affiliated with the University of Toronto. It has the largest cancer research program in Canada. This rich working environment provides ample opportunities for collaboration and scientific exchange with a large community of clinical, genomics, computational biology, and machine learning groups at the University of Toronto and associated institutions, such as The Hospital for Sick Children and the Donnelly Centre.
- Ali Madani presents a short talk titled “Pathway-based optimization of drug development for cancer” in Personalizing Cancer Medicine in 2017 Conference: Harnessing the Cross Disciplinary Approach
- Ali Madani gives a talk entitled “increasing vulnerability of triple negative breast cancer to the immune system using epigenetic therapy” at the 16th Annual BCI-McGill Workshop: Cancer and the immune system in the Barbados.
- Vandana will leverage her deep expertise in pancreatic cancer to contribute to the OICR/UHN PancuRx project. Welcome Vandana!
- Vandanu wins the Princess Margaret Postdoctoral Fellow Excellence Award
More stories can be read on the News page.
There are currently positions available in the lab for Software Developers, Postdoctoral Fellows, Graduate and Undergraduate Students in Cancer Computational Biology. Our research focuses on the development of novel computational approaches to best characterize carcinogenesis, drug mechanisms of action and therapeutic potential from high-throughput genomic data. Learn more on the Positions page.