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.
- Ma Jun will will give an oral presentation at the 2017 International Conference on Bioinformatics and Computing Technologies, Hong-Kong
- Their paper entitled “Tissue specificity of in vitro drug sensitivity” was chosen for a best paper award at the 2016 Translational Bioinformatics Conference, South Korea
- Ali Madani presents a poster entitled “Pathway-based optimization of drug development for cancer” at the Centre for Pharmaceutical Oncology Research Symposium, Canada
- Benjamin Haibe-Kains presents “Inference and validation of large-scale causal gene regulatory networks from transcriptomic data” in the Workshop on Models for Oncogenesis, Clonality and Tumor Progression at the Mathematical Biosciences Institute, USA
More stories can be read on the News page.
There are currently positions available in the lab for Postdoctoral Fellows 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.