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 researchers leverage cutting-edge computational technologies to advance translational research in bioinformatics and machine learning. Here at BHK Lab we specialize in three core research areas:
Pharmacogenomics: BHK Lab researchers actively work in the fields of biomarker discovery and drug repurposing using both preclinical and clinical data. Our research projects are aimed at predicting the trajectory of carcinogenesis and identifying optimal treatment options from high-throughput genomic and pharmacological data.
Radiomics: There is a growing demand for new predictive tools to support clinical decision-making. BHK Lab researchers develop advanced machine learning algorithms that extract high-dimensional features from medical imaging data. Our radiomics team aims to effectively enhance clinical decision-making through the development of diagnostic, prognostic, and predictive radiomic models.
Software Development:The BHK Lab seeks to translate its research outputs into practical solutions. We work tirelessly to create and improve software packages, web-applications and databases for public use by researchers and clinicians. Our tools empower the scientific community in their pharmacogenomic and radiomic analyses of preclinical and clinical data. Visit our Software Developments website to see the list of developed applications.
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.
Lab members have been published in journals such as Nature, Genomics, PLoS Computational Biology and Nature Methods. Publication highlights may be reviewed on the publications page.