Haibe-kains, Benjamin

Biography

Benjamin Haibe-Kains is Scientist at the Princess Margaret Cancer Centre (PM), University Health Network, and Assistant Professor in the Medical Biophysics and Computer Science departments of the University of Toronto. Dr. Haibe-Kains earned his PhD in Bioinformatics at the Université Libre de Bruxelles (Belgium), for which he was awarded a Solvay Award (Belgium). Supported by a Fulbright Award, Dr. Haibe-Kains did his postdoctoral fellowship at the Dana-farber Cancer Institute and Harvard School of Public Health (USA). Dr. Haibe-Kains started his own laboratory at the Institut de Recherches Cliniques de Montréal (Canada) and moved to PM in November 2013. Dr. Haibe-Kains’ research focuses on the integration of high-throughput data from various sources to simultaneously analyze multiple facets of carcinogenesis. Dr. Haibe-Kains and his team are analyzing high-throughput (pharmaco)genomic datasets to develop new prognostic and predictive models and to discover new therapeutic regimens in order to significantly improve disease management. Dr. Haibe-Kains' main scientific contributions include several prognostic gene signatures in breast cancer, subtype classification models for ovarian and breast cancers, as well as genomic predictors of drug response in cancer cell lines. Benjamin Haibe-Kains is Scientist at the Princess Margaret Cancer Centre (PM), University Health Network, and Assistant Professor in the Medical Biophysics and Computer Science departments of the University of Toronto. Dr. Haibe-Kains earned his PhD in Bioinformatics at the Université Libre de Bruxelles (Belgium), for which he was awarded a Solvay Award (Belgium). Supported by a Fulbright Award, Dr. Haibe-Kains did his postdoctoral fellowship at the Dana-farber Cancer Institute and Harvard School of Public Health (USA). Dr. Haibe-Kains started his own laboratory at the Institut de Recherches Cliniques de Montréal (Canada) and moved to PM in November 2013. Dr. Haibe-Kains’ research focuses on the integration of high-throughput data from various sources to simultaneously analyze multiple facets of carcinogenesis. Dr. Haibe-Kains and his team are analyzing high-throughput (pharmaco)genomic datasets to develop new prognostic and predictive models and to discover new therapeutic regimens in order to significantly improve disease management. Dr. Haibe-Kains' main scientific contributions include several prognostic gene signatures in breast cancer, subtype classification models for ovarian and breast cancers, as well as genomic predictors of drug response in cancer cell lines.

Research Interest

Focus of the lab includes Computational Biology, Bioinformatics, Machine learning, Cancer Genomics and Pharmacogenomics Strong emphasis on translational research, with close collaboration with clinicians to develop relevant biomarkers and novel therapeutic strategies Large network of collaborators in multiple cancer types Integrative analysis of multi -omics data in different model systems (cell lines, xenografts, primary tumors) Algorithmic and software development at the core of the lab research We make our research fully reproducible!