Molecular subtyping of cancers is key in the path of personalized medicine. Gene expression-based classification models to robustly identify cancer molecular subtypes. Collaborating with Entagen, I have implemented a web application that enable users to easily infer predictive gene interaction networks by combining interactions extracted from publications, pathway databases and gene expression data (Nucleic Acids Res 2012). I implemented predictionet, a network inference approach integrating genomic data and priors, and developed a new validation framework based on high-throughput perturbation experiments (Genomics 2014). With the recent interest in network medicine I decided to investigate the use of gene interactions extracted from the biomedical literature and structured biological databases (referred to as “priors”) to better infer gene-gene interaction networks from gene expression data. Large-scale causal gene regulatory networks. We are now developing new analysis pipelines to increase robustness of drug phenotypic measurements to build more robust genomic predictors of drug response. We then investigated the consistency between these two large pharmacogenomic studies as potential cause of failure for most of our predictors and discovered that, although gene expression data were highly concordant, the drugs sensitivity data were highly inconsistent across studies (Nature 2013 Cancer Res 2014). We used CGP data to train genomic predictors of response to 15 drugs screened in both studies, and showed that half of these models could not be validated on CCLE (J Am Med Inform Assoc 2013). The Cancer Genome Project (CGP) and the Cancer Cell Line Encycolpedia (CCLE) studies recently published drug sensitivity data on large panel of genomically-characterized cancer cell lines with the aim of unraveling new associations between genomic features of these cell lines and their response to drugs. My laboratory is focusing on developing robust genomic predictors of drug response from pharmacogenomic data. Predictors of drug response based on pharmacogenomic data. Haibe-Kains’ team is analyzing large-scale radiological and (pharmaco)genomic datasets to develop new prognostic and predictive models to improve cancer care. Haibe-Kains’ research focuses on the integration of high-throughput data from various sources to simultaneously analyze multiple facets of carcinogenesis. Supported by a Fulbright Award, he did his postdoctoral fellowship at the Dana-Farber Cancer Institute and Harvard School of Public Health (USA). Haibe-Kains earned his PhD in Bioinformatics at the Université Libre de Bruxelles (Belgium). Benjamin Haibe-Kains is a Senior Scientist at the Princess Margaret Cancer Centre (PM), University Health Network, and Associate Professor in the Medical Biophysics department of the University of Toronto. We make our research fully reproducible!ĭr.Algorithmic and software development at the core of the lab research.Integrative analysis of multi -omics data in different model systems (cell lines, xenografts, primary tumors).Large network of collaborators in multiple cancer types.Strong emphasis on translational research, with close collaboration with clinicians to develop relevant biomarkers and novel therapeutic strategies.Focus of the lab includes Computational Biology, Bioinformatics, Machine learning, Cancer Genomics and Pharmacogenomics.
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