The research performed in the Haibe-Kains Laboratory focuses on the integration of high-throughput data from various sources to simultaneously analyze multiple facets of diseases, with a particular emphasis on cancer. Dr. Haibe-Kains and his team are using publicly available genomic datasets and data generated through his collaboration to better understand the biology underlying diseases and to develop new predictive models in order to significantly improve disease management. Dr. Haibe-Kains's main 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.
Nat Commun. 2017 Oct 24;8(1):1126
Pharmacogenomics. 2017 Oct 23;:
J Am Med Inform Assoc. 2017 Jul 07;:
Elife. 2017 Jul 21;6:
AutoDock and AutoDockTools for Protein-Ligand Docking: Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1(BACE1) as a Case Study.
Methods Mol Biol. 2017;1598:391-403
An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets.
J Hematol Oncol. 2017 May 15;10(1):107
Lancet Oncol. 2017 May;18(5):e238
BioData Min. 2017;10:15
Transcriptome Analysis of Human Reninomas as an Approach to Understanding Juxtaglomerular Cell Biology.
Hypertension. 2017 Apr 10;:
Scientist, Princess Margaret Cancer Centre
Assistant Professor, Department of Medical Biophysics, University of Toronto
Adjunct Assistant Professor, Department of Computer Science, University of Toronto
Ontario Institute for Cancer Research (OICR) Associate