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.
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;:
Bidirectional terminators in Saccharomyces cerevisiae prevent cryptic transcription from invading neighboring genes.
Nucleic Acids Res. 2017 Apr 05;:
Cancer Res. 2017 Mar 17;:
Brief Bioinform. 2017 Jan 09;:
Nature. 2016 Nov 30;540(7631):E2-E4
Nature. 2016 Nov 30;540(7631):E11-E12
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