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.
External validation and transfer learning of convolutional neural networks for computed tomography dental artifact classification.
Phys Med Biol. 2019 Dec 18;:
Clin Cancer Res. 2019 Nov 06;:
Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth.
Phys Med Biol. 2019 Nov 04;:
Cancer Res. 2019 Sep 26;:
Identifying clusters of cis-regulatory elements underpinning TAD structures and lineage-specific regulatory networks.
Genome Res. 2019 10;29(10):1733-1743
Sci Data. 2019 09 03;6(1):166
JNCI Cancer Spectr. 2018 Apr;2(2):pky019
MetaGxData: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature.
Sci Rep. 2019 Jun 19;9(1):8770
Eur J Nucl Med Mol Imaging. 2019 Jun 15;:
Bioinformatics. 2019 Jun 14;:
Senior Scientist, Princess Margaret Cancer Centre
Scientific Lead, Data Science Program, Princess Margaret Cancer Center, University Health Network
Scientific Lead, Radiomics Program of the Radiation Medicine Program (RMP), Princess Margaret Cancer Center, University Health Network
Associate Professor, Department of Medical Biophysics, University of Toronto
Adjunct Professor, Department of Computer Science, University of Toronto
Faculty Associate, Ontario Institute of Cancer Research
Faculty Affiliate, Vector Institute