The Campbell Lab works on a diverse set of problems at the intersection of machine learning and biomedical research. We are based in Toronto's Discovery District at the Lunenfeld-Tanenbaum Research Institute of Sinai Health System and the Departments of Molecular Genetics and Statistical Sciences at the University of Toronto.
We work on many things including:
- Machine learning models for spatial ‘omics data
- Understanding the composition and dynamics of the tumour microenvironment and how it enables tumour progression
- Machine learning algorithms to help automate biological and biomedical data analysis
- Software development for the design and analysis of highly multiplexed imaging datasets
Latest news
Oct 6, 2024
New preprint
Congratulations to Vesal on his new preprint on how to design and evaluate protein bait subsets for BioID profiling.
Sep 10, 2024
New preprint
Congratulations to Chengxin on his preprint showing how multicellular niches can be inferred from single-cell atlases and linking these to patient outcomes in pancreatic cancer.
Jun 17, 2024
New lab publication
Congratulations to Cindy and Alina for their paper Beyond benchmarking and towards predictive models of dataset-specific single-cell RNA-seq pipeline performance now published in Genome Biology.
Jun 8, 2024
Kieran presenting at Spatial Biology East Coast 2024
Kieran will be giving a talk on Machine learning tools for the analysis and interpretation of highly multiplexed imaging data at Spatial Biology East Coast 2024.
May 12, 2024
Welcome Cece and Elliot
Welcome to Cece and Elliot who join as summer undergraduate research students.
Apr 15, 2024
Lab presentations at RECOMB 2024
Michael and Jett will be presenting posters/short talks on their work on mapping loss of antigen presentation at single cell resolution and on segmentation aware clustering for multiplexed imaging at RECOMB and RECOMB-CCB 2024.
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