An overview of the PROOF Centre’s machine learning strategy and HEARTBiT program.  The program aims to create a minimally invasive test for monitoring heart transplant rejection using genomic technologies. We will describe the work that led to initial discovery of a transcriptomic signature using cDNA microarrays, moving this signature to a clinically relevant targeted platform and conducting initial clinical and analytical validation work. We will also discuss our soon-to-be-completed prospective trial and several ongoing projects to enhance clinical outcomes for heart transplant recipients at every stage of their care. We will also showcase the computational method development opportunities that arose through pursuit of biomarkers for various other research studies.
 
What you'll learn in this session:

  • Machine learning methods used in biomarker discovery
  • Necessary steps in the development of a genomic-based diagnostic
  • Common types of genomic biomarkers and endpoints
  • Limitations and opportunities offered by tissue complexity and data integration

Learn more about the PROOF Centre of Excellence

About Casey Shannon

Director, Data Science,  PROOF Centre of Excellence

Casey Shannon is a data scientist and computational biologist. At the PROOF Centre of Excellence, he uses high dimensional gene, protein, and metabolite data to derive clinically useful diagnostic or prognostic tests. This involves the application of machine learning approaches to identify informative features from the vast amounts of data generated by high throughput omics platforms.

Prior to joining PROOF, Casey’s research interests included the study of spinal cord injury in rodent models, particularly the use of adult skin-derived stem-cells in spinal cord injury repair. This work lead to publications in The Journal of Neurotrauma and The Journal of Neuroscience.

Casey holds Bachelor’s degrees in Cell Physiology and Genetics, as well as Computer Science, from the University of British Columbia.
Casey Shannon