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organizations' existing HPCs if his team ever decides to go down that road.
Participating in this Collaborative Cancer Cloud has facilitated two new collaborations for the Ontario Institute for Cancer Research, one with Dana Farber and the other with the Knight Cancer Institute at the Oregon Health and Science University.
It's also led to research in a new emerging area of machine learning. How do you create new machine learning techniques for a federated data model?
The project has enabled the Ontario Institute for Cancer Research to engage in a large-scale meta-analysis. Boutros said his organization is looking at a critical question in prostate cancer research: If a patient is diagnosed, should he be treated or not? Clinicians will make mistakes about a third of the time, Boutros said. Some men get therapy that doesn't give them any benefit, and some men don't get the therapy that they need.
Intel's Olson has gotten the treatment to stem his cancer's growth because of genetic sequencing and research. Collaborating takes that to the next level.
"Even if a single hospital sequenced every cancer patient who is on file with them, they'd only have 1% of the cancer population data, because cancer population data is so dispersed across the United States," Olson said.
"No single hospital is going to solve this. The only way you are going to advance the science is if you can figure out a way to enable these cancer institutions all over the United States and all over the world to collaborate with each other and share. Then you can get access to this big data pool."