Coincidence and connection have been the architect of many great beginnings. That was certainly the case for Nasimeh Asgarian. In 2016, a former colleague from her days at the Alberta Innovates Centre for Machine Learning at the University of Alberta told her about an opportunity with a company doing some exciting work in breast cancer detection research. They thought it would be a perfect fit for Nasimeh’s expertise. Turns out, they were right. Six years later, Nasimeh’s expertise in data and machine learning have played a critical role in the development and launch of Syantra DX | Breast Cancer.
We asked Nasimeh to tell us about her work with machine learning and biological data and why she’s passionate about Syantra’s mission.
I have a Master’s of Science in Computing Science and I'm specialized in machine learning. Even when I was working on my thesis at the U of A, I was interested in applying machine learning or artificial intelligence techniques to biological data. From the data, we can make predictions based on the patterns that we find.
The moment that I realized that we can make good use of machine learning. Sometimes there's so much data available, but there's so little that's done with it. At Syantra, we have the tools to honour the data. I think it's a great opportunity to use machine learning algorithms… If we can detect cancer at earlier stages, it's much easier to treat. And that's exactly what we want to do — use blood analysis to detect cancer at the very earliest stage so patients can be treated much faster and more efficiently.
I think it's very important because I see breast cancer around me a lot — in my friends, in my family members. My hope is that based on the techniques and the tools that we have, we can detect breast cancer at the very earliest stage for every woman, not only among women over 50 years of age. Because many of my friends who are in their forties, or even their thirties, have breast cancer. And they weren’t part of the regular screening program. If we don't have this type of test, younger women might go undetected or they find out very late and they won't be able to treat it anymore. But with this test, we can detect breast cancer early.
We can use machine learning in so many different areas of healthcare. It's fascinating how machine learning can be used together with clinical or biomedical data to come up with new techniques for detection or prediction to ultimately improve patient outcomes.
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