Fighting cancer with code
Machine learning advances personalized vaccines
There’s a unique code written in the cancer cells of every patient’s body. And that data can be key to developing life-saving personalized cancer vaccines. But how can we decipher the code? Researchers at Waterloo’s Cheriton School of Computer Science have enlisted an unlikely ally in the quest: machine learning.
Cancer is the leading global cause of death
(World Health Organization)
People were diagnosed with cancer globally in 2022
(American Cancer Society)
people develop cancer in their lifetime
(World Health Organization)
Every patient is different and every cancer is different, so cancer treatment shouldn’t be the same for all.
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Dr. Ming Li is a professor at the Cheriton School of Computer Science, University of Waterloo. Li and his team are applying AI and deep learning to develop tools that detect short peptides and neoantigens on the surface of a cancer cell using mass spectrometer paving the way for new cancer drugs and treatments.
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Life-saving machine learning model
DeepNovo – Li and team’s new machine learning model – uses computer programs that can learn from data. DeepNovo analyzes data from mass spectrometry, looking for patterns in the fragments of peptides to solve complex peptide sequencing challenges. The more thorougher we discover peptides from the cancer cell surface, the easier it will be to develop therapeutic drugs or treatments for medical conditions, like cancer. DeepNovo is ground-breaking because it can be trained to work with different types of data sources, paving the way for cancer vaccines personalized to every patient.
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