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Cracking the code of cancer
Applying machine learning to understand patient response
Cancer is a complex and diverse disease arising from gene mutations that can happen in a myriad of ways, therefore, finding one single cure is an impossible task. Designing effective, targeted therapies for given cancer types requires an understanding of the mutation patterns that gave rise to the disease. Using machine learning techniques and cell simulations, it is now possible to catch a glimpse of those patterns, which, paired with biological insight, have a vast potential for facilitating cancer drug development. That's the big picture, but what's the first step? When does machine learning come into play, and what can you do with it? This talk is going to pull back the curtains on a real-life research project aiming to uncover the genetic patterns affecting drug resistance in patients with chronic lymphocytic leukemia - from idea to implementation and beyond.
Turbine enhances cancer drug discovery processes by taking trial and error out of the laboratory and moving it onto servers that can run millions of simulated experiments in minutes.