Using Big Data to Personalize Medicine
For most Canadians, being treated by a doctor entails a “one-size-fits-all” approach. Doctors evaluate symptoms, order diagnostic tests and craft treatment plans. But is this the best approach? As Canada Research Chair in Data Science, Dr. Jiguo Cao wants to come up with a better way of treating patients.
Currently, the recommended course of action is often the same for any patient with the same condition, regardless of age, sex or ethnicity. If the first treatment does not work, doctors move on to the next, using a trial and error approach. But in an ideal world, doctors would provide drugs or treatments to patients based on their unique traits, environment, diet and genes.
Today’s modern laboratory technology allows for more personalized medicine by providing essential genome sequencing at a low cost. But to make this a reality, researchers first need to gain a better understanding of which genes are associated with, or affected by, particular diseases, drugs or treatments.
Cao and his research team are using big data analysis and developing novel statistical methods to dramatically increase our knowledge of the relationship between genes, diseases and treatments. They are using these tools to identify genes that control complex systems, such as the mechanisms of absorption and distribution of drugs in the body, the infection and clearance of HIV virus, and brain networks of patients with Alzheimer's disease. Ultimately, their findings could help doctors provide more personalized care in the future.