“In nature, nothing is perfect. Trees can be contorted, bent in weird ways and they’re still beautiful.”
- Alice Walker
On a genetic level, we humans often more resemble trees twisted in individual, complex ways than the neat diagrams presented in anatomy texts.
This makes it a whole lot more complicated to figure out how to keep people healthy and prevent, diagnose and treat disease.
Artificial intelligence, big data, and machine learning techniques have the potential to help researchers and medical professionals with this, though, as many research leaders discussed during San Francisco State University Department of Biology’s annual Personalized Medicine Conference. Held at the South San Francisco Conference Center May 31st, 2018, this gathering touched on the promises – and the complications – of data-driven medicine, individualized for particular groups of people, and ultimately, particular people.
Computers’ ability to store and analyze large sets of data can allow researchers to determine which patients are more or less likely to develop a disease or respond to a certain treatment. As keynote speaker Dr. Manuel Rivas, Assistant Professor of Biomedical Data Science at Stanford University pointed out, most individual human genes have only a weak link to a person’s health. Computerized examination of large data sets taken from many patients’ genomes might give us a better idea of which combinations of genes, acting together, would lead to greater or lesser risks of developing a condition, as his work pointed to with Type 2 diabetes.