Machine learning used to understand and predict dynamics of worm behavior
Biophysicists have used an automated method to model a living system—the dynamics of a worm perceiving and escaping pain. The Proceedings of the National Academy of Sciences (PNAS) published the results, which worked with data from experiments on the C. elegans roundworm.
"Our method is one of the first to use machine-learning tools on experimental data to derive simple, interpretable equations of motion for a living system," says Ilya Nemenman, senior author of the paper and a professor of physics and biology at Emory University. "We now have proof of principle that it can be done. The next step is to see if we can apply our method to a more complicated system."
Dr. Nemenman is a faculty member in the NS and PBEE programs.
Click here to view the full story in Phys.org.