Machine learning techniques may reveal cause-effect relationships in protein dynamics data

Machine learning algorithms excel at finding complex patterns within big data, so researchers often use them to make predictions. Researchers are pushing this emerging technology beyond finding correlations to help uncover hidden cause-effect relationships and drive scientific discoveries. At the University of South Florida, researchers are integrating machine learning techniques into their work studying proteins. As they report in The Journal of Chemical Physics, one of their main challenges has been a lack of methods to identify cause-effect relationships in data obtained from molecular dynamics simulations. "Proteins can be thought of as nanoscopic machines that perform a set of tasks. But when and where proteins carry out their specific tasks is controlled by cells through various stimuli, such as small molecules," said Sameer Varma, an associate professor of biophysics at USF. "These stimuli interact with proteins to switch them'on' and'off,' and can even modify their speeds and strengths."