Machine learning reveals cause-effect relationships in protein dynamics

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. The researchers report that one of their main challenges has been a lack of methods to identify cause-effect relationships in data obtained from molecular dynamics simulations. Machine learning-based analysis of the signaling pathways found inside amino acids found in human proteins.