Scientists at the University of California, Berkeley, have recently published work that lays the foundation for new ways of thinking about pathogen evolution. "Our research highlights that template-free modeling that uses machine learning is indeed superior to template-based modeling for the secreted proteins of the destructive fungal pathogen Magnaporthe oryzae," said Kyungyong Seong, first author of the paper published in the Molecular Plant-Microbe Interactions (MPMI) journal.