science
Graph-based neural networks improve paramyxovirus host predictions in sparse-data tests
A bioRxiv preprint describes a graph neural network that incorporates viral evolutionary relationships when predicting the host associated with a paramyxovirus sequence. The authors report advantages over unstructured neural networks when training data are sparse, while finding that phylogeny alone explains much of the predictive performance.