Network embedding unveils the hidden interactions in the mammalian virome.

Predicting host-virus interactions is fundamentally a network science problem. We develop a method for bipartite network prediction that combines a recommender system (linear filtering) with an imputation algorithm based on low-rank graph embedding. We test this method by applying it to a global database of mammal-virus interactions and thus show that it makes biologically plausible predictions that are robust to data biases.

Portrait of driving practice following a mild stroke: a secondary analysis of a chart audit.

: The majority of individuals who have had a mild stroke are discharged home from acute care. Yet, the proportion who are assessed for driving ability and given related recommendations is unknown.: To describe acute care practice related to driving among individuals whose discharge location is home.