"We have designed a new machine learning model that can identify which emerging strains of bacteria could be a public health concern. Using this tool, we can tackle massive data sets and get results in seconds,” said Nicole Wheeler, co-lead author from the Wellcome Sanger Institute. “Ultimately, this work will have a big impact on the surveillance of dangerous bacteria in a way we haven't been able to before, not only in hospital wards, but at a global scale."

The develop the machine learning tool, researchers trained the model with old varieties of salmonella that included six bacterial strains that caused invasive infections and seven gastrointestinal strains. The model was then able to identify almost 200 genes correlating to the severity of the salmonella strain. When examining the new enterica strain, the model correctly identified the bacteria from a pool of circulating infections. The model found the strain to be more dangerous, resulting in higher rates of bloodstream infections than other bacterial strains.

"The machine learning tool is an advance compared to other methods as it not only searches for genes and mutations, it looks for the functional impacts mutations have in these bugs,” said Lars Barquist, co-lead author from the Helmholtz Institute for RNA-based Infection Research in Germany. “It can tell us which mutations make pathogens better at spreading beyond the gut and causing a life-threatening disease rather than food poisoning. This will help in designing more effective treatments in the future."