What is it about?
The group of bacteria known as Salmonella includes many different types that vary in the severity of the disease they cause. Some types cause food poisoning, whereas others cause severe disease by spreading beyond the gut, for example typhoid fever. To understand the genetic changes that determine whether an emerging strain of salmonella will cause food poisoning versus a more severe infection, we built a machine learning model that identifies they types of mutations that play an important role.
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Why is it important?
The new machine learning tool can be used for flagging dangerous bacteria before they cause an outbreak, from individual hospital wards up to a global scale. In the past we've noticed these dangerous strains of bacteria suddenly appear in large numbers of patients, and when we trace their origins we find they emerged and started spreading around the world decades earlier but we failed to notice them. This kind of software lets us identify these bugs as soon as they appear, so we can prevent their spread.
Perspectives
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. 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. Since this model has been released, we've been able to work with other researchers to test strains they're interested in and assess how dangerous they look. The results of these tests have aligned well with reported symptoms from patients, as well as more detailed analyses of their genomes and work done in the lab.
Dr Nicole E Wheeler
Wellcome Sanger Institute
Read the Original
This page is a summary of: Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica, PLoS Genetics, May 2018, PLOS,
DOI: 10.1371/journal.pgen.1007333.
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