Results


Less Than 10% of Bovine i E. coli /i Strains Affect Human Health

#artificialintelligence

Using software to compare genetic information in bacterial isolates from animals and people, researchers have predicted that less than 10% of Escherichia coli 0157:H7 strains are likely to have the potential to cause human disease. In this study, the researchers applied machine learning to predict the zoonotic potential of bacterial isolates from the United Kingdom and the United States. "[O]ne of the cattle isolates (apart from outbreak trace-back isolates) achieved very high human association probabilities ( 0.9), potentially indicating that those posing a serious zoonotic threat are very rare," the authors write. As a consequence, experts could use targeted control strategies, including vaccination or eradication, in cattle carrying strains of high zoonotic potential, in order to better protect human health.



Researchers Use Machine Learning to Detect Pathogenic Bacteria in Cattle

#artificialintelligence

A team of researchers has found a new way to detect dangerous strains of bacteria, potentially preventing outbreaks of food poisoning. The team developed a method that utilizes machine learning and tested it with isolates of Escherichia coli strains. The team utilized machine learning, a form of artificial intelligence that allows computers to learn and analyze patterns. If researchers can quickly identify cattle herds carrying dangerous strains of E.coli, that particular herd can be treated or isolated before an outbreak occurs.


The Roslin Institute (University of Edinburgh) - News

#artificialintelligence

Machine learning can predict strains of bacteria likely to cause food poisoning outbreaks, research has found. The study – which focused on harmful strains of E. coli bacteria – could help public health officials to target interventions and reduce risk to human health. The team trained the software on DNA sequences from strains isolated from cattle herds and human infections in the UK and the US. The study highlights the potential of machine learning approaches for identifying these strains early and prevent outbreaks of this infectious disease.