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Project context and summary :
Klebsiella pathogens affect human and animal health and are widely distributed in the environment. Among these, the Klebsiella pneumoniae species complex, which includes seven phylogroups, is an important cause of community and hospital infections. The Klebsiella oxytoca species complex also causes hospital infections and antibiotic-associated haemorrhagic colitis. The unsuitability of currently used clinical microbiology methods to distinguish species within each of these species’ complexes leads to high rates of misidentifications that are masking the true clinical significance and potential epidemiological specificities of individual species. Through this project, we will propose a single website for Klebsiella species identification by the mean of MALDI-TOF mass spectrometry. This website will host two tools. One is Klebsiella MALDI TypeR, which was built at Pasteur based on the automatic detection of defined biomarkers. The other one is based on machine learning, with a classifier based on a random forest algorithm trained on hundreds to thousands of spectra. The latter was developed in Melbourne University.Related team publications :
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