Leveraging machine-learning assisted MALDI-TOF MS to enable identification and typing of the foodborne pathogen Listeria monocytogenes

• Unité de recherche: INNOSUISSE
• Numéro de projet: 35569.1 IP-LS
• Titre du projet anglais: Leveraging machine-learning assisted MALDI-TOF MS to enable identification and typing of the foodborne pathogen Listeria monocytogenes

Description succincte (Allemand)

Leveraging machine-learning assisted MALDI-TOF MS to enable identification and typing of the foodborne pathogen Listeria monocytogenes

Description succincte (Anglais)

Leveraging machine-learning assisted MALDI-TOF MS to enable identification and typing of the foodborne pathogen Listeria monocytogenes

Résumé des résultats (Abstract) (Allemand)

The Institute for Food Safety and Hygiene (UZH) will leverage machine learning algorithms trained by using whole genome sequencing data for processing of MALDI-TOF MS spectra to allow for identification and subtyping of the foodborne pathogen Listeria.