Tag: Random Forest

Mycobacterium abscessus

A Robust Machine Learning Method for Mycobacterium abscessus Typing

Posted on by cloverbio_webadmin

A new machine learning system correctly identifies Mycobacterium abscessus subspecies 97% of the time using MALDI-TOF mass spectra gathered from six European countries. This study is led by E. Padial-Fuillerat and stems from a collaboration between the University of Granada, Hospital Gregorio Marañón and Clover Biosoft. It has been published as open access in the Read more about A Robust Machine Learning Method for <em>Mycobacterium abscessus</em> Typing[…]

Machine Learning in predicting antimicrobial resistance (AMR)

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Recently, Rui et al. published the journal pre-proof of their review “Machine learning in predicting antimicrobial resistance: a systematic review and meta-analysis” which will be published in the International Journal of Antimicrobial Agents. In their publication they used a substantial amount of literature to review and summarize the most used Machine Learning (ML) algorithms and Read more about Machine Learning in predicting antimicrobial resistance (AMR)[…]

An improved MALDI-TOF MS data analysis pipeline for the identification of carbapenemase-producing Klebsiella pneumoniae

Posted on by cloverbio_webadmin

Authors: Eva Gato, Ignacio Pedro Constanso, Ana Candela, Fátima Galán, Bruno Kotska Rodiño-Janeiro, Manuel J. Arroyo, Gema Méndez, Luis Mancera, Tyler Alioto, Marta Gut, Ivo Gut, Miguel Álvarez-Tejado, Belén Rodríguez-Sánchez, Germán Bou, Marina Oviaño DOI: 10.1128/JCM.00800-21 We present the latest published paper at JCM as a result of our collaboration with Microbiology Service from Hospital Read more about An improved MALDI-TOF MS data analysis pipeline for the identification of carbapenemase-producing Klebsiella pneumoniae[…]