MALDI-TOF MS platform combined with machine learning to establish a model for rapid identification of methicillin-resistant Staphylococcus aureus.

Journal: Journal of microbiological methods
PMID: 33271209

Abstract

MALDI-TOF MS is an effective potential tool to distinguish between MSSA and MRSA. By combining the ClinProTools3.0 software and manual grouping intervention, we proposed a model optimization method for the first time. The cross validation of the model increased from 95.82% to 96.68%, and the accuracy of the model increased from 88.89% to 91.98%. Finally, we reported nine characteristic peaks of rapid detection of MRSA.

Authors

  • Kewen Tang
    Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei Province, China.
  • Dongling Tang
    Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei Province, China.
  • Qianyu Wang
    Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei Province, China.
  • Congrong Li
    Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei Province, China. Electronic address: congrong203020053@163.com.

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