Application of machine learning in MALDI-TOF MS identification of microorganisms

Abstract:

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a novel high-throughput technology widely used in rapid identification of clinical microorganisms, food microorganisms and aquatic microorganisms. Currently, however, how to further improve the resolution of MALDI-TOF MS in microbial identification is a major challenge for this technology. To effectively deal with the large amounts of high-dimensional microbial MALDI-TOF MS data, a variety of machine learning algorithms have been applied. This paper reviews the applications of machine learning in MALDI-TOF MS identification of microorganisms. Herein, the workflow of machine learning in the classification of microbial MALDI-TOF MS is introduced. Then, the characteristics of MALDI-TOF MS data, MALDI-TOF MS database, the preprocessing of the MALDI-TOF MS data, and the performance evaluation of the model are further described. The applications of typical machine learning classification algorithms and ensemble learning algorithms are also discussed.