MetaData

The NewSTHEPS MetaData catalogue gives an overview of all persons and institutes involved and lists all created datasets and publications coming out of the project.

 

Persons Institutes Publications Datasets

 

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Fast pathogen identification using single-cell matrix-assisted laser desorption/ionization-aerosol time-of-flight mass spectrometry data and deep learning methods
Papagiannopoulou, C.; Parchen, R.; Rubbens, P.; Waegeman, W. (2020). Fast pathogen identification using single-cell matrix-assisted laser desorption/ionization-aerosol time-of-flight mass spectrometry data and deep learning methods. Anal. Chem. 92(11): 7523-7531. https://dx.doi.org/10.1021/acs.analchem.9b05806
In: Analytical chemistry. American Chemical Society: Washington. ISSN 0003-2700; e-ISSN 1520-6882
Peer reviewed article  
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  • Papagiannopoulou, C.
  • Parchen, R.
  • Rubbens, P.
  • Waegeman, W.
Abstract
    In diagnostics of infectious diseases, matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) can be applied for the identification of pathogenic microorganisms. However, to achieve a trustworthy identification from MALDI-TOF MS data, a significant amount of biomass should be considered. The bacterial load that potentially occurs in a sample is therefore routinely amplified by culturing, which is a time-consuming procedure. In this paper, we show that culturing can be avoided by conducting MALDI-TOF MS on individual bacterial cells. This results in a more rapid identification of species with an acceptable accuracy. We propose a deep learning architecture to analyze the data and compare its performance with traditional supervised machine learning algorithms. We illustrate our workflow on a large data set that contains bacterial species related to urinary tract infections. Overall we obtain accuracies up to 85% in discriminating five different species.
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