Welkom op het expertplatform!
Dit platform verschaft informatie en kennis omtrent de WL expertisedomeinen 'hydraulica en sediment', 'havens en waterwegen', 'waterbouwkundige constructies', 'waterbeheer' en 'kustbescherming' - gaande van WL medewerkers met hun expertise, het curriculum van deze instelling, tot publicaties, projecten, data (op termijn) en evenementen waarin het WL betrokken is.
Het WL onderschrijft het belang van "open access" voor de ontsluiting van haar onderzoeksresultaten. Lees er meer over in ons openaccessbeleid.
| one publication added to basket [324685] |
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
|   |
| Auteurs | | Top |
- 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. |
IMIS is ontwikkeld en wordt gehost door het VLIZ.
