{"refrec":{"BRefID":324685,"RR":"<b>Papagiannopoulou, C.; Parchen, R.; Rubbens, P.; Waegeman, W.</b> (2020). Fast pathogen identification using single-cell matrix-assisted laser desorption/ionization-aerosol time-of-flight mass spectrometry data and deep learning methods. <i>Anal. Chem. 92(11)</i>: 7523-7531. <a href=\"https://dx.doi.org/10.1021/acs.analchem.9b05806\" target=\"_blank\">https://dx.doi.org/10.1021/acs.analchem.9b05806</a>","BEntID":318162,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Anal. Chem. 92(11)</i>: 7523-7531. <a href=\"https://dx.doi.org/10.1021/acs.analchem.9b05806\" target=\"_blank\">https://dx.doi.org/10.1021/acs.analchem.9b05806</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Papagiannopoulou, C.; Parchen, R.; Rubbens, P.; Waegeman, W.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Papagiannopoulou, C. <i>et al.</i>","Englishabstract":"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. 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