期刊
FRONTIERS IN MICROBIOLOGY
卷 13, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fmicb.2022.830832
关键词
pseudotargeted metabolomic; deep learning; LC-QQQ-MS; variational autoencoder (VAE); convolutional neural network (CNN)
类别
Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry fingerprinting, combined with pseudotargeted metabolomics and deep learning, enables rapid and accurate identification and visualization of pathogens.
Matrix-assisted laser desorption/ionization time-of-flight mass (MALDI-TOF) spectrometry fingerprinting has reduced turnaround times, costs, and labor as conventional procedures in various laboratories. However, some species strains with high genetic correlation have not been directly distinguished using conventional standard procedures. Metabolomes can identify these strains by amplifying the minor differences because they are directly related to the phenotype. The pseudotargeted metabolomics method has the advantages of both non-targeted and targeted metabolomics. It can provide a new semi-quantitative fingerprinting with high coverage. We combined this pseudotargeted metabolomic fingerprinting with deep learning technology for the identification and visualization of the pathogen. A variational autoencoder framework was performed to identify and classify pathogenic bacteria and achieve their visualization, with prediction accuracy exceeding 99%. Therefore, this technology will be a powerful tool for rapidly and accurately identifying pathogens.
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