Pseudotargeted Metabolomic Fingerprinting and Deep Learning for Identification and Visualization of Common Pathogens - Feng, Ying; Chen, Moutong; Wei, Xianhu; Zhu, Honghui; Zhang, Jumei; Zhang, Youxiong; Xue, Liang; Huang, Lanyan; Chen, Guoyang; Chen, Minling; Ding, Yu; Wu, Qingping (Frontiers in Microbiology, 2022)
The MALDI-TOF-MS 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- convolutional neural network (VAE-CNN) 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.