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Semiquantitative Fingerprinting Based on Pseudotargeted Metabolomics and Deep Learning for the Identification of Listeria monocytogenes and Its Major Serotypes
journal contributionposted on 2023-04-04, 16:10 authored by Ying Feng, Xianhu Wei, Moutong Chen, Honghui Zhu, Jumei Zhang, Youxiong Zhang, Liang Xue, Lanyan Huang, Guoyang Chen, Qihui Gu, Shi Wu, Yu Ding, Qingping Wu
The rapid identification of pathogenic microorganism serotypes is still a bottleneck problem to be solved urgently. Compared with proteomics technology, metabolomics technology is directly related to phenotypes and has higher specificity in identifying pathogenic microorganism serotypes. Our study combines pseudotargeted metabolomics with deep learning techniques to obtain a new deep semiquantitative fingerprinting method for Listeria monocytogenes identification at the serotype levels. We prescreened 396 features with orthogonal partial least-squares discrimination analysis (OPLS-DA), and 200 features were selected for deep learning model building. A residual learning framework for L. monocytogenes identification was established. There were 256 convolutional filters in the initial convolution layer, and each hidden layer contained 128 filters. The total depth included seven layers, consisting of an initial convolution layer, a residual layer, and two final fully connected classification layers, with each residual layer containing four convolutional layers. In addition, transfer learning was used to predict new isolates that did not participate in model training to verify the method’s feasibility. Finally, we achieved prediction accuracies of L. monocytogenes at the serotype level exceeding 99%. The prediction accuracy of the new strain validation set was greater than 97%, further demonstrating the feasibility of this method. Therefore, this technology will be a powerful tool for the rapid and accurate identification of pathogens.
squares discrimination analysissemiquantitative fingerprinting basedpredict new isolatesorthogonal partial least256 convolutional filtersprescreened 396 featurespathogenic microorganism serotypesinitial convolution layerachieved prediction accuraciesresidual learning frameworkdeep learning techniqueslisteria monocytogenes identificationresidual layerdeep learningtransfer learningprediction accuracymajor serotypeslisteria monocytogenes200 featuressolved urgentlyserotype levelspseudotargeted metabolomicspowerful toolmonocytogenes identificationmodel traininghigher specificitydirectly relatedda ),bottleneck problemaccurate identification97 %,