Deep learning for rapid identification of microbes using metabolomics profiles

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dc.contributor.authorWang, Danhui
dc.contributor.authorGreenwood, Peyton
dc.contributor.authorKlein, Matthias S.
dc.date.accessioned2023-03-22T18:07:40Z
dc.date.available2023-03-22T18:07:40Z
dc.date.issued2021
dc.descriptionArticle originally published in Metabolites, 11(12), 863. English. Published online 2021. https://doi.org/10.3390/metabo11120863
dc.description.abstractRapid detection of viable microbes remains a challenge in fields such as microbial food safety. We here present the application of deep learning algorithms to the rapid detection of pathogenic and non-pathogenic microbes using metabolomics data. Microbes were incubated for 4 h in a protein-free defined medium, followed by 1D 1H nuclear magnetic resonance (NMR) spectroscopy measurements. NMR spectra were analyzed by spectral binning in an untargeted metabolomics approach. We trained multilayer (“deep”) artificial neural networks (ANN) on the data and used the resulting models to predict spectra of unknown microbes. ANN predicted unknown microbes in this laboratory setting with an average accuracy of 99.2% when using a simple feature selection method. We also describe learning behavior of the employed ANN and the optimization strategies that worked well with these networks for our datasets. Performance was compared to other current data analysis methods, and ANN consistently scored higher than random forest models and support vector machines, highlighting the potential of deep learning in metabolomics data analysis.en_US
dc.identifier.citationThis is a published version of an article that is available at https://doi.org/10.1002/ansa.202100052. Recommended citation: Wang, D., Greenwood, P., & Klein, M. S. (2021). Deep learning for rapid identification of microbes using metabolomics profiles. Metabolites, 11(12), 863. This item has been deposited in accordance with publisher copyright and licensing terms and with the author’s permission.en_US
dc.identifier.urihttps://hdl.handle.net/11274/14711
dc.identifier.urihttps://doi.org/10.3390/metabo11120863
dc.language.isoen_USen_US
dc.publisherMDPIen_US
dc.rights.holder© 2021 by the authors.
dc.rights.licenseCC BY 4.0
dc.subjectArtificial neural networksen_US
dc.subjectMachine learningen_US
dc.subjectFood safetyen_US
dc.subjectNMRen_US
dc.subjectPathogensen_US
dc.titleDeep learning for rapid identification of microbes using metabolomics profilesen_US
dc.typeArticleen_US

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