ArticleJournal of biophotonics2022
Discriminating cell line specific features of antibiotic-resistant strains of Escherichia coli from Raman spectra via machine learning analysis.
Article in Journal of biophotonics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Interpretable Multiscale Convolutional Neural Network for Classification and Feature Visualization of Weak Raman Spectra of Biomolecules at Cell Membranes.ACS sensors · 2025Article
- Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy for rapid, label-free biological imaging.The Review of scientific instruments · 2025Article
- Toward Gene-Correlated Spatially Resolved Metabolomics with Fingerprint Coherent Raman Imaging.The journal of physical chemistry. B · 2023Article
- Recent advances of Raman spectroscopy for the analysis of bacteria.Analytical science advances · 2023Review
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Authors and funding
4 authors.
Funding
Abstract
While Raman spectroscopy can provide label-free discrimination between highly similar biological species, the discrimination is often marginal, and optimal use of spectral information is imperative. Here, we compare two machine learning models, an artificial neural network and a support vector machine, for discriminating between Raman spectra of 11 bacterial mutants of Escherichia coli MDS42. While we find that both models discriminate the 11 bacterial strains with similarly high accuracy, sensitivity and specificity, it is clear that the models form different class boundaries. By extracting strain-specific (and function-specific) spectral features utilized by the models, we find that both models utilize a small subset of high intensity peaks while separate subsets of lower intensity peaks are utilized by only one method or the other. This analysis highlights the need for methods to use the complete spectral information more effectively, beginning with a better understanding of the distinct information gained from each model.
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Registered trials
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