Machine learning using convolutional neural networks for SERS analysis of biomarkers in medical diagnostics
© 2022 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons Ltd.
| Veröffentlicht in: | Journal of Raman spectroscopy : JRS. - 1999. - 53(2022), 12 vom: 05. Dez., Seite 2044-2057 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | Journal of Raman spectroscopy : JRS |
| Schlagworte: | Journal Article convolutional neural network machine learning molecular diagnostics multiplexed spectral analysis surface‐enhanced Raman spectroscopy |
| Zusammenfassung: | © 2022 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons Ltd. Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications because of narrow spectral features that allow multiplexed analysis. Machine learning (ML) has been used for non-dye-labeled SERS spectra but has not been applied to SERS dye-labeled materials with known spectral shapes. Here, we compare the performances of spectral decomposition, support vector regression, random forest regression, partial least squares regression, and convolutional neural network (CNN) for SERS "spectral unmixing" from a multiplexed mixture of 7 SERS-active "nanorattles" loaded with different dyes for mRNA biomarker detection. We showed that CNN most accurately determined relative contributions of each distinct dye-loaded nanorattle. CNN and comparative models were then used to analyze SERS spectra from a singleplexed, point-of-care assay detecting an mRNA biomarker for head and neck cancer in 20 samples. The CNN, trained on simulated multiplexed data, determined the correct dye contributions from the singleplex assay with RMSElabel = 6.42 × 10-2. These results demonstrate the potential of CNN-based ML to advance SERS-based diagnostics |
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| Beschreibung: | Date Revised 16.09.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 0377-0486 |
| DOI: | 10.1002/jrs.6447 |