ArticleScientific reports2023
Classification of deep-sea cold seep bacteria by transformer combined with Raman spectroscopy.
Article in Scientific reports, 2023. 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, 34 citations in OpenAlex.
- Artificial Intelligence for Microbial Isolation and Cultivation: Progress and Challenges.Microorganisms · 2026Review
- Recent Advances in Raman Spectral Classification with Machine Learning.Sensors (Basel, Switzerland) · 2026Review
- Transfer learning from inorganic materials to ivory detection.Scientific reports · 2025Article
- Raman ConvMSANet: A High-Accuracy Neural Network for Raman Spectroscopy Blood and Semen Identification.ACS omega · 2023Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Raman spectroscopy is a rapid analysis method of biological samples without labeling and destruction. At present, the commonly used Raman spectrum classification models include CNN, RNN, etc. The transformer has not been used for Raman spectrum identification. This paper introduces a new method of transformer combined with Raman spectroscopy to identify deep-sea cold seep microorganisms at the single-cell level. We collected the Raman spectra of eight cold seep bacteria, each of which has at least 500 spectra for the training of transformer model. We compare the transformer classification model with other deep learning classification models. The experimental results show that this method can improve the accuracy of microbial classification. Our average isolation level accuracy is more than 97%.
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