ArticleProceedings of the National Academy of Sciences of the United States of America2022
Accurate virus identification with interpretable Raman signatures by machine learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Native Label-Free Spectral Biochemical Fingerprinting of an Enveloped Mammalian RNA Virus Reveals Mode of Action of a Virucidal Drug.Small methods · 2026Article
- Surface-Enhanced Raman Spectroscopy for Viral Diagnostics: Principles, Strategies, Clinical Challenges, and Future Directions.Chemical reviews · 2026Review
- Recent Advances in Raman Spectral Classification with Machine Learning.Sensors (Basel, Switzerland) · 2026Review
- Portable Raman spectroscopy combined with machine learning for rapid and label-free recognition of five pathogenic bacteria.Frontiers in microbiology · 2026Article
- Nanostructure-Engineered Optical and Electrochemical Biosensing Toward Food Safety Assurance.Foods (Basel, Switzerland) · 2025Review
- Extracting True Virus SERS Spectra and Augmenting Data for Improved Virus Classification and Quantification.ACS sensors · 2025Article
- Machine Learning Interpretation of Optical Spectroscopy Using Peak-Sensitive Logistic Regression.ACS nano · 2025Article
- Interpretable Multiscale Convolutional Neural Network for Classification and Feature Visualization of Weak Raman Spectra of Biomolecules at Cell Membranes.ACS sensors · 2025Article
- Multiplex Detection and Quantification of Virus Co-Infections Using Label-free Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms.ACS sensors · 2025Article
- Towards high-accuracy bacterial taxonomy identification using phenotypic single-cell Raman spectroscopy data.ISME communications · 2025Article
- Rapid and label-free influenza A virus subtyping using surface-enhanced Raman spectroscopy with incident-wavelength analysis.Biomedical optics express · 2024Article
- Article
- Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Raman Multi-Omic Snapshot and Statistical Validation of Structural Differences between Herpes Simplex Type I and Epstein-Barr Viruses.International journal of molecular sciences · 2023Article
- Review
- Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques.Biosensors · 2023Review
- Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy.Computational and structural biotechnology journal · 2023Article
- Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications.Biosensors · 2022Review
- Article
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device, coupled with label-free Raman spectroscopy, holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning (ML) approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such an ML approach for analyzing Raman spectra of human and avian viruses. A convolutional neural network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A versus type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus [IBV]) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups—for example, amide, amino acid, and carboxylic acid—we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids, and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses.
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