Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning

@article{Ye2021AccurateVI,
  title={Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning},
  author={Jiarong Ye and Yin-Ting Yeh and Yuan Xue and Ziyang Wang and Na Zhang and He Liu and Kunyan Zhang and Zhuohang Yu and Allison E. Roder and N{\'e}stor Perea L{\'o}pez and Lindsey J. Organtini and Wallace Greene and Susan Hafenstein and Huaguang Lu and Elodie Ghedin and Mauricio Terrones and Shengxi Huang and Sharon Huang},
  journal={bioRxiv},
  year={2021}
}
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 approach applied to recognize the virus based on its Raman spectrum. In this paper, we present a machine learning analysis on Raman spectra of human… 

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