Strain-Level Identification and Analysis of Avian Coronavirus Using Raman Spectroscopy and Interpretable Machine Learning

Peng Jin, Yin Ting Yeh, Jiarong Ye, Ziyang Wang, Yuan Xue, Na Zhang, Shengxi Huang, Elodie Ghedin, Huaguang Lu, Anthony Schmitt, Sharon X. Huang, Mauricio Terrones

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Strain-level identification of viruses is important for decision making in public health management. Recently, Raman spectroscopy has attained great attention in virus identification since it enables rapid and label-free analysis. In this paper, we present an interpretable machine learning approach for strain-level identification of avian coronaviruses based on Raman spectra. Specifically, we design a spectral transformer to classify the Raman spectra of 32 avian coronavirus strains. After training, relevance maps can be generated through gradient and relevance propagation to further understand the contribution of each wavenumber to the identification. Experimental results show that the proposed method outperforms several machine learning and deep learning baseline models, and achieves 72.72% accuracy in the 32-class identification problem. The relevance maps generated reveal some wavenumber ranges that are important for the identification of almost all strains, and these ranges correlate with Raman peak ranges for lipids, nucleic acids, and proteins.

Original languageEnglish (US)
Title of host publication2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
PublisherIEEE Computer Society
ISBN (Electronic)9781665473583
StatePublished - 2023
Event20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia
Duration: Apr 18 2023Apr 21 2023

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference20th IEEE International Symposium on Biomedical Imaging, ISBI 2023

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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