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Natl. Acad. Sci. U.S.A."],"published-print":{"date-parts":[[2022,6,7]]},"abstract":"<jats:title>Significance<\/jats:title>\n                  <jats:p>A large Raman dataset collected on a variety of viruses enables the training of machine learning (ML) models capable of highly accurate and sensitive virus identification. The trained ML models can then be integrated with a portable device to provide real-time virus detection and identification capability. We validate this conceptual framework by presenting highly accurate virus type and subtype identification results using a convolutional neural network to classify Raman spectra of viruses. The accurate and interpretable ML model developed for Raman virus identification presents promising potential in a real-time, label-free virus detection system that could be used in future outbreaks and pandemics.<\/jats:p>","DOI":"10.1073\/pnas.2118836119","type":"journal-article","created":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T14:33:47Z","timestamp":1654180427000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":63,"title":["Accurate virus identification with interpretable Raman signatures by machine learning"],"prefix":"10.1073","volume":"119","author":[{"given":"Jiarong","family":"Ye","sequence":"first","affiliation":[{"name":"College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 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