Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning

TL;DR

This study develops a CNN-based machine learning method to accurately identify various viruses from Raman spectra, providing interpretable spectral signatures linked to viral biomolecules, enabling rapid and explainable virus detection.

Contribution

It introduces a CNN classifier tailored for spectral data that achieves high accuracy in virus identification and interprets spectral features to understand biomolecular signatures.

Findings

Achieves 99% accuracy in influenza virus classification

Identifies spectral ranges linked to viral biomolecules

Provides interpretable Raman signatures for virus detection

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 approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such a machine learning 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 vs. type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and non-enveloped 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, 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.