人工智能
病毒
拉曼光谱
埃博拉病毒
冠状病毒
鉴定(生物学)
计算机科学
计算生物学
病毒学
模式识别(心理学)
生物
2019年冠状病毒病(COVID-19)
物理
医学
传染病(医学专业)
植物
光学
疾病
病理
作者
Jiarong Ye,Yin‐Ting Yeh,Yuan Xue,Ziyang Wang,Na Zhang,He Liu,Kunyan Zhang,RyeAnne Ricker,Zhuohang Yu,Allison Roder,Néstor Perea‐López,Lindsey J. Organtini,Wallace Greene,Susan Hafenstein,Huaguang Lu,Elodie Ghedin,Mauricio Terrones,Shengxi Huang,Xiaolei Huang
标识
DOI:10.1073/pnas.2118836119
摘要
Significance 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI