Identification of Proteins of Tobacco Mosaic Virus by Using a Method of Feature Extraction - Chen, Yu-Miao; Zu, Xin-Ping; Li, Dan (Frontiers in Genetics, 2020)
Tobacco mosaic virus, TMV for short, is widely distributed in the global tobacco industry and has a significant impact on tobacco production. It can reduce the amount of tobacco grown by 50-70%. In this research of study, we aimed to identify tobacco mosaic virus proteins and healthy tobacco leaf proteins by using machine learning approaches. In the experimental process, we use different feature extraction methods and different machine learning classification algorithms. After comparing the experimental results, we select the feature extraction methods that perform well and combine them with machine learning algorithms to conduct the classification experiment. The experiment’s results showed that the support vector machine algorithm achieved high accuracy in different feature extraction methods and the 188_CKSAAGP combination feature extraction method improved the classification accuracy, so the support vector machine algorithm and 188_CKSAAGP were finally selected as the final experimental methods. In the 10-fold cross-validation processes, the SVM combined with 188_CKSAAGP obtained 93.5% accuracy, and the evaluation index of the results of experiments indicate that the method developed by us is valid and robust.