Abstract
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. The experiment's results showed that the support vector machine algorithm achieved high accuracy in different feature extraction methods. And 188-dimensions feature extraction method improved the classification accuracy. In that the support vector machine algorithm and 188-dimensions feature extraction method were finally selected as the final experimental methods. In the 10-fold cross-validation processes, the SVM combined with 188-dimensions achieved 93.5% accuracy on the training set and 92.7% accuracy on the independent validation set. Besides, the evaluation index of the results of experiments indicate that the method developed by us is valid and robust.
Highlights
Tobacco mosaic virus is worldwide distribution and is the furthest invasive virus which is most harmful to crops
The ACC and Matthews correlation coefficient (MCC) of Support Vector Machine (SVM) and Random Forest (RF) were mostly higher than the predictors of Naive Bayes (NB), K-Nearest Neighbor (KNN) and Bagging under different feature extraction methods (Figures 2, 3)
The sensitivity (Sn) and specificity (Sp) of SVM, RF, and Bagging predictor variables are greater than those of NB and KNN (Table 1 and Figure 5). This result shows that SVM, RF, and Bagging predict tobacco mosaic virus are better than NB and KNN due to the difference in the ability of these five common classification algorithms to handle multidimensional datasets
Summary
Tobacco mosaic virus is worldwide distribution and is the furthest invasive virus which is most harmful to crops. Tobacco is one of the important economic crops in our country, the existence of tobacco mosaic disease has greatly reduced the yield and quality of tobacco. Metzler and Kalinina (2014) used one-class SVM method to detect atypical genes in viral families based on their statistical features, without the need for explicit knowledge of the source species. The simplicity of the statistical features used allows the method to be applied to a variety of viruses. Salama et al (2016) predicted new drug-resistant strains that facilitate the design of antiviral therapies. Neural network techniques were used to predict new strains, and using a rough set theory based on algorithm to extract these points mutation patterns. For phage virion proteins (PVPs) prior to in vitro, Manavalan et al (2018) developed a SVM-based predictor that exhibited good performance and avoided the expensive costs required for experiments
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