Author: Alguwaizani, Saud; Park, Byungkyu; Zhou, Xiang; Huang, De-Shuang; Han, Kyungsook
Title: Predicting Interactions between Virus and Host Proteins Using Repeat Patterns and Composition of Amino Acids Document date: 2018_5_9
ID: 0dxrai3j_26
Snippet: As discussed earlier, we trained the SVM model with the training dataset TR1 consisting of PPIs of human with +ssRNA viruses except hepatitis C virus (HCV) and SARS [20] to assess the independence of the test data from the training data. As shown in Table 5 , target virus proteins in the test datasets showed a very low average sequence similarity in the range (3.12% to 5.20%) to the virus proteins in the training dataset (see Additional file 4 fo.....
Document: As discussed earlier, we trained the SVM model with the training dataset TR1 consisting of PPIs of human with +ssRNA viruses except hepatitis C virus (HCV) and SARS [20] to assess the independence of the test data from the training data. As shown in Table 5 , target virus proteins in the test datasets showed a very low average sequence similarity in the range (3.12% to 5.20%) to the virus proteins in the training dataset (see Additional file 4 for the similarity of every sequence pair between the training and test datasets). Table 6 shows the results of testing the prediction model on 5 independent datasets of PPIs of new viruses. Despite such a low sequence similarity and species difference, the SVM model showed a high performance in independent testing. In particular, the SVM model showed a higher sensitivity (94.37% and 96.67%) for HCV and SARS virus, which are +ssRNA viruses. It is interesting to note that HPV-16, which is a dsDNA virus, showed the highest specificity of 94.04% and accuracy of 87.93%. Figure 4 shows the ROC curves of independent testing of the SVM model on PPIs of five new viruses.
Search related documents:
Co phrase search for related documents- accuracy high specificity and high sensitivity: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63
- accuracy high specificity and high specificity: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
- accuracy high specificity and prediction model: 1, 2, 3, 4, 5, 6, 7
- accuracy high specificity and ROC curve: 1
- accuracy high specificity and SVM model: 1, 2, 3
- accuracy high specificity and test dataset: 1
- additional file and dsdna virus: 1
- additional file and high sensitivity: 1
- additional file and new virus: 1, 2, 3, 4, 5
- additional file and SVM model: 1
- additional file and virus protein: 1, 2
Co phrase search for related documents, hyperlinks ordered by date