Selected article for: "cross validation and machine learning"
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_31
    Snippet: Methods. For a comparative purpose, we ran our SVM model on the datasets of two other methods for virus-host PPIs: Barman's method [22] and DeNovo [9] . In Barman's study [22] , three machine learning methods (SVM, Naive Bayes, and Random Forest) were used to predict virus-host PPIs using several features such as domain -domain association in interacting protein pairs and composition of methionine, serine, and valine in virus proteins. In a 5-fol.....
    Document: Methods. For a comparative purpose, we ran our SVM model on the datasets of two other methods for virus-host PPIs: Barman's method [22] and DeNovo [9] . In Barman's study [22] , three machine learning methods (SVM, Naive Bayes, and Random Forest) were used to predict virus-host PPIs using several features such as domain -domain association in interacting protein pairs and composition of methionine, serine, and valine in virus proteins. In a 5-fold cross validation with virus-host PPIs from VirusMINT [23] , their Random Forest (RF) and SVM showed a better performance than Naive Bayes. us, we tested our SVM model on the same dataset used in Barman's study, which contains 1,035 positive and 1,035 negative interactions between 160 virus proteins of 65 types and 667 human proteins. As shown in Table 9

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