PMC:2667511 / 50769-52147 JSONTXT

{"project":"2_test","denotations":[{"id":"19173748-16423290-8336760","span":{"begin":574,"end":576},"obj":"16423290"}],"text":"We have developed a classification method that categorizes each PPI into one of four different types using association rule based classification (ARBC). The application of association rule mining over 354 known PPI domains using 14 properties yielded a total of 157 rules, which in turn discriminated the features of interaction sites for different PPI types and were used to generate a classification model to predict PPI types. Our ARBC approach performed competitively compared with conventional methods applied directly to the property values: for example, the work in [12] reported an accuracy of 91.8% for the classification of three types of interactions by directly applying SVM. Although it is not possible to make a direct comparison of their method with ours due to heterogeneity of the data set, this suggests that the processes of association rule generation and subsequent pruning do not incur a loss of relevant information. At the same time, our results demonstrated that we were able to considerably improve the accuracy of the prediction of PPI types through the use of structural domain information for the description of interaction interfaces, and also the use of secondary structure content. Although SSE content alone could not classify interaction sites with high accuracy, its incorporation with other properties improved the accuracy of classification."}