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Predicting Lysine Glutarylation Sites by Combining Multiple Feature Selection Methods

Lysine glutarylation is a dynamic and conserved post-translational modification of protein acylation. For identification of the molecular behavior of lysine glutarylation, it is necessary to identify the lysine glutarylation sites. Machine learning techniques help in the pathogenesis of biological mechanism processes to make a proper decision for developing glutarylated site identification. In this study, several feature encoding methods have been applied, i.e. amino acid factors (AAF), binary encoding (BE), and composition of k-spaced amino acid pairs (CKSAAP) to encode features from peptide sequences of lysine glutarylation modification database. Multiple feature selection methods have been used, i.e. Student's t-test, Kruskal-Wallis test, and minimum redundancy maximum relevance (mRMR) method to improve the prediction performance of the whole system reducing the number of features. A combined feature selection approach has been proposed. Moreover, due to the imbalance problem of the dataset, a biased support vector machine has been used as the classification model. The performance of different feature selection approaches has been measured according to the area under curve (AUC). Experimental studies show that the features that are selected by the combined approach provide better performance and efficiency than that of by individual approach.