Feature Selection for Improved Classification of Protein Structures

Many research groups work on analyzing the structures of protein molecules since that may help to gain knowledge that can be used for designing drugs. To understand the protein structures, it is very important to categorize them in corresponding classes. Therefore, protein classification is one of the main topics in bioinformatics. In this paper, we propose an approach for classifying protein structures. First, the characteristics of the proteins are extracted in corresponding feature vectors. Then, feature selection is made in order to reduce the dimensionality of the dataset, as well as to keep only the most significant features. For feature selection, we use various feature selection techniques. Finally, we build models by using different classification methods. The proposed approach is evaluated in details and also the benefits of applying feature selection are analyzed.