Examination of Different Representations of Proteins Using Protein Ray-based Descriptor and Deep Learning Models

Abstract

The study of proteins has been of high importance because it is needed to understand the processes in the living organisms in which these molecules are involved. Proteomics is the research area that studies the protein structures. One of the tasks on which proteomics is focused on is solving the protein classification task. Although there are many studies focused on this problem, it is still a popular task because there is still need for faster methods for protein classification. The aim of the study presented in this paper is to develop a fast and accurate protein classification model. For that purpose, for feature extraction we use our protein ray-based descriptor. We use a deep learning architecture for generating prediction model. Besides the standard form of the protein ray-based descriptor, we also consider several other representations of the proteins and make examination which is the most appropriate representation. Some experimental results are given and discussed.