New Graph Embedding Approach for 3D Protein Shape Classification

Abstract

We address the problem of 3D protein deformable shape classification. Proteins are macromolecules characterized by deformable and complex shapes which are related to their function making their classification an important task. Their molecular surface is represented by graphs such as triangular tessellations or meshes. In this paper, we propose a new graph embedding based approach for the classification of these 3D deformable objects. Our technique is based on graphs decomposition into a set of substructures, using triangle-stars, which are subsequently matched with the Hungarian algorithm. The proposed approach is based on an approximation of the Graph Edit Distance which is characterized by its robustness against both noise and distortion. Our algorithm defines a metric space using graph embedding techniques, where each object is represented by a set of selected 3D prototypes. We propose new approaches for prototypes selection and features reduction. The classification is performed with supervised machine learning techniques. The proposed method is evaluated against 3D protein benchmark repositories and state-of-the-art algorithms. Our experimental results consistently demonstrate the effectiveness of our approach. Contributions-We propose a new graph embedding approach to classify 3D deformable protein shapes and new techniques for prototypes selection and dimensionality reduction then we performed the classification using a Naive Bayes (NB) classifier and we achieve better results than the state-of-the-art.