Local phase quantization texture descriptor for protein classification


In this work we propose a method for protein classification based on a texture descriptor, called local phase quantization that utilizes phase information computed from the image extracted from the 3-D tertiary structure of a given protein. To build this texture, the Euclidean distance is calculated between all the atoms that belong to the protein backbone. Moreover, we study classification fusion with a state-of-the-art method for describing the proteins: the Chou's pseudo amino acid descriptor. Our experiments show that the fusion between the two approaches improves the performance of Chou's pseudo amino acid descriptor. We use support vector machines as our base classifier. The effectiveness of our approach is demonstrated using four benchmark datasets (protein fold recognition, DNA-binding proteins recognition, biological processes and molecular functions recognition/enzyme classification).

Keywords protein classification; texture descriptors; primary structure; local phase quantization; support vector machines.

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