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3D deformable protein shapes classification based on triangles-stars and composite deep neural networks

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1109/AICCSA56895.2022.10017554
AuthorSearch for: ; Search for: 1
Affiliation
  1. National Research Council of Canada. Digital Technologies
FormatText, Article
Conference2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA), December 5-8, 2022, Abu Dhabi, United Arab Emirates
Subject3D protein shape classification; 3D deformable object classification; pattern recognition; graph decomposition; graph metric; metric learning; deep learning; proteins; three-dimensional displays; shape; neural networks; benchmark testing; feature extraction
Abstract
In this paper, the problem of 3D protein deformable shape classification is addressed. Proteins are macromolecules with deformable shapes, their classification based only on their molecular surfaces is challenging problem. In addition, the protein shapes are related to their functions which makes their classification an important task. Triangular meshes (graphs) my be considered to represent the protein molecular surfaces. In this paper, we propose a new deep learning-based approach for 3D protein deformable shape classification. We propose a 3D deformable shape descriptor and a composite deep neural network. The shape descriptor is based on a set of global and local features resulting from the decomposition of protein 3D shapes into triangles-stars, following the different neighborhood order considered. The classification is performed by a composite deep neural network, each branch corresponding to a neighborhood order. The proposed approach is evaluated against 3D protein benchmark repositories and state-of-the-art methods. Our experimental results show and demonstrate the high performances and the effectiveness of our approach.
Publication date
PublisherIEEE
In
  • 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA), 10017554 (20 January 2023).
LanguageEnglish
Peer reviewedYes
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Record identifierf6ad06f8-2371-4989-af70-364a8cf99b0d
Record created2023-05-15
Record modified2023-05-16
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